Displays

Ambient noise seismology has greatly reduced the cost of acquiring data for seismic monitoring and imaging by reducing the need for active sources. For applications requiring time-lapse imaging or continuous monitoring, we desire sensor arrays that require little effort, money, and power to maintain over long periods of time. Distributed Acoustic Sensing repurposes a standard fiber optic cable as a series of single-component strain rate sensors with spacing at the scale of meters over distances of kilometers. With a single location providing the power source and recording all data, along with the ability to use existing underground fiber optic networks, a small team is now able to easily establish a monitoring network and acquire massive amounts of strain rate data continuously.

This talk will explore two conceptual changes when using DAS data for ambient noise interferometry: greatly increased data volumes, and the difference between velocity and distributed strain-rate data. These two challenges will be illustrated in the context of experiments with applications in near-surface Vs imaging with applications in earthquake hazard analysis, permafrost thaw monitoring, and urban geohazard and hydrology monitoring.

On the issue of data volumes: Orders of magnitude more sensors and high sample rates (often in the kilohertz range) quickly result in data quantities that exceed the limits of computational infrastructure and algorithms available to many seismologists, potentially at the petabyte/year scale for modern acquisition instruments. New algorithms focused on reduced data movement are improving our ability to analyze more data with existing resources. This talk will include a brief overview of some recent algorithmic improvements for both ambient noise interferometry for imaging, and interferometry-based event detection.

On the issue of changing from velocity to distributed strain rate data: Because strain rate is a tensor quantity and velocities are a vector quantity, the sensitivity of DAS to seismic sources at different orientations is quite different from typical seismometers. This difference can be clear both in polarity and amplitude of the signal, and is particularly significant in shear and Love wave recordings. We will describe simple models to describe expected changes in how seismometers and DAS record the same noises, and the corresponding changes expected in noise correlation functions. These sensitivity differences are more pronounced in ambient noise correlation functions than they are in raw signal recordings, effectively emphasizing a different distribution of ambient noise sources. Modeling these sensitivities helps determine which sensor orientations are reliable for use in ambient noise interferometry imaging.

How to cite: Martin, E., Lindsey, N., Biondi, B., Ajo-Franklin, J., and Zhu, T.: What changes when we use ambient noise recorded by fiber optics? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6022, https://doi.org/10.5194/egusphere-egu2020-6022, 2020.

Measuring seismic and acoustic signals on seafloor telecom cables has proven recently its very high potential for earthquake monitoring but also for beter understanding the interaction between the oceans and the solid earth. A consequence of these interactions is the generation of the primary and secondary microseismic noise on coastal regions and in the deep ocean respectively. These seismic noises that propagate across continents are central to a large fraction of todays' seismic imagery and monitoring campaigns. Compared to previous studies and instrumentation setups, acoustic sensing over oceanic telecom cables offer the unique ability to measure in a very dense manner waves that are generated on the seafloor. We analyse a week long record of ambient noise measurements on the 41.5 km-long seafloor telecom cable offshore Toulon, south of France. At shallow depth, close to the coast, we measure the pressure changes caused by the oceanic gravity waves. The bottom pressure is then compared to an oceanographic buoy located a few kilometers away from the cable. The amplitude and frequency of the pressure are modulated by the gravity waves height and dominant periods. This observation opens the way for a distributed measurement of the oceanic waves characteristics over several kilometers. At depth larger than a 1km, we observe Scholte waves at the ocean-solid earth interface produced by the non-linear interaction of gravity waves. These waves have the double frequency of the gravity waves seen at the coast. We find that the amplitude and frequency change over time, as do the gravity waves observed near the coast. The frequency-wave number decomposition of the signal reveals that the apparent velocity of the Scholte waves does not depend of the azimuth of the fiber. These observations confirm that these deep Scholte waves are secondary microseismic noise, generated locally from the interaction of landward gravity waves with oceanward gravity wave reflected on the coast. Spatially distributed monitoring of the ambient noise wave field at the ocean-solid earth interface provides a better understanding of the noise generation and therefore will allow a better modeling of the ambient noise in the future.

How to cite: Rivet, D., Guérin, G., Mata, D., Lior, I., Sladen, A., and Ampuero, J.-P.: In-situ microseism noise generation measured from distributed acoustic sensing on seafloor optical cable, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11124, https://doi.org/10.5194/egusphere-egu2020-11124, 2020.

The interactions of wind with the ocean surface, ocean wave with acoustic wave, acoustic wave with seismic wave below the sea bottom, and the interplay among them drive important energy flows from the atmosphere to the lithosphere. Uncertainty remains regarding the origin of wind-related noise in the ocean and its coupling to seismic noise below the sea floor. Seismic interferometry is a powerful tool that uses microseisms, or ambient noise within solid earth, to monitor temporal seismic velocity change by referring to the auto/cross-correlation as a Green’s function at the sites, and its temporal change. The most important assumption when detecting seismic velocity changes with seismic interferometry is that mutually uncorrelated noise sources are distributed randomly in space and time without any temporal changes in their density and intensity in a fully diffuse wave field. An effect of temporal variation on the ambit noise field to the retrieval of Green’s function is, however, not fully understood, nor is how reliable temporal changes in interferogram noise are, especially when accompanied by large earthquakes and slow slip events. Here, we show relationships among the temporal changes of sea surface wave, acoustic wave, and seismic wave fields, which are observed in ocean bottom pressure gauges and seismometer arrays installed in New Zealand. The temporal variation in the power spectrum obtained from continuous ocean bottom seismometer and pressure records near 200 mHz correlates with the temporal variation in wind speed above the sites, particularly during wind turbulence of more than 5 m/s. The temporal fluctuation in the ocean bottom pressure caused by the ocean surface wave field correlates to that of a microseism near 200 mHz. The temporal variations in the power spectrum from both continuous ocean bottom pressures and microseisms in the 200–800 mHz range show a positive correlation. After calculating the auto/cross-correlation functions (ACF/CCF) from ambient noise in a 200–800 mHz pass band every 6 h, the temporal variation in the correlation between the ACF/CCFs is investigated every 6 h. The temporal variation in the ACF/CCFs correlates with the time derivative of the temporal changes in the power spectrum amplitude of both the bottom pressure and the microseism rather than the temporal changes in the amplitude of the power spectrum. This suggests that the temporal change that occurs in the seismic interferogram owing to ambient noise, is mostly controlled by the temporal change in the ocean wave field undergoing fluctuations by the atmospheric turbulence over the sea surface. The temporal variations in the noise field in space and time may break the assumption on seismic interferometry, and eventually make the apparent temporal change in interferogram noise.

How to cite: Ito, Y., Uemura, M., Webb, S. C., Mochizuki, K., and Henrys, S.: Ambient noise field and temporal changes on ambient noise auto/cross-correlogram at the sea bottom inferred from ocean-bottom seismic and pressure arrays, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6159, https://doi.org/10.5194/egusphere-egu2020-6159, 2020.

Beamforming and backprojection methods offer a data-driven approach to image noise sources, but provide no opportunity to account for prior information or iterate through an inversion framework. In contrast, recent methods have been developed to locate ambient noise sources based on cross-correlations between stations and the construction of finite-frequency kernels, allowing for inversions over multiple iterations (i.e., Tromp et al., 2010, Ermert et al. 2017, Sager et al. 2018). These kernel-based approaches show great promise, both in mathematical rigour and in results, but may remain difficult to understand or implement for the wider community. Here we show that these two different classes of methods, beamforming and kernel-based inversion, are achieving exactly the same result in certain circumstances. This means existing beamforming and backprojection methods can also incorporate prior information in a mathematically correct manner.

We start with a description of a relatively simple beamforming or backprojection algorithm, based on time-domain shifting and measurement of waveform coherence. Only by changing the order of steps, we begin to resemble the kernel-based approaches. By adding a physical model for the distribution of noise sources, and therefore synthetic correlation functions, we can extend backprojection to an iterative, gradient-based inversion scheme. Adjoint methods and a direct simulation of correlation wavefields can later be used to increase computational efficiency, but we stress that these are not needed to understand the approach.

Given the equivalence of these approaches between these two communities, both sides can benefit from bridging the gap. For example, for kernel-based inversion schemes, a current challenge lies in defining the misfit and time window over which a correlation will be scored; a windowing function based on beamform images offers a more intuitive way to identify significant contributions in the noise wavefield, exploiting more than just the direct surface-wave arrivals.

How to cite: Bowden, D., Sager, K., Fichtner, A., and Chmiel, M.: On the link between Beamforming and Kernel-based Source Inversion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15228, https://doi.org/10.5194/egusphere-egu2020-15228, 2020.

Wind turbines (WTs) have proven to be an increasingly cost-efficient source of sustainable energy. With further cost reductions and growth of environmental awareness, the amount and size of WTs will further expand. In the seismic literature, WTs have mainly been considered a threat rather than an opportunity. WTs act as infrasound and seismic sources, whose wavefield might overwhelm signal from earthquakes. Rather than focusing on the detrimental effects, we embrace the WT revolution and focus on the novel possibilities of the WT seismic source. We show detailed characteristics of this source using recordings over the Groningen seismic network. We further show examples of using the WT seismic noise for extracting medium parameters. Moreover, we exploit the repeatable nature of the source for subsurface monitoring.

How to cite: Ruigrok, E., Jagt, L., and van der Vleut, B.: Exploiting wind-turbine noise for seismic imaging and monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11208, https://doi.org/10.5194/egusphere-egu2020-11208, 2020.

The theory of ambient seismic noise interferometry offers techniques to retrieve estimates of inter-receiver responses from continuously recorded ambient seismic noise. This is usually achieved by correlating and stacking successive noise panels over sufficiently long periods of time. If the noise panels contain significant body-wave energy, the stacked correlations expected to result in retrieved estimates of the body-wave responses, including reflections. Such application combined with a dense surface seismic array is promising for imaging the subsurface structures at lower cost and lower environmental impact as compared to with controlled seismic sources. Subsequently, this technique can be an alternative to active-source surveys in a range of challenging scenarios and locations, and can also be used to perform time-lapse subsurface characterization.

In this study, we apply seismic body-wave noise interferometry to 30-days of continuous records from a surface line of 31 receivers spaced by 25 meters in the South of the Netherlands with the aim to image subsurface reflectors, at depths from a few hundreds of meters to a few kilometers. As a first step, we compute stacked auto-correlations and compare the retrieved zero-offset section with a co-located stacked section from a past active reflection survey on the site.

Yet, the retrieval of reflectivity estimates relies on the identification and collection of a sufficient number of noise panels with recorded body waves that have travelled from the subsurface towards the array. Even in the case of favorable body-wave noise conditions, the panels are most often contaminated with stronger anthropogenic coherent seismic noise, mainly in the form of surface waves, which in turn prevents the stacked correlations to reveal reflectivity. Because of the limited effect of frequency filtering, the application of seismic body-wave noise interferometry requires in fact extensive effort to identify noise panels without prominent coherent noise from the surface activity. Typically, this leads to disregard a significant amount of actually useful data.

For this reason, we designed, trained and tested a deep convolutional neural network to perform this classification task more efficiently and facilitate the repetition of the retrieval method over long periods of time. We tested several supervised learning schemes to classify the panels, where two classes are defined, according to the presence or absence of prominent coherent noise. The retained classification models achieved close to 90% of prediction accuracy on the test set.

We used the trained classification models to correlate and stack panels which were predicted in the class with coherent noise absent. The resulting stacked correlations exhibit potential reflectors in a larger depth range than previously achieved. The results show the benefits of using machine learning to collect efficiently a maximum amount of favorable noise panels and a way forward to the upscaling of seismic body-wave noise interferometry for reflectivity imaging.

How to cite: Boullenger, B., de Bakker, M., Verdel, A., and Carpentier, S.: Retrieval of reflectivity images from ambient seismic noise correlations using machine learning as a noise-panel classification tool, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1680, https://doi.org/10.5194/egusphere-egu2020-1680, 2020.

The overall purpose of the recently finalized GEMex project*, a European-Mexican collaboration, has been to gain an improved understanding of the subsurface at two unconventional geothermal sites: for EGS development at Acoculco and for a superhot resource near Los Humeros. Providing a more precise description of both the geological structure and the geothermal reservoir behavior for these two sites form important requirements for achieving that goal.

For delineating the main structural features at geothermal reservoir level, reflection retrieval from ambient seismic noise can be considered interesting because of its relatively low-cost and low environmental impact as compared to more conventional, controlled-source, seismic surveying practice, where (expensive) active sources are required.

In this study, we present results from the application of ambient noise seismic interferometry (ANSI) to retrieve zero-offset reflected P-waves from continuous seismic data recorded during the second half of 2017 at the Los Humeros geothermal field, Mexico. It is known from noise interferometry theory that reflected P-waves can provide local structural detail at locations directly underneath the employed seismic stations.

We address various data selection and processing aspects related to the retrieval of these reflected P-waves. The reflections are thereafter compared with modelled reflectivities at station locations with sufficient data availability, data quality and proximity to a location at which seismic interval velocity information is available from the literature.

From our study it can be concluded that the ANSI auto-correlation technique that was applied for zero-offset reflectivity retrieval at the Los Humeros site indeed can provide relatively high structural detail: for near-horizontal reflectors in the close vicinity of the selected stations, local depth-estimates of seismic velocity-contrasts were determined. This information can be used to constrain both the geological structure and geothermal reservoir property description.

As such, results from this passive-seismic method may partially complement and partially confirm subsurface information derived from active-seismic, that can only be acquired at a higher cost, which is more labor-intensive and which has more impact on the environment.

We thank the Mexican GEMex team around Angel Figueroa Soto from UMSNH and Marco Calo from UNAM for setting up the seismic network and station maintenance as well as data retrieval. The Comisión Federal de Electricidad (CFE) kindly provided us with access to their geothermal field and permission to install the seismic stations. OGS is thanked for providing us the location details of the four active seismic lines. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550 and the Mexican Energy Sustainability Fund CONACYT-SENER, project 2015-04-68074.

* http://www.gemex-h2020.eu/index.php?option=com_content&view=featured&Itemid=101&lang=en

How to cite: Verdel, A., Boullenger, B., E. Martins, J., Obermann, A., Toledo, T., and Jousset, P.: Structural delineation at the Los Humeros geothermal field, Mexico, by P-wave reflection retrieval from noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7324, https://doi.org/10.5194/egusphere-egu2020-7324, 2020.

With the advent of large and dense seismic arrays, there is an opportunity for novel inversion methods that exploit the information captured by stations in close proximity to each other. Estimating surface waves dispersion is an interest for many geophysical applications using both active and passive seismic data. We present an inversion scheme that exploits the spatial and temporal relationships of the Helmholtz equation to estimate dispersion relations directly from surface wave ambient noise data, while reconstructing the full wavefield in space and frequency. The scheme is a PDE constrained inverse problem in which we jointly estimate the state and parameter spaces of the seismic wavefield. Key to the application on ambient seismic noise recordings is to remove the boundary conditions from the PDE constraint, which renders a conventional waveform inversion formulation singular. With synthetic acoustic and elastic data examples we show that using a variable projection scheme, we can iteratively update an initial estimate of the medium parameters and recover an estimate for the true underlying velocity field. Our examples show that the we can reconstruct the full wavefield even in the case of strong aliasing and irregular sampling. This works forms the basis for a new approach to inverting ambient seismic noise using large and dense seismic arrays.

How to cite: de Ridder, S. A. L., Maddison, J. R., Shaiban, A., and Curtis, A.: Wavefield reconstruction inversion for ambient seismic noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22018, https://doi.org/10.5194/egusphere-egu2020-22018, 2020.

We simultaneously extract both, direct P-waves and Rayleigh waves, from the seismic ambient noise field recorded by a dense seismic network in Iran. With synthetics, we show that the simultaneous retrieval of body and surface waves from seismic ambient noise leads to the unavoidable appearance of spurious arrivals that could lead to misinterpretations.

We work with 2 months of seismic ambient noise records from a dense deployment of 119 sensors with interstation distances of 2 km in Iran. To retrieve body and surface waves, we calculate the cross-coherency in low-frequency ranges, i.e. frequencies up to 1.2 Hz, to provide the empirical Green’s functions between each pair of stations. To separate the P and Rayleigh waves, we use the polarization method that also enhances the small amplitude body waves.

We observe both P and Rayleigh waves with an apparent velocity of 4.9±0.3 and 1.8±0.1 km/s in the studied area, respectively, as well as S or higher mode of Rayleigh waves, with an apparent velocity of 4.1±0.1 km/s. Besides these physical arrivals, we also observe two spurious arrivals with similar amplitudes before/after the P and/or Rayleigh waves that render the discrimination challenging.

To better understanding these arrivals, we perform synthetic tests. We show that simultaneously retrieving the body and surface waves from seismic ambient noise sources will unavoidably lead to the appearance of superior arrivals in the calculation of empirical Green’s functions.

How to cite: Riahi, A., Shomali, Z.-H., Obermann, A., and Kamayestani, A.: Simultaneous body and surface wave retrieval from the seismic ambient field and discrimination from unavoidably arising spurious artifacts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-481, https://doi.org/10.5194/egusphere-egu2020-481, 2020.

Imaging azimuthal anisotropy from seismic noise cross-correlations is challenging, especially in very complex tectonic settings such as the Alps. In this region, the focus has been mainly on retrieving anisotropy using SKS-splitting data, but this data does not provide strong depth constraints. In this work, we map the azimuthal anisotropy of Rayleigh-wave velocity in the Alps using seismic noise cross-correlations. This initial study focusses on waves at ~15 s period. The study area is divided into small zones for which all the stations outside are used as virtual sources and all the stations inside are used as receivers. For each virtual source and each zone, we perform time domain beam forming to retrieve the local phase velocity and propagation direction. As the distances between sources and receivers are relatively small, we use an algorithm that takes into account circular wavefronts. The beam forming shows that the waveforms are very coherent for different stations within each small array, and that deviations from great-circle propagation can be significant. The resulting phase velocities in each zone show a variation with azimuth which is in some locations very small (indicating that anisotropy is insignificant) and which in all other locations has a 2θ dependency on azimuth, indicative of well resolved azimuthal anisotropy. Bootstrapping uncertainty estimates show that the results are very stable if a sufficient number of source stations is used. The combination of permanent stations with the temporary AlpArray stations provides us with a very high station density that allows us to carry out this measurement across a large area. The resulting anisotropy maps show a good resolution, with higher uncertainties in the Po plain and the areas of low station density. The clear 2θ azimuth dependency is a sign that our method overcomes both effects related to source directivity (which has an approximate 1θ dependency) and measurement instability which can be significant for Eikonal tomography in the case of irregular networks.

How to cite: Soergel, D., Pedersen, H., Paul, A., and Stehly, L.: Imaging azimuthal anisotropy in the alpine crust using noise cross-correlations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10085, https://doi.org/10.5194/egusphere-egu2020-10085, 2020.

It is now well established that large seismic events change the surrounding velocities, and that these velocities slowly recover over time.  Precisely which mechanisms control the recovery process are less well understood.  We present the results of laboratory experiments to better characterise what properties of the underlying material control the recovery process.  We do this by mixing two waves, one which perturbs the velocity of the sample (as an earthquake does in field data) and one which senses the change in velocity (as in changing noise correlations).  This is an inherently nonlinear experiment as we mix two waves and measure the effects of this wave mixing.  Within our experiments, we vary the properties of the samples to understand which are most important in controlling the nonlinear response.  We focus on two mechanisms.  The first is fractures and how changes in fracture properties change the nonlinear response.  The second is fluids, in particular the effect of low saturations on the nonlinear response.  By changing the fluids and fractures we can turn on and off the nonlinear mechanism, helping us to move toward a better understanding of the underlying mechanisms of these wave-wave interactions.

How to cite: Malcolm, A., Khajehpour Tadavani, S., and Poduska, K.: The Effects of Cracks and Fluids on Post-Seismic Healing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9991, https://doi.org/10.5194/egusphere-egu2020-9991, 2020.

Seismic and ultrasonic waves are sometimes used to track fluid injections, propagation, infiltrations in complex material, including geological and civil engineered ones. In most cases, one use the acoustic velocity changes as a proxy for water content evolution. Here we propose to test an alternative seismic or acoustic observable: the waveform decorrelation. We use a sample of compacted millimetric sand as a model medium of highly porous multiple scattering materials. We fill iteratively the sample with water, and track changes in ultrasonic waveforms acquired for each water level. We take advantage of the high sensitivity of diffuse coda waves (late arrivals) to track small water elevation in the material. We demonstrate that in the mesoscopic regime where the wavelength, the grain size and the porosity are in the same order of magnitude, Coda Wave Decorrelation (waveform change) is more sensitive to fluid injection than Coda Wave Interferometry (apparent velocity change). This observation is crucial to interpret fluid infiltration in concrete with ultrasonic record changes, as well as fluid injection in volcanoes or snow melt infiltration in rocky glaciers. In these applications, Coda Wave Decorrelation might be an extremely interesting tool for damage assessment and alert systems [1].

[1] R. Thery, A. Guillemot, O. Abraham, E. Larose, Tracking fluids in multiple scattering and highly porous materials: toward applications in non-destructive testing and seismic monitoring, Ultrasonics, 102, 106019 (2019).

How to cite: Larose, E., Thery, R., Abraham, O., and Guillemot, A.: Interpreting Coda Wave Decorrelation from ambient seismic noise interferometry, inputs from laboratory experiments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6620, https://doi.org/10.5194/egusphere-egu2020-6620, 2020.

The ambient seismic noise has proven to be a powerful tool to assess velocity changes within the ground using coda-wave interferometry (CWI). CWI is based on the analysis of small waveform changes in the coda of the signals. Localizing and imaging the source that generates changes can be done with the help of sensitivity kernels which contain information on how each part of the surrounding medium contributes to the overall waveform perturbation that is recorded at a receiver. Although progress has been made in the theory of sensitivity kernels in the case of a full elastic space,  the inclusion of a free surface has proven to be difficult. Indeed, the free surface couples body waves and surface waves, which affects the sensitivity of coda waves with respect to the full-space case. Furthermore, one expects the depth sensitivity of coda waves to be strongly dependent on the relative contribution of surface and body waves, which depends on the lapse-time, source-receiver distance and scattering properties of the medium. Using the Monte-Carlo method, we compute traveltime-sensitivity kernels in a 3D scalar problem that includes body and surface waves, based on a recent theoretical model that integrates both through a mixed boundary condition. From these results, we assess the impact of the depth of a velocity perturbation on the recorded signals at the surface. Our results will be compared with previous numerical approaches from the literature. 

How to cite: Barajas, A., Margerin, L., and Campillo, M.: Sensitivity kernels for coda-wave interferometry in a three-dimensional scalar scattering media, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20890, https://doi.org/10.5194/egusphere-egu2020-20890, 2020.

Between 2013 and 2017, the Groningen gas field was monitored by several deployments of an array of geophones in a deep borehole at reservoir level (3 km). Zhou & Paulssen (2017) showed that the P- and S-velocity structure of the reservoir could be retrieved from noise interferometry by cross-correlation. Here we show that deconvolution interferometry of high-frequency train signals from a nearby railroad not only allows determination of the velocity structure with higher accuracy, but also enables time-lapse measurements. We found that the travel times within the reservoir decrease by a few tens of microseconds for two 5-month periods. The observed travel time decreases are associated to velocity increases caused by compaction of the reservoir. However, the uncertainties are relatively large. 
Striking is the large P-wave travel time anomaly (-0.8 ms) during a distinct period of time (17 Jul - 2 Sep 2015). It is only observed for inter-geophone paths that cross the gas-water contact (GWC) of the reservoir. The anomaly started 4 days after drilling into the reservoir of a new well at 4.5 km distance and ended 4 days after the drilling operations stopped. We did not find an associated S-wave travel time anomaly. This suggests that the anomaly is caused by a temporary elevation of the GWC (water replacing gas) of approximately 20 m. We suggest that the GWC is elevated due to pore-pressure variations during drilling. The 4-day delay corresponds to a pore-pressure diffusivity of ~5m²/s, which is in good agreement with the value found from material parameters and the diffusivity of (induced) seismicity for various regions in the world. 

How to cite: Paulssen, H. and Zhou, W.: Time-lapse changes within the Groningen gas field caused reservoir by compaction and distant borehole drilling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3567, https://doi.org/10.5194/egusphere-egu2020-3567, 2020.

To create virtual shot gather from passive signals it is essential to cross-correlate all the signals with the reference trace. Since surface sources dominate the origin of seismic noise, the correlated sections are highly dominated by surface waves. If the target is surface wave inversion general cross-correlation will suit the target. But if the extraction of body waves from those signals is the main objective, coherent ground roll events mask the body waves making it difficult to extract them. To tackle this issue a frequency-spatial nonCoherent filter (FX-NCF) plus a post-correlation processing module are introduced. FX-NCF is a prediction filter and the filter operator is a function of frequency, station interval and the slope of the interested event. In the frequency domain, the filter is looking for the prediction of n-th trace coherence spectrum from the (n-1)-th signal’s coherence spectrum by minimizing the objective function. Hybrid norms used to minimize the error. The coherence spectrum of each trace is the coherency between the reference signal and the desired trace. Applying the FX-NCF on 2D real recorded passive signals shows its superiority over general cross-correlation, deconvolution interferometry, cross-coherence and multi-taper-method-coherence-estimation methods in highlighting surface and body waves also improving the signal-to-noise (S/N) ratio. To show the necessity of post correlation processing (before applying on real recorded signals) to highlight reflection events, hyperbolic Radon transform (HRT) as a suitable post-correlation module applied on correlated section due to applied FX-NCF on simulated passive signals from a simple 2D synthetic model. The result encouraged us to apply the same hybrid modules (FX-NCF plus HRT) on real recorded passive signals to reconstruct wanted reflection events.

How to cite: Hariri Naghadeh, D. and Bean, C.: Retrieving the Reflection events from passive signals , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-43, https://doi.org/10.5194/egusphere-egu2020-43, 2020.

Seismic interferometry is widely applied in various scales to reconstruct seismic signals for investigating Earth interior. The method of Phase Cross Correlation (PCC) takes less pre-processing and is more stable for retrieving of crustal signals than that of the conventional cross correlations by using amplitude information. In order to obtain the crustal reflectors in Taiwan, we applied auto-correlation with PCC to two independent datasets, (1) temporary seismic array in eastern Taiwan with 110 short period seismometers and (2) broadband seismic arrays (BATS and TAIGER) in Taiwan. As a result, the retrieved crustal reflectors, such as Moho reflectors, are stable with different recording time periods and instruments: temporal and spatial signal consistencies in the same site and neighborhood stations, respectively, and also high waveform similarities between short period and broadband seismometers.

Comparing the results with previous studies of velocity model and receiver function, the reflections at 10-12 seconds (roughly 30-40 km) are often observed in most of the results which are correlated to the Moho depths inferred from the receiver function and tomography studies. It is interesting to note that, besides the Moho reflections, some inter-crustal reflectors beneath the Central Range are revealed. The results show that the autocorrelation method has the potential to investigate some signals that are difficult to observe in the past by using other methods.

Another interesting observation from a dense seismic array in eastern Taiwan shows that the chimei fault serves as a sharp boundary to separate the reflectional signals into the northern and southern parts. In the southern part few reflections can be observed and also lack high frequency energies from autocorrelation comparing with those in the northern part. It implies that the distribution of ambient sources or near surface materials could influence the results. After examining the PCC’s feasibility and stability in this study, it is necessary to verify the reliability of results by understanding the source’s properties and local geological situations before interpretation.

How to cite: Guan, Z.-K. and Kuo-Chen, H.: Investigating the crustal reflections of Taiwan from autocorrelation of seismic noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21542, https://doi.org/10.5194/egusphere-egu2020-21542, 2020.

To notice key obstacles and suggest effective processing methods for virtual reflection images, numerical modeling was performed by the 2-D finite difference method with time and space intervals of 0.2 ms and 1.25 m, respectively. Vertical sources of the Ricker wavelet with a main frequency of 20 Hz were assumed to be detonated independently at five buried locations with intervals of 500 m. Vertical components of the particle velocity were computed at 99 receivers at 10 m depth with intervals of 20 m. Synthetic data show that maximum amplitudes of reflection signals are less than 2% of those of direct Rayleigh waves on an average. This indicates that the non-reflection events should be attenuated as much as possible before correlating traces to compute virtual seismic data. For attenuating both direct and diffracted Rayleigh waves in the synthetic data, a median filter with a time window of a 0.1-s length was effective. Because stationery-phase source locations for virtual reflections concentrate near receiver locations, only common midpoint gathers close to the sources should be used for good virtual stack images.

How to cite: Kim, K., Song, Y.-S., and Byun, J.: Effects of non-reflection events and stationery source locations on virtual seismic reflection images , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4382, https://doi.org/10.5194/egusphere-egu2020-4382, 2020.

In recent years, seismic interferometry (SI) has been widely used in passive seismic data, it allows to retrieve new seismic responses among physical receivers by cross-correlation or multidimensional deconvolution (MDD). Retrieval of reflected body waves from passive seismic data has been proved to be feasible. Marchenko method, as a new technique, retrieves Green’s functions directly inside the medium without any physical receiver there. Marchenko method retrieves precise Green’s functions and the up-going and down-going Green’s functions can be used in target-oriented Marchenko imaging, and internal multiples related artifacts in Marchenko image can be suppressed. 

Conventional Marchenko imaging uses active seismic data, in this abstract, we propose the method of passive seismic Marchenko imaging (PSMI) which retrieves Green’s functions from ambient noise signal. PSMI employs MDD method to obtain the reflection response without free-surface interaction as an input for Marchenko algorithm, such that free-surface multiples in the retrieved shot gathers can be eliminated, besides, internal multiples don’t contribute to final Marchenko image, which means both free-surface multiples and internal multiples have been taken into account. Although the retrieved shot gathers are contaminated by noises, the up-going and down-going Green’s functions can be still retrieved. Results of numerical tests validate PSMI’s feasibility and robustness. PSMI provides a new way to image the subsurface structure, it combines the low-cost property of passive seismic acquisition and target-oriented imaging property of Marchenko imaging, as well as the advantage that there are no artifacts caused by internal multiples and free-surface multiples.

Overall, the significant difference between PSMI and conventional Marchenko imaging is that passive seismic data is used into Marchenko scheme, which extends the Marchenko imaging to passive seismic field. Passive seismic Marchenko imaging avoids the effects of free-surface multiples and internal multiples in the retrieved shot gathers. PSMI combines the low-cost property of passive seismic acquisition and target-oriented imaging property of Marchenko imaging which is promising in future field seismic survey.

This work is supported by the Fundamental Research Funds for the Central Universities (JKY201901-03). 

How to cite: Jin, Z.: Passive Seismic Marchenko Imaging, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3413, https://doi.org/10.5194/egusphere-egu2020-3413, 2020.

How to cite: Ulrich, I.: Active Noise Tomography in Medical Ultrasound, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9140, https://doi.org/10.5194/egusphere-egu2020-9140, 2020.

Since the successful retrieval of surface-wave responses from the ambient seismic field via cross-correlation, noise-based interferometry has been widely used for high-resolution imaging of the Earth’s lithosphere from all around the globe. Further applications on geothermal fields reveal the potential of ambient noise techniques to either characterize the subsurface velocity field or to understand the temporal evolution of the velocity models due to field operations.

Following the completion of the GeMEX^* project, a European-Mexican collaboration to improve our understanding of two geothermal sites in Mexico, we present the results of ambient noise tomography (ANT) techniques over the Los Humeros geothermal field. We used the vertical component of the data recorded by the seismic network active from September 2017 to September 2018. The total network is composed of 45 seismometers from which 25 are Broadband (BB) and the remaining ones short-period stations. From the ambient noise recorded at the deployed seismic network, we extract surface-waves after the computation of the empirical Green’s functions (EGF) by cross-correlation and consecutive stacking. After the cross-correlations, we pick both phase and group velocity arrival times of the ballistic surface-waves for which we derive independent tomographic maps. Finally, using both the retrieved phase and group velocities, we jointly invert the tomographic results from frequency to depth.

We identify positive and negative velocity variations from an average velocity between -15% and 15% for group and between -10% and 10% for phase velocities in the frequency domain. While the velocity variations are consistent for both the phase and group velocities (with expected group velocities lower than the phase velocities), the group velocity anomalies are more pronounced than the phase velocity anomalies. Low-velocity anomalies fall mostly within the inner volcano caldera, the area of highest interest for geothermal energy. This is consistent with the surface temperatures measured at the Los Humeros caldera, indicating the presence of a heat source. Finally, we compare our results with other geophysical studies (e.g geodesy, gravity, earthquake tomography and magnetotelluric) performed during the GeMEX project within the same area.

We thank the European and Mexican GEMex team for setting up the seismic network and station maintenance as well as data retrieval (amongst which Tania Toledo, Emmanuel Gaucher, Angel Figueroa and Marco Calo). We thank the Comisión Federal de Electricidad (CFE) who kindly provided us with access to their geothermal field and permission to install the seismic stations. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550 and the Mexican Energy Sustainability Fund CONACYT-SENER, project 2015-04-68074.

* http://www.gemex-h2020.eu/index.php?option=com_content&view=featured&Itemid=101&lang=en

How to cite: Martins, J., Obermann, A., Verdel, A., and Jousset, P.: 3D-S wave velocity model of the Los Humeros geothermal field, Mexico, by ambient-noise tomography , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21956, https://doi.org/10.5194/egusphere-egu2020-21956, 2020.

One of the aims of project SIGHT (SeIsmic and Geochemical constraints on the Madeira HoTspot system) is to obtain a 3D model of SV-wave velocities of the crust and upper mantle of the Northeast Atlantic area encompassing Madeira and Canary Islands to the Atlas-Gibraltar zone, using seismic noise cross-correlations in the period range 2-100 s. Ambient noise cross-correlation has been successfully applied in a variety of tectonic environments to image the structure of the Earth subsurface. This technique overcomes some limitations ascribed to source–receiver geometry and sparse and irregular earthquake distribution, allowing to image Earth structure with a resolution that mainly depends on the network design. However, the effect of the water layer in the short period Empirical Green Functions, which are obtained by seismic noise cross-correlation, for interstation paths crossing the ocean is still poorly understood.

In several studies, it has been observed that the presence of water and sediments is responsible for later wave-train arrivals. Those later arrivals are frequently disregarded when measuring group velocity, either by considering only longer periods or by specifying a given velocity range.

In this work, we present a systematic study of the influence of the water layer on both vertical and radial synthetic Rayleigh waves, as well as on higher-mode conversion and on the group velocities dispersion measurements.

We show that although the fundamental mode dominates, the presence of the first overtones at short periods (typically below 8 seconds) cannot be neglected. We also show that specifying a given velocity range when retrieving group velocity can result in a mixture of modes. Our tests reveal that, at short periods, the water has a dominant effect on ocean-continent laterally varying media.

This is a contribution to projects SIGHT (Ref. PTDC/CTA-GEF/30264/2017) and STORM (Ref. UTAP-EXPL/EAC/0056/2017). The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – IDL.

How to cite: Silveira, G., Carvalho, J., Pinzon, J., Custódio, S., Corela, C., and Matias, L.: Effect of the water layer on seismic noise cross-correlation across the Northeast Atlantic, from Madeira and Canaries to the Atlas-Gibraltar zone, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18561, https://doi.org/10.5194/egusphere-egu2020-18561, 2020.

The shear-wave velocities structure at depth can be unraveled from ambient noise (AN) as well as from earthquake-generated (EQ) surface waves. While the first approach mostly provides information at crustal scale, earthquake-based surface waves sense deeper structures due to their lower frequency content. However, for periods between 20 and 40 s, where the two methods often overlap, a number of studies have shown that phase velocities from EQ surface waves are systematically higher (~1%) than those retrieved from AN. The reason for such systematic bias is still debated; finite-frequency effects, overtone contamination, and off-path propagation of surface waves due to structural inhomogeneities have all been invoked as possible explanations of the discrepancy in question.

We explore the validity of the latter hypothesis, by correcting Rayleigh-wave phase velocities for the effect of off-path arrivals at two stations. The deviation from the theoretical path is estimated by evaluating the resemblance of the vertical with the π/2-shifted radial component of the recorded seismograms. We developed a two-station algorithm implementing such a correction and tested it on a dataset of seismograms collected from more than 350 stations recording 443 earthquake events from 2005 to 2019. We demonstrate that by compensating for the arrival-angle effects, the discrepancy between the two methods is significantly reduced. This result suggests that the off-path propagation between epicenters and receivers due to lateral inhomogeneity in the Earth's structure explains most of the discrepancy between AN and EQ phase velocities previously reported in the literature. Such improvement in determining Rayleigh phase velocities will lead to more reliable seismic tomographies and enhanced interpretations of seismic anomalies in terms of thermo-chemical characteristics.

How to cite: Diaferia, G., Magrini, F., Boschi, L., and Cammarano, F.: Reconciling phase velocities from ambient noise and earthquake-generated surface waves by accounting for arrival-angle effects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7179, https://doi.org/10.5194/egusphere-egu2020-7179, 2020.

The performances and sensitivity of gravitational wave (GW) detectors are significantly affected by the seismic environment. In particular, the seismic displacements and density fluctuations of the ground due to seismic-wave propagation introduce noise in the detector output signal; this noise is referred to as gravity-gradient noise, or Newtonian Noise (NN). The development of effective strategies for mitigating the effects of NN requires, therefore, a thorough assessment of seismic wavefields and medium properties at and around the GW detector. In this work, we investigate wave propagation and the subsurface velocity structure at the Virgo GW detector (Italy), using data from a temporary, 50-element array of vertical seismometers. In particular, we analyze the recordings from the catastrophic Mw=6.2 earthquake which struck Central Italy on August 24, 2016, and six of the following aftershocks.  The general kinematic properties of the earthquake wavefields are retrieved from the application of a broad-band, frequency-domain beam-forming technique. This method allows measuring the propagation direction and horizontal slowness of the incoming signal; it is applied to short time windows sliding along the array seismograms, using different subarrays whose aperture was selected in order to match different frequency bands. For the Rayleigh-wave arrivals, velocities range between 0.5 km/s and 5 km/s, suggesting the interference of different wave types and/or multiple propagation modes. For those same time intervals, the propagation directions are scattered throughout a wide angular range, indicating marked propagation effects associated with geological and topographical complexities. These results suggest that deterministic methods are not appropriate for estimating Rayleigh waves phase velocities. By assuming that the gradient of the displacement is constant throughout the array, we then attempt the estimation of ground rotations around an axis parallel to the surface (tilt), which is in turn linearly related to the phase velocity of Rayleigh waves. We calculate the ground tilt over subsequent, narrow frequency bands. Individual frequency intervals are investigated using sub-arrays with aperture specifically tailored to the frequency (wavelength) under examination. From the scaled average of the velocity-to-rotation ratios, we obtain estimates of the Rayleigh-wave phase velocities, which finally allow computing a dispersion relationship. Due to their diffusive nature, earthquake coda waves are ideally suited for the application of Aki’s autocorrelation method (SPAC). We use SPAC and a non-linear fitting of correlation functions to derive the dispersion properties of Rayleigh wave for all the 1225 independent inter-station paths. The array-averaged SPAC dispersion is consistent with that inferred from ground rotations, and with previous estimates from seismic noise analysis.  Using both a semi-analytical and perturbational approaches, this averaged dispersion is inverted to obtain a shear wave velocity profile down to ~1000m depth. Finally, we also perform an inversion of the frequency-dependent travel times associated with individual station pairs to obtain 2-D, Rayleigh wave phase velocity maps spanning the 0.5-3Hz frequency interval. 

How to cite: Saccorotti, G., Gaviano, S., Giunchi, C., Fiori, I., Koley, S., and Van den Brand, J.: Wave propagation and subsurface velocity structure at the Virgo gravitational wave detector (Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21852, https://doi.org/10.5194/egusphere-egu2020-21852, 2020.

Although train-induced vibrations are mainly regarded as a source of unwanted noise for classical seismological applications, these vibrations act as powerful sources for seismic imaging using seismic interferometry. Most of the seismic interferometry studies to date have concentrated on using the ambient seismic field generated by natural processes but the appropriate use of train-induced vibrations could result in higher resolution images.

In this study, we present results of seismic interferometry applied on 3 days of railroad traffic data recorded by an array of 3-component seismographs along a railway in Dublin, Ireland. Train-generated waves show a significantly higher frequency range than those recovered from typical ambient noise interferometry. Analysing the recorded signal, we have been able to distinguish between different train types (e.g. cargo vs. passenger trains) and train lengths (3-4, 5-6, 7-9, and/or 10-11 wagons).

For seismic interferometry, a Common Mid-Point – Cross-Correlation (CMP-CC) stack approach has been used to directly image the structures beneath the array. This approach produces a reflection image with interfaces consistent with nearby borehole data at ~450-500 m and ~1350-1400 m depth.

In addition to this reflection image, our results document a strong relation between the ambient source location (trains in this case) and the retrieved seismic reflection image. Since we have train location GPS data, we extracted 2-s time windows for when the train is 1500 m, 1000 m, and 500 m away from the first sensor and we applied the CMP-CC procedure to produce reflection images. As expected, the reflection images are sensitive to the location of the ambient noise source.

Numerical forward modelling of seismic wavefields for various source-receiver configurations also documents a strong correlation between the source location and the retrieved reflection image.

This research emanates from PACIFIC - Passive seismic techniques for environmentally friendly and cost-effective mineral exploration - which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No~776622. We also acknowledge support from the European Research Council under grant No.~817803, FAULTSCAN.

How to cite: Rezaeifar, M., Maggio, G., Xu, Y., Bean, C., Lavoué, F., Boué, P., Pinzon-Rincon, L., and Brenguier, F.: Imaging shallow structures in Dublin city using seismic interferometry of seismic waves generated by train traffic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16678, https://doi.org/10.5194/egusphere-egu2020-16678, 2020.

Trains have recently been recognised as powerful sources for seismic imaging and monitoring based on the correlation of continuous noise records, but the optimal use of these signals still requires a better understanding of their source mechanisms. In this study, we present a simple approach for modelling train-generated seismic signals inspired from early work in the engineering community, which assumes that seismic waves are emitted by  sleepers regularly spaced along the railway and excited by the passage of the train wheels. 
     As already known in the engineering literature, we exemplify the importance of the spatial distribution of each axle load over the rail track on the high-frequency content of the corresponding source time functions, and therefore of the final seismograms resulting from the contributions of all sleepers. In practice, this high-frequency content mainly depends on ground stiffness beneath the railway.
     Furthermore, we identify two end-member mechanisms to explain the two types of observations documented in the seismological literature. The first is the case of a single stationary source (fixed sleeper) excited by successive wheels of a train. This generates a harmonic spectrum characterised by a narrow spacing between frequency peaks related to a fundamental frequency f₁ = V_train / L_w controlled by train speed and wagon length. The second is the case of a single moving load (single wheel) exciting all sleepers along the railway. This also yields a harmonic spectrum, but with a larger spacing between frequency peaks, related to a fundamental frequency f₂ = V_train / Δ_sleeper  controlled by train speed and sleeper spacing. This moving source also generates a clear Doppler effect. 
     In more realistic cases, considering all wheels and all sleepers, our modelling well reproduces the observations, both in the frequency domain (harmonic spectra) and in the time domain (tremor-like emergent shapes). The dominance of the previously-identified end-member mechanisms depends on sleeper regularity: perfectly-regular sleepers generate signals dominated by the signature of a single moving load with fundamental frequency f₂ and a clear Doppler effect, while slightly-irregular sleepers generate signals dominated by the signature of stationary sources with fundamental frequency f₁. We speculate that our modelling parameter of sleeper regularity actually depends on the properties of the railway infrastructure in real cases.
     Finally, we discuss the perspectives of this work in view of using train-generated signals for seismic imaging and monitoring. In this regard, an important conclusion is that the frequency content of the signals is dominated by interferences between harmonic waves. Therefore, the exact value of the fundamental frequency at play matters less than the generation and preservation of the high frequencies, which depend on the distribution of the train load over the rail track and on propagation effects (medium heterogeneities, scattering and attenuation). Therefore, most of train traffic worldwide is expected to generate signals with a significant frequency content in the band [1 - 50] Hz of interest for seismic applications, in particular in the case of trains travelling at variable speeds which are expected to produce truly broadband signals. 

How to cite: Lavoué, F., Coutant, O., Boué, P., Pinzon-Rincon, L., Brenguier, F., Dales, P., Mordret, A., Rezaeifar, M., Bean, C., and Working Group, T. A.: Understanding seismic waves generated by train traffic via modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16828, https://doi.org/10.5194/egusphere-egu2020-16828, 2020.

The general objectives of the “Seismic Ambient Noise Imaging and Monitoring of Shallow Structures” (SANIMS) project, funded by the Spanish Ministry of Science, Research and Innovation (Ref.: RTI2018-095594-B-I00), are focused into the application and development of methods based on ambient noise seismic data recorded by dense networks to image and monitor natural and human-altered environments. To achieve this objective, temporal seismic networks have been installed since late 2019 in two very different settings; the Cerdanya Basin, a sedimentary basin located in the eastern Pyrenees and the city of Barcelona.

Regarding the Cerdanya Basin, a relatively unaltered setting, a network of up to 25 broad-band stations has been installed for a period of one year. Additionally, a high resolution grid of seismic nodes will be deployed for 2 months in the central part of the basin, with interstation distances of 1.5 km. In order to constraint the uppermost crustal structure using ambient noise, vertical component recordings will be processed using the phase cross-correlation and time-frequency domain phase-weighted stacking to extract fundamental mode Rayleigh waves. The surface waves will then be used to measure inter-station group and phase velocity dispersion curves that will be inverted using the Fast Marching Surface Tomography method. Depending on data quality, we will also process the horizontal components to extract Love waves for joint inversions with Rayleigh waves to constrain radial anisotropy and/or the application of new strategies to perform attenuation tomography.

Regarding areas strongly altered by human activity, we have deployed a network of 15 short-period stations within the city of Barcelona, in most of the cases installed in the basement of secondary schools, for a duration of 9-12 months. The objective of this deployment is twofold; acquire new valuable scientific data and introduce the students in an Earth Science research project. Although the Barcelona area has been investigated using MHVSR methods by different authors, the new data acquired by the SANIMS project will expand the available data and will allow to analyze the time variability of the measurements. This new dataset will also be used to analyze the applicability of the methods based on Rayleigh wave ellipticity inversion of ambient noise and earthquake data to provide S-velocity depth profiles. Under the assumption of an isotropic horizontally layered medium, the ellipticity inversion is not affected by the directivity of the diffusive noise wave field and seems therefore to be a good option to determine local S-velocity depth profiles in areas with little lateral inhomogeneities and uneven distribution of noise sources.

We expect that the use of ambient noise methods will allow to map the basement and to obtain new higher resolution ambient noise tomographic images of the upper crust in the Cerdanya Basin and to better constrain the subsoil properties of Barcelona, hence improving the existing seismic hazard maps. Besides, comparing the results in both areas will allow to compare the performance of the different methods based on ambient noise in quiet and noisy areas.

How to cite: Diaz, J., Schimmel, M., Ruiz, M., and Carbonell, R.: Testing the applicability of ambient noise methods in zones with different degree of anthropogenic sources., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3418, https://doi.org/10.5194/egusphere-egu2020-3418, 2020.

Laboratory experiments report that detectable seismic velocity changes should occur in the vicinity of fault zones prior to earthquakes. However, operating permanent active seismic sources to monitor natural faults at seismogenic depth has been nearly impossible to achieve. The FaultScan project (Univ. Grenoble Alpes, Univ. Cal. San Diego, Univ. South. Cal.) aims at leveraging permanent cultural sources of ambient seismic noise to continuously probe fault zones at a few kilometers depth with seismic interferometry. Results of an exploratory seismic experiment in Southern California demonstrate that correlations of train-generated seismic signals allow daily reconstruction of direct P body-waves probing the San Jacinto Fault down to 4 km depth. In order to study long-term earthquake preparation processes we will monitor the San Jacinto Fault using such approach for at least two years by deploying dense seismic arrays in the San Jacinto Fault region. The outcome of this project may facilitate monitoring the entire San Andreas Fault system using the railway and highway network of California. We acknowledge support from the European Research Council under grant No.~817803, FAULTSCAN.

How to cite: Brenguier, F., Mordret, A., Ben-Zion, Y., Vernon, F., Boué, P., Johnson, C., and Share, P.-E.: Passive seismic velocity monitoring of natural faults: The FaultScan project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5480, https://doi.org/10.5194/egusphere-egu2020-5480, 2020.

The observations of seismicity, ground deformation, and volcanic gas geochemistry indicate a magmatic unrest of the Changbaishan volcano, northeast China between July 2002 and July 2005. In this study, we collected the continuous waveform data from more than 10 stations of permanent and portable networks around Changbaishan volcano area from 2000 to 2018, and studied the temporal velocity changes beneath the volcano based on both the cross-correlation of station pairs and auto-correlation of singe station method. We adopted the time-frequency domain phase weighted technique to speed up the convergence process of the noise-based Green's function, and improved the time resolution of monitoring from several tens of days to several days. We measured the temporal seismic velocity of the Changbaishan volcano in various frequency bands. The results shown that there were obvious seasonal changes of the seismic velocity for most frequency bands, and for 0.5-1 Hz frequency band a sudden velocity drop was observed starting on June 10, 2002 and the amplitude of velocity changes was up to 0.5%. After that, the number of volcanic events increased significantly. Our results suggest that there may be a precursory velocity drop phenomenon before the magma unrest, which is of great scientific significance for the studies of magma unrest and possible volcanic eruption in the future.

How to cite: Liu, Z.: Temporal changes of seismic velocity associated with a magmatic unrest of Changbaishan volcano, northeast China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6847, https://doi.org/10.5194/egusphere-egu2020-6847, 2020.

Monitoring temporal changes in seismic wavespeed can inform our understanding of the evolution of crustal rocks’ mechanical state caused by perturbations in stress field, damages, and fluids. Furthermore, imaging these time-lapse changes in space can help unravel the response of rocks with different elastic properties. In this study, we analyze the spatiotemporal variations of seismic wavespeed in Southern California from 2007 to 2017. We compute the Green’s functions by daily cross-correlations using ambient noise at over three hundred broadband seismic stations. Instead of calculating simply the linear regressions of travel-time shifts over lag-times, which only resolves homogeneous changes, we scrutinize the variations of travel-time shifts at different lag-times and frequencies using coda-wave sensitivity kernels, in order to probe the spatial distribution of wavespeed changes. The long-term and large-scale analysis allows us to investigate the mechanical response of different crustal materials to various transient processes. As an example we use the 2010 Mw 7.2 El Mayor-Cucapah Earthquake (EMC) and show that large coseismic wavespeed reductions occur in Salton Sea area and the Los Angeles sedimentary basin. In the latter region, the ground motion amplification and high susceptibility of sedimentary materials explain the remote signature of the earthquake. In the Salton Sea region, particularly in the geothermal area with highly pressurized fluids, the non-linear crustal response illustrated by wavespeed changes can be analyzed with regard to the high-level micro-seismicity triggered by EMC.

How to cite: Mao, S., Lecointre, A., Wang, Q., van der Hilst, R., and Campillo, M.: Noise-Based Monitoring of Spatiotemporal Changes in Crustal Seismic Wavespeed across Southern California, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12871, https://doi.org/10.5194/egusphere-egu2020-12871, 2020.

In the last decade, correlation of ambient seismic noise has opened a window to new possibilities for the study of structural properties of the Earth. One such possibility is the monitoring of transient changes in the mechanical properties of the surrounding crustal material following an earthquake. These changes, expressed as variations in seismic velocities, are usually associated to fracture damage and release of fluids due to the earthquakes shaking, but could also be related to deformation associated with afterslip. On April 16, 2016, a Mw 7.8 earthquake struck the coast of Ecuador, rupturing a ~100 km-long segment of the megathrust interface previously identified as highly coupled. Shortly after the mainshock, we deployed a temporary seismic network to monitor the post-seismic phase, in addition to the already in-place permanent Ecuadorian network. Here we present results from cross-correlation of continuous ambient seismic noise during a ~12-months period following the mainshock. Taking advantage of the dense and extensive station network, we investigate the spatio-temporal evolution of the post-seimic seismic velocity changes. Our results show a slow but sustained increase in the average seismic velocities after the earthquake, with a decay in the rate of the increase during the last few months. Spatially, the increase is more notorious nearby the rupture area, whereas the amplitude of the increase diminishes as we move away from the epicenter. We interpret these variations in seismic velocities (steady increase) as the crust’s response to the healing process that takes place during the post-seismic phase, following the sudden coseismic decrease of seismic velocities during the mainshock. This healing process could involve the decrease of fluid-related pore pressures and the healing of fractures and cracks generated during the mainshock, both at the interface and on the overriding plate.

How to cite: Agurto-Detzel, H., Rivet, D., and Charvis, P.: Seismic velocity changes in the epicentral area of the Mw 7.8 Pedernales (Ecuador) earthquake from cross-correlation of ambient seismic noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17543, https://doi.org/10.5194/egusphere-egu2020-17543, 2020.

In this work, we use seismic noise autocorrelations to monitor the temporal evolution of the upper crust in Central Italia in order to look for changes that could have occured before the 2009 Mw6.3 l'Aquila and the 2016 Mw 6.2 Amatrice earthquake.

To that end, we use the Coherence of Correlated Waveforms [CCW] method, that consists in measuring changes in the waveform of autocorrelations with a temporal resolution of 5 days.

Our measurements of the CCW show that the L'Aquila Earthquake  is preceded by a 150-days oscillation whose amplitude and frequency progressively increases until the rupture. Analysing 17 years of data, we found that this signal occured only before the L'Aquila and the Amatrice earhtquake.  This suggests the existence of a unique nucleation process.

Finally, we compare the results obtained using the CCW method with the temporal evolution of the seismic waves velocity (dv/v) obtained by analysing the coda of seismic noise autocorrelations.

How to cite: Stehly, L., Delouche, E., Voisin, C., and Poli, P.: Looking for changes in the upper crust associated with large magnitude earthquakes in central Italia using seismic noise autocorrelations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9578, https://doi.org/10.5194/egusphere-egu2020-9578, 2020.

We report the temporal change of the near-surface(<400m) seismic structure of Taiwan revealed by coda interferometry. Following our earlier work (Chen et al., 2017), the Empirical Green’s Functions (EGF) of shear waves extracted from the earthquake coda recorded by the vertical pairs of borehole array, deployed by the Central Weather Bureau, are used to examine the temporal variations of vs and Vs azimuthal anisotropy at the borehole sites. In total, about 700 local events, from 2013 to 2018, are used in this study. The band-passed (3 – 8 hz) EGF extracted from each single event are stacked over variable time period to ensure the reliability of measurements and the desired temporal resolution. The averaged Vs and patterns of Vs azimuthal anisotropy are in good agreement with the site geology, the ambient stress and those reported in our early work. Apparent drop in the Vs isotropic velocities and perturbations in Vs azimuthal anisotropy are observed in few representative borehole sites, and we also noticed that such variations are tightly correlated with the occurrence of major earthquakes in Taiwan. We present the preliminary results and discuss the triggering mechanisms, the healing revolution, and their relationship with the site geology.

How to cite: Cai, S.-J., Chen, L.-W., Lee, H.-Y., Chen, Y.-N., and Gung, Y.-C.: Temporal Variations of Near-surface seismic structure of Taiwan revealed by coda interferometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15135, https://doi.org/10.5194/egusphere-egu2020-15135, 2020.

To understand fault systems, it is required to identify the structure of the crust and upper mantle. Seismic investigations have long been relying on active sources generating an incident wave-field from the Earth surface. The reflected wave-field is then recorded by sensors deployed at the surface. Nowadays, passive imaging has been adopted as an alternative of this source-receiver configuration by computing the correlations of ambient noise. This process allows to estimate the Green’s function between two receivers. We here present a passive imaging technique applied to data recorded with the Dense Array of North Anatolia [1], which was deployed in western Turkey during 16 months. The array consists of 73 stations covering the two major fault branches of the North Anatolian Fault (NAF). Inspired by previous works in optics and acoustics, we introduce a matrix approach of seismic imaging based on seismic noise cross correlations. Our method applies focusing operations at emission and reception (Blondel et al.,2019) allowing to project the reflection matrix recorded at the surface to depth (redatuming). Although seismic noise is dominated by surface waves, focusing operations allow to extract the body wave components that carry information about the reflectivity of in-depth structures. However, complex velocity distribution of the Earth’s crust results in phase distortions, referred to as aberrations in the imaging process. Phase distortions prevent the imaging of the true reflectivity of the subsurface leading to unphysical features and blurry images. To overcome these issues, we introduce a new operator: the so-called distortion matrix. It connects any virtual source induced by focusing at emission with the distorted part of the reflected wave-front in the spatial Fourier domain. A time-reversal analysis of the distortion matrix allows to correct for high-order aberrations. Crustal-scale 3D images of the fault structure of the North Anatolian Fault are revealed with optimal resolution and contrast.

(1) DANA. Dense array for north anatolia. International Federation of Digital Seismograph Networks doi:10.7914/SN/YH2012, 2012.

How to cite: Touma, R., Campillo, M., Aubry, A., and Blondel, T.: Passive Reflection Seismic Imaging of the North Anatolian Fault at crustal-scale: A Matrix Framework for Aberrations Correction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18923, https://doi.org/10.5194/egusphere-egu2020-18923, 2020.

Monitoring of temporal seismic velocity changes can provide us with information on the mechanical state of the Earth’s crust due to processes of stress build-up and release. 

In current work, we use the Dense Array of North Anatolia [1], which has been continuously recording from May 2012 until October 2013, to analyse the spatio-temporal variations of seismic velocity changes in the North Anatolian Fault zone (NAF). We compute daily ambient-noise cross-correlation functions for all 63 three-component stations in the frequency band between 0.1 – 1 Hz.

To retrieve spatial distribution of seismic velocity changes in such an inhomogeneous fault zone, we go beyond the simple linear travel-time shifts approximation and homogeneous sensitivity kernel. We therefore invert for the travel-time shifts at different lag-times. Furthermore, we use sensitivity kernels for media with inhomogeneous scattering properties. The scattering properties for the sensitivity kernels are derived from the data: a scattering mean free path inside the fault zone (northern strand of NAF) of ∼ 10 km and ∼ 150 km outside the fault zone, the attenuation coefficient inside and outside the fault zone are 80 and 100 respectively. 

[1] DANA. Dense array for north anatolia. International Federation of Digital Seismograph Networks doi:10.7914/SN/YH2012, 2012.

How to cite: van Dinther, C., Campillo, M., Margerin, L., and Lecointre, A.: Monitoring of temporal seismic velocity changes in the North Anatolian Fault zone using data derived scattering properties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15195, https://doi.org/10.5194/egusphere-egu2020-15195, 2020.

Scattered seismic coda waves are frequently used to characterize small scale medium heterogeneities, intrinsic attenuation or temporal changes of wave velocity. Spatial variability of these properties raises questions about the spatial sensitivity of seismic coda waves. Especially the continuous monitoring of medium perturbations using ambient seismic noise led to a demand for approaches to image perturbations observed with coda waves. An efficient approach to localize the property variations in the medium is to invert the observations from different source-receiver combinations and different lapse times in the coda for the location of the perturbations. The key of such an inversion is calculating the coda-wave sensitivity kernels which describe the connection between observations and the perturbation. Most discussions of sensitivity kernels use the acoustic approximation and assume wave propagation in the diffusion regime.

We model 2-D  elastic multiple nonisotropic scattering in a random medium with spatially variable heterogeneity and attenuation. The Monte Carlo method is used to numerically solve the radiative transfer equation that describes the wave scattering process here. Recording of the specific intensity of the wavefield I(r,n,t) which contains the complete information about the energy at position r at time t with the propagation direction n allows us to calculate sensitivity kernels according to rigorous theoretical derivations. We investigate sensitivity kernels that describe the relationships between changes of the model parameters P- and S-wave velocity, P- and S-wave attenuation, and the strength of fluctuation on the one hand and the observables envelope amplitude, travel time changes and decorrelation on the other hand. These sensitivity kernels reflect the effect of the spatial variations of medium properties on wavefield. Our work offers a direct approach to compute these new expressions and adapt them to spatially variable heterogeneities. The sensitivity kernels we derived are the first step in the development of an inversion approach based on coda waves.

How to cite: Zhang, T. and Sens-Schönfelder, C.: Simulation of seismic wave scattering for the computation of probabilistic coda-wave sensitivity kernels, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5408, https://doi.org/10.5194/egusphere-egu2020-5408, 2020.

A wide spectrum of processing schemes is commonly applied during the calculation of seismic noise correlations. This is intended to suppress large-amplitude transient and monochromatic signals, to accelerate convergence of the correlation process, or to modify raw correlations into more plausible approximations of inter-station Green's functions. Many processing schemes, such as one-bit normalisation or various non-linear normalizations, clearly break the linear physics of seismic wave propagation. This naturally raises the question: To what extent are the resulting noise correlations physically meaningful quantities?

In this contribution, we rigorously demonstrate that most commonly applied processing methods introduce an unphysical component into noise correlations. This affects noise correlation amplitudes but also, to a lesser extent, time-dependent phase information. The profound consequences are that most processed correlations cannot be entirely explained by any combination of Earth structure and noise sources, and that inversion results may thus be polluted.

The positive component of our analysis is a new class of processing schemes that are optimal in the sense of (1) completely avoiding the unphysical component, while (2) closely approximating the desirable effects of conventional processing schemes. The optimal schemes can be derived purely on the basis of observed noise, without any knowledge of or assumptions on the nature of noise sources.

In addition to the theoretical analysis, we present illustrative real-data examples from the Irish National Seismic Network and the Lost Hills array in Central California. This includes a quantification of potential artifacts that arise when mapping unphysical traveltime and amplitude variations into images of seismic velocities or attenuation.

How to cite: Fichtner, A., Bowden, D., and Ermert, L.: Optimal processing and unphysical effects in seismic noise correlations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7832, https://doi.org/10.5194/egusphere-egu2020-7832, 2020.

We have recently shown that all features in the earthquake-coda correlogram can be explained by the similarity of seismic phases that have a common slowness for the analysed receiver pair. This includes both the features that have their equivalents in the conventional traveltime stacks, but also those that were previously unexplained. Consequently, the information contained in the correlograms – cross-correlated ground-motion time-series in a two-dimensional representation – can be used to constrain Earth’s internal structure, however, that requires a proof of concept and further investigation into the origin of the correlation wavefield. We thus first decompose relevant correlogram features into discrete constituents with respect to their arrival times and we uniquely identify contributing seismic phases to each constituent. This confirms that the correlation wavefield does not arise due to the reconstruction of body waves between the two receivers (a.k.a. Green’s function) – instead, it is dominated by the interaction of various body waves, and its features are characterised by complex sensitivity kernels.

We demonstrate that the event locations relative to the receivers alter the similarities between the body waves, and may result in significant waveform distortions and inaccuracies in arrival-time predictions. We further show that the nature of source-mechanism and energy-release dynamics are the key influencers responsible for individual correlograms equal in quality to a stack of hundreds of correlograms. In other words, a single seismic event that meets a set of criteria in the presence of multiple receivers can completely `illuminate’ the Earth’s interior. Quantitative kernel-decomposition and identification of body-wave pairs that contribute to a given feature in the correlogram, along with informed choices of seismic events, thus makes the correlation-wavefield tomography and other applications fully feasible. This has the potential to change the course of global seismology in the coming decades.

How to cite: Tkalčić, H., Wang, S., and Pham, T. S.: The Earth’s Correlation Wavefield: Proof of Concept, Origin and Applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20464, https://doi.org/10.5194/egusphere-egu2020-20464, 2020.

Common assumptions in ambient noise seismology such as Green’s function retrieval and equipartitioned wavefields are often not met in the Earth. Full waveform ambient noise tomography methods are free of such assumptions, as they implement knowledge of the time- and space-dependent ambient noise source distribution, whilst also taking finite-frequency effects into account. Such methods would greatly simplify near real-time monitoring of the sub-surface. Additionally, the distribution of the secondary microseisms could act as a new observable of the ocean state since its mechanism is well understood (e.g. Ardhuin et al., 2011).

To efficiently forward-model global noise cross-correlations we implement (1) pre-computed high-frequency wavefields obtained using, for example, AxiSEM (Nissen-Meyer et al., 2014), and (2) spatially variable grids, both of which greatly reduce the computational cost. Global cross-correlations for any source distribution can be computed within a few seconds in the microseismic frequency range (up to 0.2 Hz). Similarly, we can compute the finite-frequency sensitivity kernels which are then used to perform a gradient-based iterative inversion of the power-spectral density of the noise source distribution. We take a windowed logarithmic energy ratio of the causal and acausal branches of the cross-correlations as measurement, which is largely insensitive to unknown 3D Earth structures.

Due to its parallelisation on a cluster, our inversion tool is able to rapidly invert for the global microseismic noise source distribution with minimal required user interaction. Synthetic and real data inversions show promising results for noise sources in the North Atlantic with the structure and spatial distribution resolved at scales of a few hundred kilometres. Finally, daily noise sources maps could be computed by combining our inversion tool with a daily data download and processing toolkit.

How to cite: Igel, J., Ermert, L., and Fichtner, A.: Rapid Global Finite-Frequency Ambient Noise Source Inversion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7662, https://doi.org/10.5194/egusphere-egu2020-7662, 2020.

Source location and evolution of the 26 s microseism from 3-C beamforming

Authors: Charlotte Bruland^1, Sarah Mader^2, Céline Hadziioannou¹
1 Institut für Geophysik, Universität Hamburg, Germany
2 Karlsruher Institut für Technologie, Karlsruhe, Germany

The interest in ambient noise has increased in the recent years due to its applications in imaging and monitoring the subsurface without the use of an active source. One of the major unknowns in this field is the origin of the noise used for these analyses. Better constraints on the location and behavior of noise sources will help us understand the ocean-solid Earth interaction processes driving them and improve our applications of ambient noise. One of the most enigmatic noise sources is the 26 s microseism. This very monochromatic source has been identified in the 1960’s and seems to come from a fixed location in the Gulf of Guinea. The source mechanism of this signal is unknown.

To investigate the origin and physical mechanisms responsible for the 26 s microseism, data from permanent broadband stations in Germany, France and Algeria, and temporary arrays in Morocco and Botswana is used for spectral analysis and 3-component beamforming. The source exhibits a strong temporal variation in spectral amplitude. The signal is not always detectable, but occasionally it becomes so strong it can be detected on stations all around the world. Such burst events can last for a couple of hours up to a couple of days. From January to April 2013, the peak was detected globally 28 percent of the time. The beamforming results confirm that the energy is coming from the Gulf of Guinea, as shown in previous studies, and the direction is temporally stable. Whenever the signal is detectable, both Love and Rayleigh waves are generated. Looking into the 26 s microseism over different time periods and using different arrays, the source is expected to be temporally stable in frequency and location, but varying in energy.

How to cite: Bruland, C., Mader, S., and Hadziioannou, C.: Source location and evolution of the 26 s microseism from 3-C beamforming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11314, https://doi.org/10.5194/egusphere-egu2020-11314, 2020.

Generated in the ocean, secondary microseisms result from the interaction of opposing ocean wave fronts and represent the strongest ambient seismic noise level measured on land. The recorded noise energy will vary with seasons due to changes in storm activity and associated secondary microseism source locations. Here, ocean bottom seismometer (OBS) data collected offshore Ireland in 2016 have been processed to look into the seasonal variations of the ambient noise wavefield recorded at the seafloor. Daily cross-correlations of OBS pairs located on top of thick sediments in deep water highlight seasonal changes between Rayleigh waves fundamental mode and first overtone for winter and summer months. Comparisons with ocean wave directional spectrum data derived from ocean wave model hindcasts suggest those variations are correlated with changing patterns in ocean waves interactions and therefore microseism source locations. In order to understand those observations in detail, we use 3D numerical simulations to show how the water column but also the subsurface structure below the sea bottom will affect the recorded wavefield at the seafloor for different stations and sources locations. Compared to land stations, the secondary microseism wavefield observed in the ocean and in particular changes in the excitation of Rayleigh modes due to site effects can help characterize the microseism source locations that fluctuate through the seasons.

How to cite: Le Pape, F. and Bean, C. J.: Seasonal fluctuations in the secondary microseism wavefield recorded offshore Ireland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19154, https://doi.org/10.5194/egusphere-egu2020-19154, 2020.

Using the three-component continuous waveform recordings of 880 broadband seismic stations in China Seismic Network from January 2014 to December 2015, we calculated power spectral densities and probability density functions over the entire period for each station，and  investigated the characteristics of seismic noise in Chinese mainland. The deep analysis on the vertical recordings  indicates that the spatial distribution of noise levels is characterized by obvious zoning for different period bands.  Densely populated areas have higher short-period noise level than sparsely populated ones, suggesting that short-period noise is related to the intensity distribution of human activities such as transportation and industry. Meanwhile，the short-period noise level near the basin is higher than the mountainous areas，which is probably caused by the amplification effect of the sedimentary layer. The microseism energy  gradually decreases from the southeastern coastal lines to the inland regions. Furthermore, horizontal-component noise level  showed a striking constrast with the vertical component at microseismic and long-period bands. In consideration of  the zoning chracteristics and the need of seismic observations, high and low noise models were  acquired for each network , which were proved to be a more effective tool to identify locally abnormal signals including earthquake, instrumental error and various distrubance compared with the global new high and low model. 

How to cite: Wang, F., Wang, W., Long, J., and Mu, L.: Seismic noise characteristics in Chinese mainland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19017, https://doi.org/10.5194/egusphere-egu2020-19017, 2020.

The most pervasive seismic signal recorded on our planet – microseismic ambient noise -results from the coupling of energy between atmosphere, oceans and solid Earth. Because it carries information on ocean waves (source), the microseismic wavefield can be advantageously used to image ocean storms. Such imaging is of interest both to climate studies – by extending the record of oceanic activity back into the early instrumental seismic record – and to real-time monitoring – where real-time seismic data can potentially be used to complement the spatially dense but temporally sparse satellite meteorological data.

In our work, we develop empirical transfer functions between seismic observations and ocean activity observations. We start by following the classical approach of Bromirski et al (1999), who computed an empirical transfer function between ground-motion recorded at a coastal seismic station and significant wave height measured at a nearby ocean buoy. We explore further developments by considering other seismic data observations – such as the polarization of seismic ambient noise – and other indicators of ocean activity observations, including the spectra of ocean waves.

In addition to employing the classical approach of empirical transfer functions, we further present preliminary tests using machine learning techniques to: 1) infer which seismic and ocean activity observables are better predictors of each other, and 2) to predict ocean activity given observed ground motion.

The analysis is made using selected datasets around the North Atlantic, namely using seismic data from North America (west Atlantic), the Azores (central Atlantic) and Portugal (east Atlantic).

This work is supported by FCT through projects UIDB/50019/2020 – IDL and UTAP-EXPL/EAC/0056/2017 - STORM.

References:

How to cite: Custódio, S., Bolrão, F., Bui, T., Hadziioannou, C., Lima, M., Rodrigues, D., Sheriffdeen, S., Silveira, G., and Carvalho, J.: Predicting ocean activity from seismic data using machine learning techniques , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22619, https://doi.org/10.5194/egusphere-egu2020-22619, 2020.

Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity (Vs) structure. Knowing the shallow Vs structure is important for geophysical data interpretation either in order to better constrain data inversions for P-wave velocity (Vp) structures such as travel time tomography or full waveform inversions, or to directly study the Vs structure for geo-engineering purposes (e.g. ground motion prediction). The purpose of this study is to appraise in particular how much information HVSR can add in a large N experiment and how different instrumentation types affect this. 

During the Maupasacq large-scale experiment, 197 three-component short-period stations, 190 geophone nodes and 54 broadband seismometers were continuously operated in Southern France for 6 months (April to October 2017) covering an area of approximately 1500 km2 with a site spacing of approximately 1 to 3 km. On the obtained HVSR and DC data, a statistical Joint inversion is performed for the shallow Vs structure. The results indicate that the addition of HVSR data to the DC inversion reduces the variance of the recovered shallow Vs model and improves the convergence to a smaller data misfit. While broadband and short period instruments delivered similar results, geophone nodes performed significantly worse due to their much higher cut off frequency. 

How to cite: Neukirch, M., García-Jerez, A., Villaseñor, A., Stehly, L., Boué, P., Chevrot, S., Sylvander, M., Díaz, J., Ruiz, M., Luzón, F., Collin, M., Calassou, S., Polychronopoulou, K., Martakis, N., and Bitri, A.: Statistics on the Performance of Instrument Types and the Significance of HVSR data for Shallow Vs HVSR/DC Joint Inversions - A Result from the Large-N Maupasacq Experiment (Southern France), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13681, https://doi.org/10.5194/egusphere-egu2020-13681, 2020.

Ocean bottom seismometers (OBS) are widely in use since recent past to monitor seismicity of slow earthquakes as well as that of ordinary earthquakes. Seismic velocity structures, especially of S-wave are essential to estimate hypocenters of them with accuracy. Here we focus on spatial and temporal stability of ambient noise horizontal to vertical spectral ratio (H/V) spectra calculated from ocean bottom seismometers, as the first step toward future application of ambient noise H/V to estimate S-wave velocity structure. We aim to use the Nakamura’s method (1989) for ambient noise H/V spectra using a 3-component OBS array in the Japan Trench, to image deep structure above the plate interface near the trench. To achieve the imaging, it is necessary to examine spatial and temporal stability of the derived H/V spectra from these seismometers. First, we split each 24-hours record into 1-hour windows after removing the instrumental response, Then, Fourier amplitude spectra of each component is taken and smoothed using Konno and Ohmachi (1998) method, with applying downsampling, mean and trend removal, and tapering to each window. Finally, a 1-hour H/V spectral ratio is calculated with taking quadratic mean of two horizontal components. However, a total of 21 OBS, 3 broadband and 18 short-period, stations have been used in this study. A daily variation and stability of the H/V spectra are examined along with comparing them spatially from one station to another. Stability of the H/V spectra from OBS is promising for carrying out our future endevour of deeper observation using the ambient noise H/V method.

How to cite: Farazi, A. H., Garcia, E. S. M., and Ito, Y.: Stability of ambient noise H/V spectra obtained from OBS near the Japan Trench, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3999, https://doi.org/10.5194/egusphere-egu2020-3999, 2020.