Retrieving surface waves using linear arrays is becoming gradually popular in urban areas with abundant anthropogenic noise [Mi et al., 2020]. Dispersion measurements can be problematic due to the presence of off-line noise sources. We use the polarization analysis of three-component noise recordings to estimate the back-azimuth and intensity of sources for linear arrays. The noise segment where the source locates in the stationary-phase zones (SPZs) is retained and the noise cross-correlation function (NCF) is weighted according to the source intensity. In this way, we obtain accurate virtual shot gathers and dispersion images.

A single three-component seismic station can simultaneously record vertical, north, and east displacements. The back-azimuth and intensity of a noise source within a time segment can be estimated by the relationship between the vertical-horizontal cross-spectra [Takagi et al., 2018]. In practical applications, we remove the mean and trend of the raw three-component noise recordings and divide them into multiple segments. We use the polarization analysis for each segment to locate the orientations and intensities of the noise sources. We then average the results obtained at multiple stations in a linear array to obtain more robust results. We retain the noise segments where the noise sources are distributed in the SPZs and perform weighted stacking of their NCFs according to the intensities of the noise sources in these segments to obtain the final NCF and perform the subsequent dispersion measurement. We use a synthetic experiment and two field examples to demonstrate the superiority of our proposed method. After using the proposed method, the NCFs become more accurate with a higher signal-to-noise ratio, and the trend of the dispersion energy is more continuous.

Takagi, R., Nishida, K., Maeda, T. & Obara, K., 2018. Ambient seismic noise wavefield in Japan characterized by polarization analysis of Hi-net records. Geophysical Journal International, 215, 1682–1699. doi:10.1093/gji/ggy334

Mi, B., Xia, J., Bradford, J.H. & Shen, C., 2020. Estimating near-surface shear-wave-velocity structures via multichannel analysis of Rayleigh and Love waves: an experiment at the Boise hydrogeophysical research site. Surveys in Geophysics, 41, 323–341. doi:10.1007/s10712-019-09582-4

How to cite: Guan, B. and Xia, J.: Improving dispersion measurement using weighted stacking based on polarization analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14919, https://doi.org/10.5194/egusphere-egu24-14919, 2024.

Railways are exposed to geotechnical hazards such as sinkholes or subsidence because they encounter many geological settings. Railway subsurface imaging is thus important in order to detect small anomalies in the elastic properties of the upper 50 meters of soils. Seismic interferometry applied on train induced signals is a promising technique, and previous works have shown clear Rayleigh waves and reflections in the retrieved Green’s functions. However, extracting the dispersion curves of the reconstructed Rayleigh waves in a automated and robust way with high-resolution is challenging.

We study a continuously acquired dataset consisting of a dense array of five lines of accelerometers deployed parallel to 120 m of track. We use Matched Field Processing (MFP) to recover a high-resolution S-wave velocity model of the subsurface. The workflow begins by correlating signals in time windows when the train is outside the array, to ensure nearly planar wavefronts before the interferometry step. However, using only trains far from the array discards the measurements associated to the train crossing the array which have a very high signal-to-noise ratio but are difficult to model. We observe experimentally that train crossings the array generate correlations compatible with the isotropic and uncorrelated source distribution hypothesis used in passive seismology. Under this assumption, we correlate signals when the train is directly next to the sensors. Combined with correlations for the train in the far-field, this allows to track the Rayleigh dispersion curve in the very high frequencies (> 50 Hz). This broadband dispersion curve extraction, along with the balanced azimuthal coverage of our image due to the source diversity, is helpful for reliable imaging of shallow structures ranging from the bottom of the ballast to the bedrock. Since array methods are robust, and trains are repeatable sources, this paves the way for reliable monitoring of the subsurface with unprecedented temporal and spatial resolution.

How to cite: Rebert, T., Bardainne, T., Cai, C., Allemand, T., and Chauris, H.: Matched Field Processing of train vibrations for opportunistic surface wave tomography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18405, https://doi.org/10.5194/egusphere-egu24-18405, 2024.

Dense seismic networks are ideally suited to detect daily or even hourly variations of the global secondary microseism via ambient noise cross-correlation beamforming (CCBF) and backprojection (BP) in the slowness-backazimuth domain. We combine the seismic recordings from Hi-net in Kyushu (HINET) network, Southern California Seismic Network (SCSN), and the Large-N AlbaNian TectonIcs of Continental Subduction (ANTICS) network to capture 3-hourly and daily northern hemisphere secondary microseism variations during 2022-2023. We calculate stable ambient noise CC with 300 s lag and 24 substacks per day. In the secondary microseism period band, 1-10s, we detect clear and vigorous high apparent velocity P phase (> 8 km/s) arrivals in 3-hourly and daily stacks for these networks. Both the 3-hourly and daily stacks show clear temporal amplitude and delay time changes in station-pair-distance and symmetry changes of causal and acausal branches, indicating the active evolution of ambient noise source location and strength. For ANTICS, the strongest energy patch emerges with back-azimuth (BAZ) 280°-330° and slowness around 8-10 s/deg. Further two energy patches appear with BAZ 90°-135° and slowness of 4-6 s/deg as well as 0° BAZ and slowness of 5-7.5 s/deg . We back-project the energy from the beamforming to the source location based on IASP91 velocity model assuming the propagation of teleseismic energy as direct P wave (including Pdiff, PKiKP and PKIKP). The back-projection results reveal that the strongest energy comes from the North Atlantic covering a broad arc-shape area (from the northeast coast of the US to the west coast of the UK, and from the south of Greenland and Iceland down to 45°N). The two other energy patches with much higher apparent velocities originate from the south Indian Ocean and the north Pacific near the Aleutian Islands. The 3-hourly and daily changes are tracked and recovered by the CCBF and BP approach for all three networks. The secondary microseism variations in the north Pacific could be improved by the SCSN and HINET whereas the north Atlantic is constrained by ANTICS and SCSN. Some small-scale autumn storms near the Japanese trench are also detected and tracked. Our results are consistent with existing wave height maps and provide a new and cheap observation for hindcasting of the state of the coupling of oceans and the solid earth. 

How to cite: Gao, Y., Rietbrock, A., Frietsch, M., Agurto-Detzel, H., Kufner, S.-K., Dushi, E., Rama, B., Koxhaj, D., Schurr, B., and Tilmann, F.: Teleseismic body wave phase extracted from ambient noise interferometry constrains the secondary microseism sources of Northern Hemisphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18041, https://doi.org/10.5194/egusphere-egu24-18041, 2024.

The understanding of microseism-source characteristics has become increasingly important, particularly in the context of retrieving Green's functions, which play a crucial role in various fields of seismology. This study aims to elucidate the characteristics of microseismic sources, encompassing seasonal variations in the activity of primary and secondary microseisms, along with periodic changes observed in microseismic peaks. The analysis involved data from seven stations carefully selected from the Korean Meteorological Administration seismic network, each with over 10 years of continuous data. Employing cross-correlation techniques, we calculated Empirical Green's Functions (EGFs) between 17 station pairs. The averaged spectra of the calculated EGFs revealed two primary peaks, concentrating energy distribution around 18 seconds and in the period range of 2-5 seconds, aligning with the peaks associated with Primary and Secondary microseism. We then categorized spectral energy distribution data into less than and more than 10 seconds, aiming to discern distinct characteristics associated with the two microseismic peaks. Subsequently, we examined average temporal energy variations for each microseism, generating, we say, spectral-time series data by summing and averaging separated energy in the period direction, and calculating energy variations for each period using a multi-filtering technique (MFT). Observing prominent dominant changes with a 1-year period in both Primary and Secondary microseisms, we noted additional periodic variations with 6 months, 3 months, and 2 months in Secondary microseism. Specifically focusing on 1-year period changes, Primary microseism displayed dominance during the summer, with lower energy levels in the winter across the entire area. For Secondary microseism, 1-year period changes often showed the lowest values in the summer and the highest values in the winter. Additionally, in Secondary microseism, maximum or minimum values were observed in the spring and autumn, resembling patterns observed in Primary microseism. Simultaneously, we reconstructed the dominant period of ocean gravity waves from Wave Watch III to explore the effect on microseisms around the stations used in this study. Comparing this data with calculated the spectral time series data of Primary and Secondary microseisms revealed not only a match in the 1-year period but also in detailed phases below 1 year. This implies a close relationship between microseisms around the Korean Peninsula and ocean activities, prompting future research to delve into detailed periodic changes, correlate them with ocean activities, and identify their underlying causes.

How to cite: Jang, Y. and Lee, W.: Microseismic Characteristics: Seasonal Variations, Periodic Changes, and Oceanic Influences in the Korean Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7251, https://doi.org/10.5194/egusphere-egu24-7251, 2024.

    Under a uniform scattered wave field, the continuous recording of Cross-Correlation Functions (CCF) between monitoring stations can be approximated by the Green's function between these stations. Therefore, passive seismic noise interference techniques can be employed to monitor changes in the structural properties of the Earth. The Zhongliao Tunnel in the southern section of National Highway No. 3 cuts through two significant active structures, the chishan Fault and the Chekualin Fault. In order to investigate the impact of fault activity on the tunnel's structure, our laboratory deployed a dense array composed of 33 portable seismometers around the Zhongliao Tunnel starting in 2020, conducting seismic observations for an entire year. With an average station spacing of less than 1 kilometer in this dense array, there is a chance to obtain high-frequency Green's functions, enabling monitoring of shallow structures. However, non-uniform distribution of noise energy may cause differences between interference waveforms and real Green's functions. Therefore, this study focuses on the spatiotemporal characteristics of background high-frequency signals, aiming to clarify their sources as a foundation for future research. Through Power Spectral Density (PSD) analysis of the stations, we observed energy drops at night in the range of 1-12Hz , possibly attributed to body waves or surface waves generated by vehicular traffic on the highway. To identify the distribution of noise sources, the research area was subdivided into 121 grid points as potential signal sources. Surface wave and body wave energy decay characteristics were fitted separately to the spatial distribution of that energy, revealing that the predominant seismic mode is surface waves, and the most likely noise source is located at the tunnel entrance, unevenly distributed on the highway. Furthermore, an analysis of the amplitude asymmetry in the cross-correlation functions between stations indicated that the high-frequency signals originate from the tunnel entrance. As there are no specific conditions near the tunnel entrance that can autonomously generate high-frequency signals, we speculate that these signals are still caused by vehicles on the highway. When seismic waves propagate around the tunnel, the velocity structure causes energy to focus at the tunnel entrance, radiating outward. In the future, we will use Eikonal tomography to analyze the velocity structure beneath the array and conduct waveform simulations to test this hypothesis.

How to cite: Lin, T.-Y., Chen, Y.-N., and Rau, R.-J.: Ambient noise characteristics of the Chung-Liao Tunnel area in National Highway No. 3, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17499, https://doi.org/10.5194/egusphere-egu24-17499, 2024.

Previous research suggests that continuous seismic noise records can effectively extract information about the properties of the Earth's subsurface. The coda of the correlation wavefield between station pairs shows sensitivity to crustal heterogeneity and has been described as a multiple scattering signal. These signals allow to monitor variations in dv/v to detect weak changes in the medium at depth. Oceanic regions, which are highly effective in generating microseisms, play a crucial role in the distribution of seismic energy sources. In Green's function estimates from cross correlations, highly asymmetric correlation wavefields are common due to non-homogeneous source distributions.

This study focuses on the impact of oceanic noise sources on the coda of the correlation wavefield between station pairs. We utilized ambient seismic noise interferometry to retrieve the correlation wavefields between some master stations throughout Europe and the Gräfenberg array located in Germany, in the microseism frequency range. We then applied cross-correlation beamforming to these correlation wavefields. This identifies the source direction for correlation wavefields over a three-year period, allowing us to compare variations in source direction and seasonality with results from raw data beamforming. We find dominant source directions towards the north-northwest of Gräfenberg in winter (with slowness expected for surface waves) and towards the south in summer (with slowness expected for body waves) in the raw data and throughout the coda of the correlation wavefields up to lapse times of one hour. This is in contrast to the diffuse wavefield expected from classical seismic interferometry and demonstrates that higher-order correlations, which are computed during the correlation beamforming of correlation functions, do not improve the degree of scattering in the correlation wavefield coda when persistent, isolated noise sources are present. Additionally, the findings demonstrate notable correlation between the seasonal incidence of microseisms and the very late coda of the correlation wavefields, raising questions about the current understanding of the correlation wavefield coda.

How to cite: Safarkhani, M., Schippkus, S., and Hadziioannou, C.: Sensitivity of coda correlation wavefields to spatio-temporal variations of microseism noise sources, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8529, https://doi.org/10.5194/egusphere-egu24-8529, 2024.

Microseism, the most continuous seismic signal on the Earth generated by the interaction between the hydrosphere, the atmosphere, and the solid Earth, is a useful tool for acquiring information about climate change. Indeed, several authors dealt with the relationship microseism-sea state and microseism-cyclonic activity, considering in particular tropical cyclones, hurricanes, typhoons, and recently Medicanes (small-scale tropical cyclones that occur in the Mediterranean Sea). In this study, we analyze, from a seismic point of view, several meteorological events that occurred in the Mediterranean Sea during the period November 2011 - February 2023. In particular, we consider 9 Medicanes and 4 more common storms. Despite the marked differences between them, each of these events caused heavy rainfall, strong wind gusts, violent storm surges with significant wave heights usually greater than 3 meters, and damage along the exposed coast. Occasionally, these events caused deaths and injuries. In this work, we analyzed the seismic signal recorded by 104 seismic stations, installed along the Italian, Maltese, Greek, and France coastal areas, and 15 seismic stations, installed in the Etnean area used only to perform array analysis. We deal with the relationships between the considered meteorological events and the features of microseism in terms of spectral content, space-time variation of the amplitude, and source locations tracked using two different methods (a grid search approach based on seismic amplitude decay and array techniques). By comparing the positions of the microseism sources, obtained from our analysis, with the areas of significant storm surges, retrieved from hindcast data, we observe that the microseism locations are in agreement with the actual locations of the storm surges for 10 out of 12 events analyzed (two Medicanes present very low intensity in terms of meteorological parameters and the microseism amplitude does not show significant variations during these two events). In addition, we also carried out two analyses that allowed us to obtain both the seismic signature of these events, by using a method that exploits the coherence of continuous seismic noise, and their strength from a seismic point of view, called Microseism Reduced Amplitude. By integrating the results obtained from these two methods, we can “seismically” distinguish Medicanes and common storms. Consequently, we demonstrate the possibility of creating a novel monitoring system for Mediterranean meteorological events by incorporating microseism information alongside with other techniques (e.g. wave buoy, wave gauge, and High-Frequency coastal radar) commonly used for studying and monitoring meteorological phenomena. In addition, since the seismometers were among the first geophysical instruments installed, it is possible to digitize old seismograms and examine historical data shedding new light on extreme weather events in a climate change scenario.

How to cite: Borzì, A. M., Minio, V., De Plaen, R., Lecocq, T., Cannavò, F., Ciarolo, G., D'Amico, S., Lo Re, C., Monaco, C., Picone, M., Scardino, G., Scicchitano, G., and Cannata, A.: Distinguishing between Medicanes and common seasonal storms using microseism, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10482, https://doi.org/10.5194/egusphere-egu24-10482, 2024.

In March 2022, a seismic crisis was declared in São Jorge Island. Despite the regular seismotectonic activity observed in the Azores Central Group, São Jorge has not exhibited significant activity since the crisis associated with an eruption in 1964. Between the fall of 2021 and the end of 2022, approximately 12,000 earthquakes (magnitudes up to ML 3.8) have been recorded, with the seismicity and geodetic modelling pointing to a magmatic intrusion. Intrusions cause gas release, fluid circulation, and pressure perturbations in the subsurface volcanic system that often induce changes in seismic velocity. Here, we probe spatial-temporal changes in the seismic velocity structure beneath São Jorge using ambient noise interferometry.

In this study, we analyzed data continuously recorded between January 2021 and December 2022 by two permanent stations (PMAN and ROSA) operated by the Instituto Português do Mar e da Atmosfera (IPMA) to investigate the presence of subsurface structural changes in response to the seismic crisis. Data were cut into 1-hr length files and filtered between 1 and 3 Hz for autocorrelation, and between 0.1 and 1.0 Hz for cross-correlation. We applied the Phase Auto- and Cross-Correlation (PAC and PCC) method to the filtered data. This method is based on phase coherence and is amplitude-unbiased. PAC and PCC functions were then linearly stacked over three days to achieve a stable noise response. To infer changes in the velocity structure, we analyzed the waveform similarity values for different time lag windows. We compared the waveform similarity results with meteorological data and ground deformation inferred from GPS. Additionally, relative velocity changes have been estimated. 

The two analyzed stations exhibit different waveform-similarity results. Preliminary interpretation of PMAN results (closer to the island center) show, in the second half of 2022, a very slight recovery of the waveform similarity at shorter lag times (shallower depths) that decreases again in the fall of the same year. Globally, data from this station exhibits a more systematic decorrelation when the crisis was declared, most likely due to perturbations in the seismic structure between < 8 - 10 km and deeper than 15 km. 

This work is a contribution to RESTLESS (DOI:10.54499/PTDC/CTA-GEF/6674/2020) and GEMMA (DOI:10.54499/PTDC/CTA-GEO/2083/2021). It was also funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020).

How to cite: Silveira, G., Carvalho, J., Schimmel, M., Mendes, V., Dias, N., Custódio, S., Fontiela, J., Hicks, S. P., and Ferreira, A.: Ambient noise interferometry to investigate temporal changes in the São Jorge Island (Azores) subsurface structure associated with the 2022 seismic crisis. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16974, https://doi.org/10.5194/egusphere-egu24-16974, 2024.

Campi Flegrei is a volcanic field located in the immediate vicinity of the densely populated area of Naples, Italy. Since 2005, the area has been experiencing a new bradyseismic crisis, i.e., a slow uplift of the subsurface caused by rising fluids in the subsurface. The uplift is accompanied by earthquake activity that has been steadily increasing for years, culminating in the strongest earthquake (ML 4.2) in the last 40 years on September 27, 2023. Such uplift and earthquakes can cause changes in seismic velocity and are often succeeded by a volcanic eruption. In this study, we utilize seismic noise to calculate velocity changes at different levels/frequencies over a 7-year period using passive image interferometry. The observed long-term velocity decrease of 1.39 % near the surface over the period from 2016 to 2023 can be explained by a volume increase of the hydrothermal system at the depth of 3 km. In 2023, the Campi Flegrei underwent several phases of velocity change. After a period of minor velocity changes, there was a gradual 0.7 % increase in velocity starting in May. Following the onset of the earthquake swarm in August, the velocity slowly decreased once again.

How to cite: van Laaten, M., Müller, J., and Wegler, U.: Monitoring seismic velocity changes at Campi Flegrei (Naples) using seismic noise interferometry - Do we see precursors of the future volcanic activity?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8922, https://doi.org/10.5194/egusphere-egu24-8922, 2024.

Mexico City, the most populated city in the Americas, undergoes significant seismic hazards. Conventional seismometers often suffer from limited spatial density, restricting detailed observations in urban areas. In contrast, Distributed Acoustic Sensing (DAS) can convert standard telecommunication fiber-optic cables into dense seismic arrays, providing great potential for high-resolution spatiotemporal monitoring. Therefore, we installed a DAS interrogator in Mexico City in May 2022 in a long-term fashion to collect data for observational studies in the region. The fiber crosses the city from south to north along a 29-kilometer path following the subway track. The dataset comprises 2266 channels with a 12.8-m spacing and a 200-Hz sampling rate. 

On Sep. 19, 2022, a Mw7.6 earthquake occurred in Michoacán, approximately 450 km away from the City. Exactly 37 years after the great 1985 Mw8.1 event. The DAS system provided high-quality, ultra-dense, yet unique data for this earthquake in particular. One of the goals of this study is to assess the earthquake-induced changes in the sedimentary basin material properties. For this endeavor, we employ seismic interferometry on the ambient noise field of DAS data and the stretching method to monitor seismic velocity variations in Mexico City. Our analysis reveals a velocity drop following the 2022 Mw7.6 earthquake in some city areas. The results indicate that DAS can effectively monitor the velocity variations in urban environments, offering valuable insights for urban hazard assessment.

How to cite: Li, Y., Perton, M., Sánchez-Sesma, F. J., and Spica, Z.: Monitoring Spatiotemporal Seismic Velocity Changes Using Seismic Interferometry and Distributed Acoustic Sensing in Mexico City, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4190, https://doi.org/10.5194/egusphere-egu24-4190, 2024.

Soil moisture is a key metric to assess soil health. Water held in the shallow subsurface between soil particles enables various biogeochemical and hydrological processes indispensable to soil functions. Potential soil moisture deficit may raise the irrigation demands, which further exacerbates the stress on the water supply. The changes in soil moisture can impact climate, further amplifying the climatic anomalies and intensifying extreme weather events. Thus, understanding soil moisture and its dynamics over time are of broad scientific interest and practical implications.

Despite the vital importance of soil moisture, it still lacks sufficient means to properly assess the parameter at a regional scale, which is an essential research dimension for addressing practical issues in the agricultural and environmental sectors. 

Ambient noise seismology provides new possibilities to infer subsurface changes in a real-time, non-intrusive, and costless manner.
In this study, we map the temporal variations in soil moisture for Central-Southern Europe with ambient seismic noise. It is the first time that the seismic method has been applied to map soil moisture at a regional scale using an ordinary seismic network setup. The method helps in bridging the resolution gap between current pointwise (e.g., tensio-, electrical- and neutron-meter) and global (e.g., satellite-based remote sensing) investigations, providing complementary information for both scientific research and public decision-making. 

How to cite: Lu, Y., Wang, Q.-Y., and Bokelmann, G.: Mapping large-scale deep soil moisture variation using ambient seismic noise, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10391, https://doi.org/10.5194/egusphere-egu24-10391, 2024.

Passive seismic interferometry has become a popular technique towards monitoring subsurface activities in a variety of settings. This includes active volcanoes and geothermal fields spanning a large range of temperatures (25C to 250C) in Belgium and Iceland. The method depends on the relative stability of background seismic sources in order to make repeatable measurements of subsurface properties. Such stability is typically assessed by examining the similarity of cross-correlation functions through time. Thus, techniques that can better assess the temporal similarity of cross-correlation functions may aid in discriminating between real subsurface processes and artificial changes related variable seismic sources.

In this work, we apply agglomerative hierarchical clustering to cross-correlation functions computed using seismic networks at volcanoes. These include Piton de la Fournaise volcano (La Réunion island) and Mt Ruapehu volcano (New Zealand). Clustering is then used to form groups of cross-correlation functions that share similar characteristics and also, unlike common similarity measures, the method does not require a defined reference period. At Piton de la Fournaise, we resolve distinct clusters that relate both to changes in the seismic source (volcanic tremor onset) and changes in the medium following volcanic eruptions. At Mt Ruapehu, we observe a consistency to cross-correlation functions computed in the frequency band of volcanic tremor, suggesting tremor could be useful as a repeatable seismic source. 

Our results demonstrate the potential of hierarchical clustering as a similarity measure for cross-correlation functions, suggesting it could be a useful step towards recognizing structure, or complex patterns, in seismic interferometry datasets. This can benefit both decisions in processing and interpretations of observed subsurface changes.

How to cite: Yates, A., Caudron, C., Lesage, P., Mordret, A., Lecocq, T., and Soubestre, J.: Assessing similarity in continuous seismic cross-correlation functions using hierarchical clustering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19994, https://doi.org/10.5194/egusphere-egu24-19994, 2024.

Understanding the nature of the Mantle Transition Zone (MTZ) can foster our knowledge about the dynamics of the Earth, especially related to the vertical heat and mass exchange between the upper and the lower mantle. The MTZ, characterized by seismic-velocity discontinuities at depths of 410 km and 660 km, is conventionally studied using seismic waves emitted by earthquakes. However, this approach suffers from a typically uneven distribution of earthquakes, biases in earthquake location, and the complexity of earthquake processes.

In this study, we used body waves retrieved from ambient noise correlations to map the mantle transition zone beneath the US. We analyzed cross-correlation functions from more than 3500 seismic stations, including the EarthScope USArray stations during its deployment time frame between 2004 and 2013. We obtained clear short period (<10 s) P410P and P660P reflection phases by using a stacking strategy that considers global noise wave field data selection. This allows us to image the MTZ at an unprecedented high resolution, providing new constraints that can shed light on the tectonic history and the mantle dynamics in this area.

How to cite: Aiman, Y. A., Lu, Y., and Bokelmann, G.: Mantle Transition Zone (MTZ) beneath the contiguous US revealed by ambient noise cross-correlations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8313, https://doi.org/10.5194/egusphere-egu24-8313, 2024.

In this study, we aim to explore the depth range of stress-aligned anisotropy (SAA) in Taiwan. Our recent works have shown that the near-surface SAA is consistent with shear-wave splitting studies employing local earthquakes. However, it contrasts with SWS studies using deep phase (SKS) and the shallow crustal Vs anisotropy model derived from noise-derived broad-band (4-20 s) Rayleigh waves. This suggests that SAA is likely confined to the uppermost crust. Despite micro-cracks assumed to be fully closed with increasing ambient stress at depths, the depth range of the SAA mechanism remains unclear.

Our approach involves noise tomography using short-period (1-10 s) Rayleigh waves enhanced by the multicomponent stacking technique. To measure the dispersion of the isolated fundamental mode Rayleigh waves accurately and effectively, we employ a modified machine learning algorithm based on the algorithm proposed by Yang et al.(2022) We employ the Recurrent-Residual U-Net (R2U-Net) developed by Liao et al. (2021) for training. The model training data consists of dispersion diagrams from CCFs derived in various regions, including Taiwan, Japan, and the South Island of New Zealand. Approximately six thousand data are included in the training stage.

With the obtained dispersion data, we apply the wavelet-based multi-scale inversion technique to derive 3D models of Vs and Vs anisotropy. In this inversion process, the results from prior studies by Lee et al. (2023) serve as a priori constraint for the uppermost section of the model.

How to cite: Chang, H.-M., Gung, Y., Liao, W., Chen, K.-X., Liao, C.-F., Kuo, B.-Y., Chen, Y.-N., and Lee, E.: Probing the SAA Depth Range using ML-measured short-period dispersion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4644, https://doi.org/10.5194/egusphere-egu24-4644, 2024.

How to cite: Frietsch, M., Forbriger, T., Giunchi, C., Di Giovanni, M., Naticchioni, L., and Rietbrock, A.: Reduction of seismic noise with depth - Characterisation of ambient seismic noise for surface and borehole stations at three candidate sites of the Einstein Telescope, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18648, https://doi.org/10.5194/egusphere-egu24-18648, 2024.

The intermediate-depth earthquakes usually occur at depths between 40-300 km, and are commonly related to deformation along and within the subducting plate. Promising but contrasting mechanisms of their seismic failure are proposed to model their generation and associated deformation processes, including ductile shear instability, dehydration embrittlement and failure of dry rocks. This work exploits one of the best examples worldwide of exposed sources of intermediate-depth earthquakes to better understand their nucleation environment. The study area consists of the Moncuni ultramafic massif (Southern Lanzo Massif, Western Italian Alps), a peridotite and gabbro section considered as a dry remnant of the Tethyan oceanic lithosphere subducted, during the Alpine orogeny, and then exhumed without experiencing ductile deformation and metamorphism. Moncuni geological units are extensively crossed by a network of pseudotachylytes (geological product of seismic slip associated to earthquakes), that locally preserve high-pressure minerals, suggesting an intermediate-depth seismic environment origin.

In this study, we want to better understand the nucleation environment of intermediate-depth earthquakes by peeking into the deeper structure of the ophiolitic peridotite and gabbro of the Moncuni area. We are performing a Nodal Ambient Noise Tomography (NANT), which allows crustal imaging based on the measurement of short-period surface wave dispersion curves between pairs of seismic stations. The used dataset was acquired by installing a temporary seismic network with 197 three-component nodal geophones over 250 km² area surronunding the Moncuni massif and operating for about one month.

To perform the ambient noise data processing, we followed the procedure of Bensen et al. (2007). Before using the data, we accomplished a careful data quality analysis by checking the possible occurrence of some perturbations, monitoring several parameters like recording time, and sensor absolute position and stability. We also computed power spectral density curves for each node to investigate the occurrence of anthropogenic noise, and to select the optimal frequency band to use for the NANT. NANT is being performed extracting the empirical Green's functions (EGFs) cross-correlating time series of noise recorded at pairs of stations, so using the frequency-time analysis (FTAN) as proposed in Bensen et al., (2007), we have produced a huge amount of dispersion curves, and we applied a machine learning approach, deep convolutional neural networks, to perform automatic picking and to attribute a quality picking score. We evaluate as reliable picks with a score > 0.7. Conversely, the picks with a score < 0.7 were checked and manually corrected. The dispersion curves will be used to construct a shear-wave velocity model of the study area, allowing us to obtain a detailed image of the deep structure of the Moncuni massif with the goal of understanding whether these earthquakes originate in presence of fluids or in dry oceanic slab.

How to cite: Raggiunti, M., Stumpp, D., Baccheschi, P., Villani, F., Lupi, M., Savard, G., Sapia, V., Pastori, M., Smedile, A., Sciarra, A., Lovati, S., Massa, M., Antonioli, A., Roselli, P., and Tonini, R.: Three-dimensional deep crustal structure of the Moncuni ultramafic massif (Western Italian Alps) imaged by ambient noise tomography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11436, https://doi.org/10.5194/egusphere-egu24-11436, 2024.

How to cite: Nanni, U., Roux, P., and Gimbert, F.: Mapping glacier structure in inaccessible areas from turning seismic sources into a dense seismic array, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8272, https://doi.org/10.5194/egusphere-egu24-8272, 2024.

Microseisms are the continuous oscillations of the earth originated from the interaction of ocean waves with the solid earth. It is divided into two types, primary microseism and secondary microseism. Primary microseisms are generated by the interaction of ocean waves in the shallow coastal part and secondary microseisms are generated due to the interaction of two waves traveling opposite towards each other in the deeper or shallower part. Secondary microseism is also known as double frequency microseism band which is divided into two parts, long period double frequency microseism band and short period double frequency microseism band.

We have taken data from IRIS DMC for ten seismic stations of the year 2018. Our study area is on the Indian Ocean. Indian ocean is considered as one of the global sources of microseism noise. In comparison to the North Indian Ocean, the Southern Ocean generates very strong amplitudes of microseism noise. Storm activity of Southern Ocean is very hazardous and destructive. In addition to that, the Antarctic circumpolar current brings warm water to the Ocean. Therefore, every year it experiences multiple cyclones which play a major role in the generation of microseism noise.

In this study, we are using the frequency-dependent polarization analysis method. Our aim is to understand the spatial variation of noise and their possible sources. Power spectral density (PSD) is calculated using the spectral covariance matrix. Diagonal elements of the matrix represent the power spectra of each component (EW, NS, and Z). For analysing the spatial variation of PSD, we have used the vertical component (Z). We have observed higher PSD in the stations that are present close to the Southern Ocean and comparatively lower amplitudes are observed in the stations far away from the Southern Ocean. Back azimuth is used to determine the dominant source direction of the noise. From our results, major source direction of noise is from the Southern Ocean while minor sources are from Bay of Bengal and Arabian Sea. Clear seasonal variation in the source direction is not observed but seasonal variation in the number of polarized signals is observed indicating maximum polarized signal in the winter season and minimum polarized signals in the summer season. Combined results of spatial variation of PSD and back azimuth analysis help us to better understand the noise sources.  

How to cite: Pradhan, G., Reddy, R., and Singha Roy, P. N.: Delineation of microseism noise sources in the Indian Ocean., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-586, https://doi.org/10.5194/egusphere-egu24-586, 2024.