SM3.1

Since early February 2019, the SEIS seismometer deployed at the surface of Mars in the framework of the NASA-InSight mission has been continuously recording the ground motion at Elysium Planitia. In this work, we take advantage of this exceptional dataset to put constraints on the crustal properties of Mars using seismic interferometry (SI). This method use the seismic waves, either from background vibrations of the planet or from quakes, that are scattered in the medium in order to recover the ground response between two seismic sensors. Applying the principles of SI to the single-station configuration of SEIS, we compute, for each Sol (martian day) and each local hour, all the components of the time-domain autocorrelation tensor of random ambient vibrations in various frequency bands. A similar computation is performed on the diffuse waveforms generated by more than a hundred Marsquakes. For imaging application a careful signal-to-noise ratio analysis and an inter-comparison between the two datasets are applied. These analyses suggest that the reconstructed ground responses are most reliable in a relatively narrow frequency band around 2.4Hz, where an amplification of both ambient vibrations and seismic events is observed. The average Auto-Correlation Functions (ACFs) from both ambient vibrations and seismic events contain well identifiable seismic arrivals, that are very consistent between the two datasets. We interpret the vertical and horizontal ACFs as the ground reflection response below InSight for the compressional waves and the shear waves respectively. We propose a simple stratified velocity model of the crust, which is most compatible with the arrival times of the detected phases, as well as with previous seismological studies of the SEIS record. The hourly computation of the ACFs over one martian year also allows us to study the diurnal and seasonal variations of the reconstructed ground response with a technique call Passive Image Interferometry (PII). In this study we present measurements of the relative stretching coefficient between consecutive ACF waveforms and discuss the potential origins of the observed temporal variations.

How to cite: Compaire, N., Margerin, L., Garcia, R. F., Calvet, M., Pinot, B., Orhand-Mainsant, G., Kim, D., Lekic, V., Tauzin, B., Schimmel, M., Stutzmann, E., Knapmeyer-Endrun, B., Lognonné, P., Pike, W. T., Schmerr, N., Gizon, L., and Banerdt, B.: Autocorrelation of the ground vibration recorded by the SEIS-InSight seismometer on Mars for imaging and monitoring applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12292, https://doi.org/10.5194/egusphere-egu21-12292, 2021.

Gravity fluctuations produced by ambient seismic fields are predicted to limit the sensitivity of the next-generation, gravitational-wave detector Einstein Telescope at frequencies below 20 Hz. The detector will be hosted in an underground infrastructure to reduce seismic disturbances and associated gravity fluctuations. Additional mitigation might be required by monitoring the seismic field and using the data to estimate the associated gravity fluctuations and to subtract the estimate from the detector data, a technique called coherent noise cancellation. In this paper, we present a calculation of correlations between surface displacement of a seismic field and the associated gravitational fluctuations using the spectral-element SPECFEM3D Cartesian software. The model takes into account the local topography at a candidate site of the Einstein Telescope at Sardinia. This paper is a first demonstration of SPECFEM3D’s capabilities to provide estimates of gravitoelastic correlations, which are required for an optimized deployment of seismometers for gravity-noise cancellation.

How to cite: Andric, T. and Harms, J.: Simulations of gravitoelastic correlations for the Sardinian candidate site of the Einstein Telescope, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-516, https://doi.org/10.5194/egusphere-egu21-516, 2021.

In seismology, new sensing technologies are currently emerging that can measure ground motion beyond the conventional seismic translation measurements. In particular, rotational motion sensors record an additional 3 components of ground motion and thus provide access to additional information about the seismic wavefield. 

So far, most studies of rotational ground motion are mainly based on recordings of earthquakes or active sources. In this study, we push the limit towards the very weak motions associated with ocean-generated ambient seismic noise. Our aim is to show the potential of using these measurements in the context of ambient noise interferometry. 

We use recordings from two ring lasers in Germany: the `G-Ring' at the Wettzell Geodetic Observatory, and `ROMY' at the Fürstenfeldbruck Observatory near Munich, at a distance of approximately 160 km. These are the most sensitive instruments to date which offer a local, direct measurement of rotational ground motion. 

We demonstrate that the sensitivity of the Wettzell instrument has been sufficiently improved to detect Love waves in the primary microseismic frequency band. Both the G-Ring and ROMY ring lasers are also capable of detecting Love waves in the stronger secondary microseismic band. This latter frequency range is used to test the possibility of performing noise interferometry with rotational records. 

The first results of rotational noise interferometry between the two ring lasers are promising. The correlation waveform is verified by comparison with interferometry carried out with co-located seismometer data at both locations, as well as with numerical simulations. 

In conclusion, we show that ambient noise interferometry is in principle feasible using real rotational recordings of ocean-generated noise. This proof of concept study forms a first step towards noise interferometery of 6-component displacement data. 

How to cite: Hadziioannou, C., Neumann, P., Wassermann, J., Igel, H., Schreiber, U., and Team, T. R.: Ambient seismic noise interferometry using rotational ground motion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-995, https://doi.org/10.5194/egusphere-egu21-995, 2021.

We assess the potential of rotational ground motions to resolve time-dependent near surface structural heterogeneities using noise correlations. Recent studies reveal an increased sensitivity of gradient related observations to near surface structural heterogeneities (e.g., material contrast, cavities) compared to directly measured wavefields (and their time derivatives). The development of new sensing technologies, such as rotational ground motion sensors and distributing acoustic sensing (DAS), enable measurements of strain and rotations and motivate this study. Combining gradient related observations with ambient noise-based monitoring methods has the potential to increase both spatial and temporal resolution. In order to investigate the suggested benefits, we perform a numerical study in 2D, where we simulate seismic noise with random sources at random locations. We apply interferometric principles and calculate cross-correlations of the resulting noise traces recorded at different receiver locations for multiple realizations of the noise field. After analysing the convergence of the correlation functions in terms of simulation length and number of simulations, we compare noise correlations of acceleration and rotation rate for a homogenous reference and a perturbed model. Ultimately, we establish that noise correlations of wavefield gradients are more sensitive than noise correlations of wavefields to small-scale heterogeneity.

How to cite: Celik, B., Sager, K., and Igel, H.: Noise Correlations of Wavefield Gradients to Improve Sensitivity to (near surface, time-dependent) Structural Heterogeneity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3155, https://doi.org/10.5194/egusphere-egu21-3155, 2021.

The aim of this work is to investigate the application of seismological noise-based monitoring for bridge structures. A large-scale two-span concrete bridge model with a build-in post-tensioning system, which is exposed to environmental conditions, is chosen as our experimental test structure. Ambient seismic noise measurements were carried out under different pre-stressed conditions. Using the seismic interferometry technique, which is applied to the measurement data in the frequency domain, we reconstruct waveforms that relate to wave propagation in the structure. The coda wave interferometry technique is then implemented by comparing two waveforms recorded in two pre-stress states. Any relative seismic velocity changes are identified by determining the correlation coefficients and reveal the influence of the pre-stressing force. The decrease of the wave propagation velocity indicates the loss of the pre-stress and weakening stiffness due to opening or event extension of cracks. We conclude that the seismological methods used to estimate velocity change can be a promising tool for structural health monitoring of civil structures.

How to cite: Liao, C.-M., Mehrkens, F., Hadziioannou, C., and Niederleithinger, E.: Investigation of ambient noise seismological methods on a bridge model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2773, https://doi.org/10.5194/egusphere-egu21-2773, 2021.

Crustal pore pressure, which could trigger seismicity and volcanic activity, varies with fluid invasion. Various studies have discussed the potential of using seismic velocity changes from ambient noise to evaluate pore pressure conditions, especially due to rainfall perturbations. Although the influence of rainfall on seismic velocity changes has been reported, consideration of the spatial influence on rainfall towards seismic velocity and its mechanism have not been well understood. We investigated the mechanism of rainfall-induced pore pressure diffusion in southwestern Japan, using seismic velocity change (Vs) inferred from ambient noise. We modeled pore pressure changes from rainfall data based on a diffusion mechanism at the locations where infiltration is indicated. By calculating the correlation between Vs changes and the modeled pore pressure with various hydraulic diffusion parameters, the optimum hydraulic diffusion parameter was obtained. We estimated the diffusion parameters with the highest negative correlation between pore pressure and Vs change because a negative correlation indicates pore pressure increase due to diffusion induced by groundwater load. Furthermore, the spatial variation of the hydraulic diffusivity infers the heterogeneity of the rocks in different locations. This finding suggests that the response of pore pressure induced by rainfall percolation depends on location.  We show that seismic velocity monitoring can be used to evaluate the status of pore pressure at different locations, which is useful for fluid injection, CO₂ wellbore storage, and geothermal development.

How to cite: Andajani, R. D., Tsuji, T., Snieder, R., and Ikeda, T.: Spatial Evaluation of Pore Pressure Variations Related to Rainfall from Seismic Velocity Changes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1546, https://doi.org/10.5194/egusphere-egu21-1546, 2021.

Previous studies examining the relationship between the groundwater table and seismic velocities have provided contradictory results, sometimes reporting positive and sometimes negative correlations between seismic velocity and groundwater table changes. Here we introduce a physics-based model relating fluctuation in the groundwater table and the pore pressure to seismic velocity variation through change in effective stress. This model can be used to explain the contradictory results of previous studies and justifies the use of seismic velocity variation for monitoring of the pore pressure and the groundwater table. It further results in a new field method to measure the pressure dependency of the shear modulus. Using data acquired in Groningen, the Netherlands, we demonstrate that measurements of seismic velocity variation can be used to monitor the pore pressure.

How to cite: Fokker, E., Ruigrok, E., Hawkins, R., and Trampert, J.: Physics-based model relating seismic velocity variation to groundwater and pore pressure fluctuation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2969, https://doi.org/10.5194/egusphere-egu21-2969, 2021.

Ambient noise interferometry is a promising technique for studying crustal behaviors, providing continuous measurements of seismic velocity changes (dv/v) in relation to physical processes in the crust over time. In addition to the tectonic-driven dv/v changes, dv/v is also known to be affected by environmental factors through rainfall-induced pore-pressure changes, air pressure loading changes, thermoelastic effects, and so forth. In this study, benefiting from the long-term continuous data of Broadband Array in Taiwan for Seismology (BATS) that has been operated since 1994, we analyze continuous seismic data from 1998 to 2019 by applying single-station cross-component (SC) technique to investigate the temporal variations of crust on seismic velocity. We process the continuous waveforms of BATS stations, construct the empirical Green’s functions, and compute daily seismic velocity changes by the stretching technique in a frequency band of 0.1 to 0.9 Hz. We observe co-seismic velocity drops associated with the inland moderate earthquakes. Furthermore, clear seasonal cycles, with a period of near one-year, are also revealed at most stations, but with different characteristics. Systematic spectral and time-series analyses with the weather data are conducted and show that the rainfall-induced pore-pressure change is likely the main cause to the seasonal variations with high correlations. The strong site-dependency of these seasonal variations also precludes air pressure and temperature which varies smoothly in space from being dominant sources and suggests spatially-varying complex hydro-mechanical interaction across the orogenic belt in Taiwan.

How to cite: Feng, K.-F., Huang, H.-H., Hsu, Y.-J., and Wu, Y.-M.: Revealing seasonal crustal seismic velocity variations in Taiwan with single-station cross-component analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7010, https://doi.org/10.5194/egusphere-egu21-7010, 2021.

Following the passage of seismic waves, most geomaterials experience non-linear mesoscopic elasticity (NLME). This is described by a drop in elastic moduli that precedes a subsequent recovery of physical properties over a relaxation timescale. Thanks to the development of seismic interferometry techniques that allows for the continuous monitoring of relative seismic velocity changes δv in the subsurface, observations of NLME (δv_NLME) in the field are now numerous. In parallel, a growing community uses seismic interferometry to monitor velocity changes induced by seasonal hydrological variations (δv_hydro). Monitoring of these variations are often independently done and a linear superposition of both effects is mostly assumed when decomposing the observed δv signal (δv =  δv_NLME + δv_hydro). However, transient hydrological behaviour following co-seismic ground shaking has been widely reported in boreholes measurements and streamflow, which suggests that  δv_hydro may be impacted by the transient variation of material properties caused by NLME. In this presentation, we attempt to characterize the relative seismic velocity variations δv retrieved from a small dense seismic array in Nepal that was deployed in the aftermath of the  2015 Mw 7.8 Gorkha earthquake and that is prone to highly variable hydrological conditions. We first investigated the effect of aftershocks in computing δv at a 10-minute resolution centered around significant ground shaking events. After correcting δv for NLME caused by the Gorkha earthquake and its subsequent aftershocks, we test whether the corresponding residuals are in agreement with the background hydrological behaviour which we inferred from a calibrated hydrological model. This is not the case and we find that transient hydrological properties improve the data description in the early phase after the mainshock. We report three distinct relaxation time scales that are relevant for the recovery of seismic velocity at our field site:  1. A long time scale activated by the main shock of the Gorkha earthquake (~1 year) 2. A relatively short timescale (1-3 days) that occurs after moderate aftershocks. 3. An intermediate timescale (4-6 months) during the 2015 monsoon season that corresponds to the recovery of the hydrological system. This timescale could correspond to an enhanced permeability caused by Gorkha ground shaking. Our study demonstrates the capability of seismic interferometry to monitor transient hydrological properties after earthquakes at a spatial scale that is not available with classical hydrological measurements. This investigation demands calibrated hydrological models and a framework in which the different forcing of δv are coupled.

How to cite: Illien, L., Sens-Schönfelder, C., Andermann, C., Marc, O., Cook, K., and Hovius, N.: Seismic velocity recovery following the 2015 Mw 7.8 Gorkha earthquake, Nepal: Towards a coupled vision of damage and hydrological-induced velocity variations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12714, https://doi.org/10.5194/egusphere-egu21-12714, 2021.

The Reykjanes peninsula, SW Iceland, was struck by intense earthquake swarm activity that occurred in January-July 2020 due to repeated magmatic intrusions in the Reykjanes-Svartsengi volcanic system. GPS and InSAR observations confirmed surface deformation centered near Mt. Thorbjorn, and during the unrest period, approximately ~14,000 earthquakes (-2≤M≤4.9) were reported at the Icelandic Meteorological Office (IMO). We investigate the behavior of the crust as a response to these repeated intrusions to provide insights into volcanic unrest in the Reykjanes peninsula. Our study presents temporal seismic wave velocity variations (dv/v, in percent) based on ambient noise seismic interferometry using continuous three-component waveforms collected by IMO, (http://www.vedur.is) for the period from April 2018 to November 2020. The state-of-the-art MSNoise software package (http://www.msnoise.org) is used to calculate cross-correlations of ambient seismic noise and to quantify the relative seismic velocity variations. We observe that magmatic intrusions in the vicinity of Mt. Thorbjorn-Svartsengi have considerably reduced the seismic wave velocities (dv/v, -1%) in the 1-2 Hz frequency band. Seismic velocity changes were compared with local seismicity, GPS and InSAR data recorded close to the repeated intrusions, and modelled volumetric strain changes. We found a good correlation between the dv/v variations and the available deformation data. The Rayleigh wave phase-velocity sensitivity kernels showed that the changes occurring at depths down to ~3-4 km in the crust were captured by our measurements. We interpret the relative seismic velocity decrease to be caused by crack opening induced by intrusive magmatic activity. Monitoring the Mt. Thorbjorn-Svartsengi volcanic unrest is crucial for successful early warning of volcanic hazards since the center of uplift is only 2km away from a fishing village and major infrastructure in the area, such as water supply and geothermal power. For the first time in Iceland, we have provided near-real-time dv/v variations to obtain a more complete picture of this magmatic activity. Our findings are supported by the analysis of other primary monitoring streams. We propose that this technique may be useful for early detection of future intrusions/increased magmatic activity. This study is supported by the Icelandic Research Fund, Rannis (Grant No: 185209-051).

How to cite: Cubuk Sabuncu, Y., Jonsdottir, K., Caudron, C., Lecocq, T., Parks, M. M., Geirsson, H., and Mordret, A.: Temporal seismic velocity changes during the 2020 rapid inflation at Mt. Thorbjorn-Svartsengi, Iceland, using ambient seismic noise, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12924, https://doi.org/10.5194/egusphere-egu21-12924, 2021.

Our aim is to monitor the temporal evolution of the crust in Greece, with a particular focus on the Gulf of Corinth.  Indeed, Greece is one of the most exposed country to earthquakes in Europe. The Gulf of Corinth,  is known for its fast extension rate of about 15 mm/yr in the western part and 10mm/yr in the eastern part. This fast extension is associated with recurrent seismic swarms and by a few destructive earthquakes. This seismicity is likely the result of a combination of multiple driving processes including fluid migration at depth.

In the present work, we use seismic noise recorded from 2010 to 2020 by all seismic stations deployed in Greece, and in particular by the dense Corinth Rift Laboratory network, to compute the seismic velocity variation (dv/v) in several subregions. By comparing the result obtained at different periods, we are able to distinguish the temporal evolution of the upper, mid and lower crust. This temporal evolution is compared to the seismicity of the Gulf of Corinth.

How to cite: Delouche, E. and Stehly, L.: Temporal evolution of relative seismic velocity in the Golf of Corinth - Greece., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15222, https://doi.org/10.5194/egusphere-egu21-15222, 2021.

The plumbing architecture of a hydrothermal feature (e.g., geyser and spring) exerts direct control over its eruption and recharge dynamics. During an active geyser’s recharge, in response to the evolution of temperature and hydrostatic pressure within the plumbing, this two-phase-flow system experiences intensive steam bubble nucleation and collapse throughout the eruption cycle. Such steam-liquid phase transitions generate seismic signals observed as hydrothermal tremor, thus the spatiotemporal pattern of its origin can depict the plumbing architecture and illuminate how the geyser operates internally. Steamboat, the tallest active geyser on Earth, is thought to have a complex architecture and dynamics owing to the hydrologic interaction with the nearby Cistern Spring, ~100 m SW of Steamboat. To study the system, in 2019 we deployed a dense array across the Steamboat-Cistern area with an aperture of ~250 m. The array was composed of 50 three-component geophones and had a spacing of 15–35 m. During the deployment, 6 eruption cycles with intervals ranging from 3 to 8 days were recorded. We observe distinct 1–5 Hz tremor emitted from Steamboat and Cistern, which are persistent and show no isolated events and discernable arrivals. To simultaneously locate the tremor from both features, we perform multicomponent cross-correlation to isolate and enhance the coherent signals of interest with each station as the virtual source. We apply the same normalization to the 3-component data so that the particle motion excited by each virtual source is retained. We observe prevalent seismic P waves at receivers near the source, with complex wavefield transition and interference at distant receivers. Using the P wave linearity, we back project the polarized directions to constrain the 3D source location. The results provide the first 4D view of the tremor throughout the eruption cycles with hourly resolution.

The 4D view reveals the conduit beneath Steamboat is vertical and extends down to ~120 m depth and the plumbing of Cistern includes a shallow vertical conduit connecting with a deep, large, and laterally offset reservoir ~60 m southeast of the surface pool. No direct connection between Steamboat and Cistern plumbing structures is found above ~120 m. The temporal variation of the tremor combined with in situ temperature and water depth measurements of Cistern, do reveal the interaction between Steamboat and Cistern throughout the eruption/recharge cycles. The observed delayed responses of Cistern in reaction to Steamboat eruptions and recharge suggest the two plumbing structures might be connected through a fractured/porous medium instead of a direct open channel, consistent with our inferred plumbing structure.

How to cite: Wu, S.-M., Lin, F.-C., and Farrell, J.: Imaging the Hydrothermal Plumbing Architecture of Steamboat Geyser Using a Dense Nodal Array and Seismic Interferometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3739, https://doi.org/10.5194/egusphere-egu21-3739, 2021.

This study aims at characterizing different seismic sources in the region of the Vatnajökull glacier using seismic interferometry. Vatnajökull is the largest Icelandic icecap, covering 4active volcanic systems. The seismic context is therefore very complex with glacial and volcanic events occurring simultaneously and a classification between the two can become cumbersome. 

We used seismic interferometry or cross-correlation of seismic noise on seismic data from 2011 to 2019). Being based on continuous records, this passive monitoring method is not relying on earthquakes to locate seismic sources. We computed the cross-correlation functions between every pair of seismic stations using MSNoise for different frequency bands, from 0.5 to 8 Hz. The first step towards the location of seismic sources was to calculate the propagation velocities for each frequency range. The total range of velocities is between 1.39 km/s and 3.92 km/s. Then, we used two different location methods based on the calculated propagation velocities. The first method is based on hyperbole’s geometry and provides the location of seismic sources as the intersection between several hyperboles, while the second one, the Ballmer’s method (Ballmer et al. 2013), is based on the calculation of theoretical differential times and provides location probabilities for the seismic sources. We located and characterized persistent oceanic seismic noise located along the southern shoreline of Iceland potentially associated with waves activity and geometry of the shore, as well as a seasonal glacial tremor around outlet glaciers in the west part of the Vatnajökull icecap, potentially linked to glacial processes inside the glacier or in the glacial rivers. The uncertainty of a few kilometers is observed. Some limitations exist for these methods. For example, The Ballmer’s method (Ballmer et al. 2013) is reliable for seismic sources inside the seismic network but can only give an azimuthal direction for seismic sources located outside of it. When using hyperboles, slightly different propagation velocities between pairs of stations can affect the precision of the intersection. Therefore, the association of the two methods is important to diminish the impact of these limitations. 

These results provide a better understanding of the seismic background of this region and will be compared and validated with other localization methods in the future.

How to cite: Nowé, S., Lecocq, T., Caudron, C., Jónsdóttir, K., and Pattyn, F.: Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland from the analyses of correlograms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15489, https://doi.org/10.5194/egusphere-egu21-15489, 2021.

Estimation of ambient seismic source distributions (e.g. location and strength) is important for studies of seismic source mechanisms and subsurface structures. It is current state of the art to estimate the source distribution by applying full-waveform inversion (FWI) to seismic crosscorrelations. We previously theoretically demonstrated the advantage of Rayleigh-wave multicomponent crosscorrelations in the FWI estimation process. In this presentation, we utilize the crosscorrelations from real ambient seismic data acquired in Hartoušov, Czech Republic, where the seismic sources are CO2 degassing areas at Earth’s surface (i.e. fumaroles or mofettes).  We develop a complete workflow from the raw data to the FWI estimation. We demonstrate that the multicomponent crosscorrelations can better constrain the source distribution than vertical-component crosscorrelations in both elastic media and anelastic media, even when we use an elastic forward model in the inversion process. Our inversion results indicate a strong seismic source near strong CO2 gas flux areas.

How to cite: Xu, Z., Mikesell, T. D., Umlauft, J., and Gribler, G.: A full-waveform inversion workflow for estimationing ambient seismic source distributions from Rayleigh-wave multicomponent crosscorrelations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1052, https://doi.org/10.5194/egusphere-egu21-1052, 2021.

Imaging the spatio-temporal variations of ambient seismic noise sources can provide important information to improve near real-time monitoring and noise tomography. Various methods have been developed to tackle this problem. For example, Matched-Field Processing (MFP) offers an efficient data-driven approach by testing different noise source locations and subsequently correlating and stacking. A more rigorous approach is treating it as a finite-frequency full-waveform inversion problem. In contrast to the MFP technique, an inversion framework allows for the incorporation of prior information and subsequent iterative updates of the noise source distribution by numerically modelling correlations and source sensitivity kernels. Bowden et al. (2020) discuss the similarities between these two methods and how one can be derived from the other. 

We aim to compare and contrast the two methods using real data from a regional to a global scale to locate the secondary microseismic sources in the ocean. Igel et al. (2021, in prep) use a logarithmic energy ratio as measurement for the sensitivity kernels, which is chosen due to its robustness with respect to unknown 3D Earth structures. However, some disadvantages of this type of measurement are not considering absolute amplitudes and discarding information outside of the expected surface wave arrival time window. By combining the two methods and first using MFP to create an initial model for the inversion, we are able to steer the inversion in the right direction, allowing us to use a more elaborate full-waveform measurement in the inversion and hence increasing the resolution and quality of the final model. 

Results for noise source inversions in the ocean on a daily basis using the combination of the two methods will be presented. This work paves the way for publicly available, daily, multi-scale ambient noise source maps.

How to cite: Igel, J., Bowden, D., Sager, K., and Fichtner, A.: Imaging noise sources: comparing and combining a matched-field processing technique with finite-frequency noise source inversions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7515, https://doi.org/10.5194/egusphere-egu21-7515, 2021.

Under certain conditions, ocean surface gravity waves (SGW) interact with the seafloor underneath to trigger relatively faint but measurable seismic waves known as ocean microseisms. Cyclonic storms (e.g. hurricanes, typhoons) wandering over the ocean are major (non-stationary) sources of the former, thus opening the possibility of tracking and studying cyclones by means of their corresponding microseims.

For this purpose, we identified storm-related microseisms hidden in the ambient seismic wavefield via array processing. Polarization beamforming, a robust and well-known technique is implemented. The analyses hinge on surface waves (Love and Rayleigh) which, in contrast to P-waves, are stronger but only constrain direction of arrival (without source remoteness). We use a few land-based virtual seismic arrays surrounding the North Atlantic to investigate the signatures of major hurricanes in the microseismic band (0.05-0.16 Hz), in a joint attempt to continuously triangulate their tracks.

In general, a better correlation with the tracks was observed for surface waves in comparison to P waves. At the same frequency band, there is a good agreement between storm-related Love and retrograde Rayleigh wave signatures, suggesting a common amplification mechanism and co-located excitation area. However, the Love wavefield appears to be comparatively more diffuse and weaker than that of Rayleigh waves, which in turn produced the sharpest and most accurate trackings. 

Our findings show that storm microseisms are intermittently excited with modulated amplitude at localized oceanic regions, particularly over the shallow continental shelves and slopes, having maximum amplitudes virtually independent of storm category. In most cases no detection was possible over deep oceanic regions, nor at distant arrays. Additionally, the rear quadrants and trailing swells of the cyclone provide the optimum SGW spectrum for the generation of microseisms, often shifted more than 500 km off the "eye". Occasionally, the passage of a cyclone near an island appears to trigger strong stationary signals lasting for a couple of days.

As a result of the aforementioned and added to the strong attenuation of storm microseisms, the inversion of tracks or physical properties of storms using a few far-field arrays is discontinuous in most cases, being reliable only if benchmark atmospheric and/or oceanic data is available for comparison. 

Even if challenging due to the complexity of the coupled phenomena responsible for microseisms, the inversion of site properties, such as bathymetric parameters (e.g. depth, seabed geomorphology), near-bottom geology or SGW spectrum might be possible if storms are treated as natural sources in time-lapse ambient noise investigations. This will likely require near-field (land and underwater) observations using optimal arrays or dense, widespread sensor networks. Improved detection and understanding of ocean microseisms carries a great potential to contribute to mechanically coupled atmosphere-ocean-earth models. 

How to cite: Pelaez, J., Becker, D., and Hadziioannou, C.: Seeking an eye using several ears: cyclonic storms as heard by seismic arrays., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6350, https://doi.org/10.5194/egusphere-egu21-6350, 2021.

Secondary Microseisms (SM) are recorded by seismometers in the period band 3-10 s. They are generated by the interaction of ocean gravity waves of similar frequencies and coming from nearly opposite directions. Typhoons create such ocean waves, and the purpose of this study is to investigate the relationship between typhoons and microseism source characteristics. We focused our study on the Northwestern Pacific and we analyzed seismic signals recorded by the Alaska array and the corresponding storm catalog. While P body waves enable to characterize the amplitude and the localization of the sources, secondary microseisms are dominated by surface waves. Therefore, we apply beamforming technique to the vertical components in order to highlight the weaker body wave signals. This analysis permits us to track the localization of SM sources every 6 hours. Our results show three cases: In the case of one active typhoon, the positions of SM sources are localized close to the typhoon position. In the case of two nearby typhoons acting simultaneously, the SM sources are localized in between the typhoons. Finally, when the typhoon arrives close to the coast, we observe sources generated by ocean wave reflections. In conclusion, the three mechanisms proposed by Ardhuin et al., (2011) are necessary to explain secondary microseisms generated by typhoons.

How to cite: Benbelkacem, N., Stutzmann, E., Schimmel, M., Farra, V., Ardhuin, F., Barruol, G., and Mangeney, A.: Secondary Microseisms generated by typhoons in the Northwestern Pacific Ocean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4709, https://doi.org/10.5194/egusphere-egu21-4709, 2021.

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. This 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, in particular, significant wave height. We employ three different approaches: 1) The approach of Ferretti et al (2013), who compute a seismic significant wave height and invert only for the empirical conversion parameters between oceanic and seismic significant wave heights; 2) 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; and 3) A novel recurrent neural-network (RNN) approach to infer significant wave height from seismic data. 
We apply the three approaches to seismic and ocean buoy data recorded in the east coast of the United States. All three approaches are able to successfully predict ocean significant wave height from the seismic data. We compare the three approaches in terms of accuracy, computational effort and robustness. In addition, we investigate the regimes where each approach works best.  The results show that the RNN approach is able to predict well the significant wave height recorded at the buoy. The prediction is improved if several nearby seismic stations are used rather than just one. 
This work is supported by FCT through projects UIDB/50019/2020 – IDL and UTAP-EXPL/EAC/0056/2017 - STORM.

How to cite: Bolrão, F., Tran, C., Lima, M., Sheriffdeen, S., Rodrigues, D., Silveira, G., Bui-Thanh, T., and Custodio, S.: Inferring significant wave height from seismic data: A comparison between methods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14837, https://doi.org/10.5194/egusphere-egu21-14837, 2021.

Ambient noise correlation uses the recordings of multiple, statistically distributed seismic sources (the noise sources) at two seismometers. By cross-correlating this signal, one obtains a wave traveling between two seismometers. Due to the principle of reciprocity it is possible to interchange the role of sources and receivers. This cannot be done with ambient noise, but another stochastic signal, the seismic coda is used. Using a cross-correlation of the seismic coda of two earthquakes recorded at multiple seismometers, it is possible to construct a seismic wave traveling between the two earthquakes in depth (inter-source interferometry). Here, I use the the time lag of the maxima in the cross-correlation of the coda wave field to measure the shear wave velocity in the source volume of swarm earthquakes. This technique is different from previous studies analyzing the decorrelation of the coda wave field of nearby events or using the cross-correlation for relocation purposes.

The technique is applied to five event clusters of the 2018 West Bohemia earthquake swarm. With the help of a high quality earthquake catalog, I was able to determine the shear wave velocity in the region of the five clusters separately. The shear wave velocities range between 3.5 km/s and 4.2 km/s. The resolution of this novel method is given by the extent of the clusters and better than for a comparable classical tomography. The method can be incorporated into a tomographic inversion to map the shear wave velocity in the source region with unprecedented resolution. The influence of focal mechanisms and the attenuation properties on the polarity and location of the maxima in the cross-correlation functions is discussed.

How to cite: Eulenfeld, T.: Shear wave velocity from inter-source interferometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8408, https://doi.org/10.5194/egusphere-egu21-8408, 2021.

The capability of the ambient noise tomography (ANT) to image subtle regional-scale velocity variations in the lower crust is limited by strong directionality of ambient noise sources in central Europe, which affects the quality of dispersion curves. Significant decrease of sensitivity kernels and sparse coverage of long interstation ray-pathes result in lower resolution at longer periods and thus increase uncertainty of the inversion solution in depth. If these well-known ANT limitations are properly addressed, the ANT is able to retrieve reliable high-resolution 3‑D shear velocities of the lower crust.

In this study we focus on seasonal variations of ambient noise sources in selected sites in different tectonic settings. We analyse ambient noise sources on continusly recorded wavefields from permanent observatories and temporary stations of AlpArray passive experiment with its complementary experiment and PACASE. These seismic networks with densely-spaced stations are well-suited for detailed analysis of period-dependent directionality of ambient noise sources and their effects on FTAN appearance and consequently on the quality of dispersion curves. In the second part of this study, we advocate a concept of layer-stripping during the stochastic inversion (enhanced ANT). It proved to be an efficient technique to explore the model space, particularly in the lower part of the crust. We discuss the sensitivity of the enhanced ANT to the imaged small-scale velocity features in the lower part of the crust, as well as the sensitivity to the sharp or gradational Moho in the models.

How to cite: Kvapil, J., Plomerova, J., and Working Group, A.: Sensitivity enhancement of the ambient noise tomography and analysis of seasonal variations of noise sources to refine velocities in the lower crust in central Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10931, https://doi.org/10.5194/egusphere-egu21-10931, 2021.

Since explosive and impulsive seismic sources such as dynamite, air guns, gas guns, or even vibroseis can have a big impact on the environment, some companies have decided to record ambient seismic noise and use it to estimate the physical properties of the subsurface. Big challenges arise when the aim is extracting body-waves from recorded passive signals, especially in the presence of strong surface waves. In passive seismic signals, such body-waves are usually weak in comparison to surface waves which are much more prominent. To understand the characteristics of passive signals and the effect of natural source locations, three simple synthetic models were created. To extract body-waves from simulated passive signals we propose and test a Radon-correlation method. This is a time-spatial correlation of amplitudes with a train of time-shifted Dirac delta functions through different hyperbolic paths. It is tested on a two-layer horizontal model, three-layer model which includes a dipping layer (with and without lateral heterogeneity) and also on synthetic Marmousi model data sets. Synthetic tests show that the introduced method is able to reconstruct reflection events at the correct time-offset positions which are hidden in results obtained by the general cross-correlation method. Also, a depth migrated section shows a good match between imaged-horizons and the true model. It is possible to generate off-end virtual gathers by applying the method to a linear array of receivers and to construct a velocity model by semblance velocity analysis of individually extracted gathers.

How to cite: Hariri Naghadeh, D., J Bean, C., Smith, P., Lebedev, S., and Mohamed, H.: Retrieving reflection arrivals from passive seismic data using Radon correlation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9179, https://doi.org/10.5194/egusphere-egu21-9179, 2021.

High-frequency seismic surface waves sample the top few tens of meters to the top few kilometres of the subsurface. They can be used to determine three-dimensional distributions of shear-wave velocities and to map the depths of discontinuities (interfaces) within the crust. Passive seismic imaging, using ambient noise as the source of signal, can thus be an effective tool of exploration for mineral, geothermal and other resources, provided that sufficient high-frequency signal is available in the ambient noise wavefield and that accurate, high-frequency measurements can be performed on this signal. Ambient noise imaging using the ocean-generated noise at 5-30 s periods is now a standard method, but less signal is available at frequencies high enough for deposit-scale imaging (0.2-30 Hz), and few studies have reported successful measurements in broad frequency bands. Here, we develop a workflow for the measurement of high-frequency, surface-wave phase velocities in very broad frequency ranges. Our workflow comprises (1) a new noise cross-correlation procedure that accounts for the non-stationary properties of the high frequency noise sources, removes bandpass filtering, replaces temporal normalization with short time window stacking, and drops the explicit spectral normalization by adopting cross-coherence; (2) a new phase-velocity measurement method that extends the bandwidth of reliable measurements by exploiting the (resolved) 2π ambiguity of phase-velocity measurements; (3) interstation-distance-dependent quality control that uses the similarity of subgroups of dispersion curves to reject outliers and identify the frequency ranges with accurate measurements. The workflow is highly automated and applicable to large arrays. Applying our method to data from a large-N array that operated for one month near Marathon, Ontario, Canada, we use rectangular subarrays with 150-m station spacing and, typically, 1 hour of data and obtain Rayleigh-wave phase-velocity measurements in a 0.55-23.8 Hz frequency range, spanning over 5.4 octaves, nearly twice the typical frequency range of 1.5-3 octaves in previous studies. Phase-velocity maps and the subregion-average 1D velocity models they constrain show a high-velocity anomaly consistent with the known, west-dipping gabbro intrusions beneath the area. The new structural information can improve our understanding of the geometry of the gabbro intrusions, hosting the Cu-PGE Marathon deposit.

How to cite: Xu, Y., Lebedev, S., Bonadio, R., Meier, T., and Bean, C.: High-frequency seismic interferometry: broadband measurements of surface-wave phase velocities using “large-N” arrays, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-937, https://doi.org/10.5194/egusphere-egu21-937, 2021.

How to cite: Giammarinaro, B., Hillers, G., Seydoux, L., Tsarsitalidou, C., Catheline, S., Campillo, M., Roux, P., and de Rosny, J.: Seismic surface wave focal spot imaging: resolution tests using time-reversal simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4226, https://doi.org/10.5194/egusphere-egu21-4226, 2021.

How to cite: Tsarsitalidou, C., Boué, P., Hillers, G., Giammarinaro, B., Campillo, M., Seydoux, L., and Stehly, L.: Seismic imaging with focusing surface waves obtained from USArray noise correlation functions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9045, https://doi.org/10.5194/egusphere-egu21-9045, 2021.

    Estimating seismic scattering and intrinsic absorption parameters, which are measures of medium heterogeneity, is important for understanding the complex structure in shallow regions of volcanoes. In recent years, seismic ambient noise cross-correlation functions (CCFs) have been used instead of records of natural earthquakes or active seismic experiments to estimate those parameters (e.g., Hirose et al., 2019; Hirose et al., 2020; van Dinther et al., 2020). This passive approach possibly allows us to estimate scattering and intrinsic absorption parameters in previously unmeasured regions and frequency bands. In this study, we apply the passive estimation method proposed by Hirose et al. (2019) to 18 active volcanoes in Japan and measure those parameters of Rayleigh waves. We used three-component seismic ambient noise data in the frequency bands of 0.5-1 Hz, 1-2 Hz, and 2-4 Hz at seismic stations of NIED, JMA, HSRI, and MFRI. Before computing CCFs, the temporal flattening technique (Weaver, 2011) was applied to ambient noise data for reducing the effect of temporal fluctuations in noise levels with retaining relative amplitudes among the stations. Daily CCFs of three components (ZZ, ZR, ZT) were computed by stacking 10-minutes-CCFs. We stacked daily CCFs over 1 year and computed mean squared envelopes by smoothing squared amplitude with 4 s (0.5-1 Hz), 2 s (1-2 Hz), or 1 s (2-4 Hz) long time windows. Scattering and intrinsic absorption parameters were estimated by modeling the space-time distributions of energy densities calculated from CCFs with 2D radiative transfer theory. Best-fit values of scattering mean free path at the 18 active volcanoes range between 1.0-4.6 km at 0.5-1Hz band, 0.7-2.9 km at 1-2 Hz band, and 0.9-2.9 km at 2-4 Hz band, respectively. These values are 2 orders of magnitude shorter than those in non-volcanic regions (e.g., Sato et al., 2012). Those of intrinsic absorption parameter range between 0.05-0.26 s^-1 at the 0.5-1 Hz band, 0.06-0.24 s^-1 at the 1-2 Hz band, and 0.06-0.32 s^-1 at the 2-4 Hz band, respectively. They are at most one order of magnitude larger than those in the non-volcanic regions. Especially strong intrinsic attenuations are estimated at volcanic islands. Water-bearing layers at a depth of several hundred meters below these islands may cause such strong intrinsic attenuations. The frequency dependence of scattering attenuations is also strong at these volcanic islands, suggesting non-uniform structures that largely fluctuate along depths. The results of this study suggest that the passive estimation method of scattering and intrinsic absorption parameters proposed by Hirose et al. (2019) is applicable to various volcanoes. Comparing estimated values of these parameters at various volcanoes will improve our understanding of complex structure at the shallow regions of volcanoes. Moreover, the parameters estimated in this study will boost locating spatial distributions of seismic velocity and/or scattering property changes associated with volcanic activities at the 18 volcanoes.

Acknowledgments: We used seismograms recorded by Japan Meteorological Agency (JMA), Hot Springs Research Institute (HSRI) of Kanagawa Prefecture, and Mount Fuji Research Institute (MFRI), Yamanashi Prefectural Government.

How to cite: Hirose, T., Ueda, H., and Fujita, E.: Passive estimation of scattering and intrinsic absorption parameters at 18 active volcanoes in Japan using envelopes of seismic ambient noise cross-correlation functions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7106, https://doi.org/10.5194/egusphere-egu21-7106, 2021.

Temporary seismic networks installed in urban areas provide a powerful tool for investigating shallow geological structures and assessing the seismic hazard using passive seismic methods, including auto- and cross-correlation of seismic noise. To examine the feasibility of the methods to image the uppermost geological structures, 20 broad- and mid-band seismological stations were deployed progressively throughout Quito in an irregular array to record ambient seismic noise between May 2016 and July 2018.

Quito, the capital of Ecuador, is located in a high seismic zone, 180 km from the Pacific subduction zone and surrounded by crustal-faults prone to generate significant earthquakes. 

The city is built on a sedimentary basin, located on the hanging wall of a system of active reverse faults. The high population density (around 2.5 million inhabitants) and the lack of planning of most of its buildings, make Quito a metropolis exposed to high seismic risk. In Quito, the basin's filling has been described as volcano-sedimentary sequences consisting of lavas, lahars, lacustrine, and pyroclastic deposits (Alvarado et al., 2014). However, the thickness of the in-fill material, its spatial arrangement, and the basin's deep structure remain poorly known. 

This study presents the results of ambient noise auto- and cross-correlation of simultaneous operating seismic stations to retrieve: 1)  zero-offset high frequency body-wave crustal reflections, and 2) inter-station, surface-wave Green's functions in the frequency band 0.1 - 2 Hz. 

Auto-correlation of seismic noise indicated at least one reflection within the first 2.5 s from the surface. 

Careful analyses of day-night variations in noise spectral power were carried out to select optimal time windows for the cross-correlation. Additionally, Rayleigh and Love phase-velocity dispersion curves were inverted to obtain shear wave velocity profiles throughout the city. Love wave trains traveling in the longitudinal direction of the basin (NNE-SSW) are much clearer than Rayleigh wave trains.

The surface-wave Green’s functions and their inversions suggest a clear difference in the basin's structure between the northern and southern parts. In the north, we detect the seismic basement at a depth of about 300 meters, whereas in the south, it appears much deeper at around 1000 meters. This significant difference could be the main explanation for the low-frequency amplification (at 0.3 Hz) highlighted in the southern part of the basin from earthquake recordings (e.g., the Mw 7.8 Pedernales earthquake on April 2016) and by the analysis of spectral ratios (Laurendeau et al., 2017).

References:

How to cite: Pacheco, D., Mercerat, D., Courboulex, F., Bonilla, F., Laurendeau, A., and Alvarado, A.: Seismic Imaging Using Auto- and Cross-correlation of seismic noise in the Quito (Ecuador) basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14056, https://doi.org/10.5194/egusphere-egu21-14056, 2021.