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The potential of the island of La Palma (Canary Islands) to host geothermal resources is very high, mainly due to its high volcanic activity. The primary goal of this study is to get a tridimensional image of the seismic intrinsic attenuation using ambient seismic noise and to identify anomalies that may be linked to active geothermal reservoirs on La Palma island. For this purpose, we developed a new Ambient Noise Attenuation Tomography (ANAT) technique, which uses seismic ambient noise for imaging intrinsic attenuation in 3-D at a local scale down to 5 km depth. Our research identifies two areas with high attenuation in the island's southern region. One area could be associated with hydrothermal alteration zones beneath the Cumbre Vieja volcanic complex. Another high-attenuation zone was observed in the island's southern part, which could be associated with extensively fractured rocks that might facilitate the circulation of heated fluids. Furthermore, we discuss the geothermal relevance of such anomalies, making a comparison with previous resistivity, S-wave velocity and density models. This study confirms that intrinsic attenuation retrieved from the coda of Rayleigh waves is more sensitive to the presence of fluids than velocity. Fluids being a key component of active geothermal reservoirs, it is reasonable to expect intrinsic attenuation anomalies in these systems. Therefore, we conclude that ANAT can be useful for geothermal exploration. [ABSTRACT FROM AUTHOR]

Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces the reliability of interpretations. In recent years, deep learning methodologies have shown promising results in performing noise attenuation tasks on seismic data. In this research, we propose modifications to the standard U-Net structure by integrating dense and residual connections, which serve as the foundation of our approach named the dense and residual (DARE U-Net) network. Dense connections enhance the receptive field and ensure that information from different scales is considered during the denoising process. Our model implements local residual connections between layers within the encoder, which allows earlier layers to directly connect with deep layers. This promotes the flow of information, allowing the network to utilize filtered and unfiltered input. The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. We evaluate this adapted architecture by applying synthetic and real data sets and calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The effectiveness of this method is well noted. [ABSTRACT FROM AUTHOR]

The recently developed Frequency-Bessel (F-J) transform array technique has significantly advanced multi-mode surface wave tomography, providing precise constraints on shear wave velocity (VS) models of the crust and uppermost mantle. This technique has primarily focused on extracting inter-station surface waves from ambient noise cross-correlation. Energetic typhoons generate abundant surface wave signals, prompting growing interest in utilizing these signals to investigate subsurface structures. This study explores the feasibility and application of using microseisms excited by strong typhoons for F-J transform surface wave tomography. By analyzing microseisms recorded by a broadband seismic array in Mongolia during energetic typhoons, we observed multi-mode surface waves with high signal-to-noise ratios. Our analysis revealed that the sources of these waves closely correspond to typhoon tracks, confirming that they were primarily excited by typhoons. We successfully extracted multi-mode dispersion curves for imaging the VS structure beneath the array using the F-J transform. Additionally, we derived multi-mode dispersion curves from a two-year noise dataset recorded by the same array, which were also used to invert for the VS structure. Comparison of VS models derived from typhoon-induced microseisms and the two-year noise dataset showed negligible differences within the depth range of 0–140 km. Our findings demonstrate the potential value of microseisms generated by energetic typhoons in precisely probing the VS structure of the crust and uppermost mantle. [ABSTRACT FROM AUTHOR]

We present a method to jointly invert surface wave dispersion data and gravity measurements for 3-D shear wave velocity and density models. We implemented a petrophysical approach to combine the kernels of both methodologies in a single process. The synthetic experiments show that jointly inverted models recover shear wave velocity and density better than separate inversions. In particular, density models benefit from the good vertical resolution of surface wave dispersion data, while shear velocity models benefit from the good lateral resolution of gravity data. We also proposed two methods to stabilize the solution when using high-grade polynomials. We applied the methodology to the Los Humeros Geothermal area to demonstrate its applicability in a complex geological scenario. Compared with separate inversion, the joint inversion contributes to enhancing key aspects of the geothermal system by (i) delimitating better the geometry of the caldera deposits in the first 0–2.8 km deep by increasing the vertical resolution in density, (ii) delimitating better the lateral borders of low- Vs bodies at different depths interpreted as a part of a complex magmatic chamber system and (iii) estimating the local shear wave velocity–density relationship that conforms to other known relationships for sedimentary and igneous rocks but with some differences that bring us additional information. [ABSTRACT FROM AUTHOR]

Monitoring seismic velocity changes obtained from ambient noise correlations is widely used to understand changes in rock properties in response to earthquakes, volcanic activities and environmental changes. Since continuous seismic data have been accumulated, this method can estimate long-term changes in seismic velocity, such as crustal recovery after a major earthquake and temporal variations in seismic velocity related to long-term environmental change. Changes in seismic velocity can be estimated with a high temporal resolution by measuring the phase differences of ambient noise correlations based on a seismic interferometry method. Still, these phase differences are influenced not only by seismic wave velocity changes but also by errors in clock timing in seismometers. The clock drift occurs due to out-of-synchronization with the GPS clock and the drift of the internal clock. Therefore, to accurately monitor temporal changes in crustal structure by measuring the phase differences of noise correlations, it is crucial to evaluate the contribution of errors in clock timing to the phase differences. Recently, a method using an extended Kalman filter based on a state-space model was developed for reliable detection of temporal changes in the waveforms of ambient noise correlations, with the state-space model offering the advantage of flexible modelling of time-series data. In this study, we incorporated the time-shifts caused by clock time errors of the seismometer into the state-space model of the temporal changes in ambient noise correlations. We estimated seismic velocity changes, amplitude changes of noise correlations and clock time errors from 2010 April to 2021 September at seismic stations around the Shinmoe-dake volcano in Japan, which experienced eruptions in 2011 and 2018, respectively. Several stations exhibited clear clock time offsets, and the occurrence of clock time-shifts coincided with the dates when the data logger was turned off for seismic station maintenance or replacement of the seismometer. The proposed method provides stable estimations with respect to the signal-to-noise ratio of the waveform, and this stable estimation facilitates accurate timing of seismic recordings, enabling precise analysis of seismic phase arrival times. [ABSTRACT FROM AUTHOR]

Oceanic transform faults (OTFs) facilitate hydrothermal circulation which can modify the fault zone materials and affect their rheological evolution. However, the depth extent and variability of fluid infiltration, degree of mineral alteration and their relationship with earthquake behaviour has only been characterized along a few OTFs globally. Here, we use first-overtone Rayleigh waves extracted from seismic ambient noise to estimate the shear-wave velocity structure beneath the Blanco Transform Fault Zone (BTFZ). Compared to the adjoining normal oceanic plates, relatively variable and slow velocities reduced by at least |$\sim$| 0.2–0.4 km s−1 (⁠|$\sim$| 4–8 per cent) are observed from the crust down to |$\sim$| 22 km depth along some segments of the BTFZ. The crustal slow velocities can be explained by enhanced fluid-filled porosity of |$\sim$| 0.4–10.9 per cent caused by intense fracturing associated with abundant seismicity. Slow uppermost mantle velocities are predominantly consistent with |$\sim$| 1.2–37 per cent serpentinization and |$\sim$| 9 per cent hydration, indicating variable and deep fluid infiltration that exceeds 15 km depth. For instance, shear-wave velocities (⁠|$\sim$| 4.3–4.4 km s−1) in the uppermost mantle beneath the Blanco Ridge suggest extensive serpentinization (⁠|$\sim$| 13–25 per cent), which might explain the recently documented earthquake swarms linked with aseismic creep. In comparison, within the vicinity of the ridge-transform intersections at depths |$\sim$| |$\gt $| 16 km, low velocities (⁠|$\sim$| 4.1–4.2 km s−1) that are consistent with the presence of up to |$\sim$| 1.6 per cent partial melt suggest intratransform magmatism which would contradict the long-held simple conservative strike-slip characterization of OTFs. [ABSTRACT FROM AUTHOR]

3-D shear wave velocity and density models are important for understanding the structures, material composition, tectonic deformations and dynamical mechanisms of the Earth's crust. Such models are usually based on surface wave tomography and gravity inversion, which provide high resolution in the vertical and horizontal directions, respectively. The joint inversion of surface wave and gravity methods can promote the mutual constraints and complementary advantages of seismic and gravity information, improving the imaging resolution and reducing the uncertainty in the individual methods. However, the traditional joint inversion methods directly construct only the shear wave velocity model, excluding density models. We present a joint inversion method of surface wave and gravity data that simultaneously constructs both crustal shear wave velocity and density models. Unlike the previous studies, we reconfigure the seismic kernels of surface wave tomography to preserve the seismic kernel of density. Moreover, the gravity kernel of density is combined with the seismic kernels to establish an objective function of simultaneous joint inversion. Consequently, the imaging resolution of density structure is improved. Our method is validated on the northeastern Tibetan plateau. The inversion results show that the Dingxi, Jiuzhaigou and Jishishan earthquakes occurred in the high–low-anomaly transition zones of shear wave velocity or density, implying that they were induced by accumulation of strain energy in the upper crust of the northeastern Tibetan plateau when the surrounding harder blocks extruded during the tectonic deformation process. [ABSTRACT FROM AUTHOR]

The Pannonian Basin, situated in Central Europe, is surrounded by the Alpine, Carpathian and Dinaric orogens. To understand its tectonic characteristics and evolution, we determine a shear wave velocity model of its crust, mantle lithosphere and asthenosphere consistently by jointly inverting Rayleigh wave phase velocities measured consistently from earthquake (EQ) and ambient noise (AN) data. For the AN data, continuous waveform data were collected from 1254 stations, covering an area within 9° from the centre of the Pannonian Basin during the time period from 2006 to 2018. This data set enabled the extraction of over 164 464 interstation Rayleigh phase-velocity curves, after applying a strict quality control workflow. For the EQ data set more than 2000 seismic events and about 1350 seismic stations were used in the broader Central and Eastern European region between the time-span of 1990 to 2015, allowing us to extract 139 987 quality controlled Rayleigh wave phase-velocity curve. Using the combined data set, a small period- and distance-dependent bias between ambient noise and earthquake measurements, mostly below 1 per cent but becoming larger towards longer periods has been found. After applying a period and distance dependent correction, we generated phase-velocity maps, spanning periods from 5 to 250 s. 33 981 local dispersion curves were extracted and a new approach is introduced to link their period-dependent roughness to the standard deviation. Using a non-linear stochastic particle swarm optimization, a consistent 3-D shear wave velocity model (PanREA2023) encompassing the crust and upper mantle down to 300 km depth was obtained with a lateral resolution reaching about 50 km at the centre of the study area for shorter periods. The crust beneath the Carpathian orogen exhibits a distinct low-velocity anomaly extending down to the Moho. It is referred to as Peri-Carpathian anomaly. Similar anomalies were observed in the Northern Apennines, while the Eastern Alps and Dinarides, as collisional orogens, generally demonstrate higher velocities in the upper crust. High crustal shear wave velocities are also evident in the Bohemian Massif and the East European Craton. The brittle upper crust of the Pannonian Basin is characterized by alternating NE–SW trending high- and low-velocity anomalies: the western and central Pannonian low-velocity anomalies and the Transdanubian and Apuseni high-velocity anomalies related to Miocene sedimentary basins and intervening intervening interbasinal highs exposing Pre-Cenozoic rocks including crystalline basement rocks. Beneath the Southeastern Carpathians, a NE-dipping slab was identified, extending to depths of at least 200 km, while a slab gap is evident beneath the Western Carpathians. A short south-dipping Eurasian slab was imaged beneath the Eastern Alps down to only 150–200 km depth. The Adriatic lithosphere is subducting near-vertically dipping beneath the Northern Apennines, and a slab gap was observed beneath the Central Apennines. In the Northern Dinarides, a short slab was evident, reaching depths of around 150 km. The Southern Dinarides featured a thinned but possibly incompletely detached slab. [ABSTRACT FROM AUTHOR]

The complete catalogue of moment tensor (MT) solutions is essential for a wide range of research in solid earth science. However, the number of reliable MT solutions for small to moderate earthquakes (3.0 ≤ M  ≤ 5.5) is limited due to uncertainties arising from data and theoretical errors. In this study, we develop a new procedure to enhance the resolvability of MT solutions and provide more reliable uncertainty estimates for these smaller to moderate earthquakes. This procedure is fully automatic and efficiently accounts for both data and theoretical errors through two sets of hybrid linear–non-linear Bayesian inversions. In the inversion process, the covariance matrix is estimated using an empirical approach: the data covariance matrix is derived from the pre-event noise and the theoretical covariance matrix is derived from the residuals of the initial solution. We conducted tests using synthetic data generated from the 3-D velocity model and interference from background seismic noise. The tests found that using a combination of the non-Toeplitz data covariance matrix and the Toeplitz theoretical covariance matrix improves the solution and its uncertainties. Test results also suggest that including a theoretical covariance matrix when analysing MT in complex tectonic regions is essential, even if we have the best 1D velocity model. The application to earthquakes in the northern region of the Banda Arc resulted in the first published Regional Moment Tensor (RMT) catalogue, containing more than three times the number of trusted solutions compared to the Global Centroid Moment Tensor (GCMT) and the Indonesian Agency for Meteorology Climatology and Geophysics Moment Tensor (BMKG-MT) catalogue. The comparison shows that the trusted solutions align well with the focal mechanism of the GCMT and BMKG-MT, as well as with the maximum horizontal stress of the World Stress Map, and tectonic conditions in the study area. The newly obtained focal mechanisms provide several key findings: (i) they confirm that the deformation in the northern and eastern parts of Seram Island is influenced by oblique intraplate convergence rather than by the subduction process; (ii) they validate the newly identified Amahai Fault with a greater number of focal mechanisms and (iii) they reveal an earthquake M w 4.7 with the same location and source mechanism 6 yr before the 2019 Ambon-Kairatu earthquake (M w 6.5) which occurred on a previously unidentified fault. [ABSTRACT FROM AUTHOR]

Neural networks have been used in various computer vision applications, including noise removal. However, removing seismic noise via deep learning approaches faces a specific issue: the scarcity of labeled data. To address this difficulty, this work introduces an adaptation of the Noise2Self algorithm featuring a one-shot learning approach tailored for the seismic context. Essentially, the method leverages a single noisy image for training, utilizing a context-centered masking system and convolutional neural network (CNN) architectures, thus eliminating the dependence on previously labeled data. In tests with Gaussian noise, the method was competitive with established approaches such as Noise2Noise. Under real noise conditions, it demonstrated effective noise suppression removal for a smaller architecture. Therefore, our proposed method is a robust alternative for noise removal that is especially valuable in scenarios lacking sufficient data and labels. With a new approach to processing seismic images, particularly in terms of denoising, our method contributes to the ongoing evolution and enhancement of techniques in this field. [ABSTRACT FROM AUTHOR]

Changes in the surrounding environment, if transmitted to the electron microscope, are frequently perceived as noise that diminishes the quality of the images. However, in fact, ‘noises’ contain rich information about the environment. This work reports a very rare event where aberration‐corrected HAADF‐STEM images were acquired during the impact of seismic waves, resulted from a mild earthquake. By analysing these images, we found that the drift and vibration of the sample are detectable and quantifiable. Despite many potential challenges, this work demonstrates the utilisation of electron microscopes in detecting and monitoring seismic waves with high spatial resolution, which may lead to unique applications in the low‐frequency regime. [ABSTRACT FROM AUTHOR]

In the past two decades or so, ambient noise tomography (ANT) has emerged as a powerful tool for investigating high-resolution crustal and upper-mantle structures. A crucial step in the ANT involves extracting phase velocities from cross-correlation functions (CCFs). However, obtaining precise phase velocities can be a formidable challenge, particularly when significant lateral velocity variations exist in shallow subsurface imaging that relies on short-period surface waves from ambient noise. To address this challenge, we propose an unwrapping correction method that enables the accurate extraction of short-period dispersion curves. Our method relies on the examination of the continuity of phase velocities extracted from CCFs between a common station and other neighbouring stations along a linear array. We demonstrate the effectiveness of our approach by applying our method to both synthetic and field data. Both applications suggest our unwrapping correction method can identify and correct unwrapping errors in phase velocity measurements, ensuring the extraction of accurate and reliable dispersion curves at short periods from ambient noise, which is essential for subsequent inversion for subsurface structures. [ABSTRACT FROM AUTHOR]

Taiwan, one of the most active orogenic belts in the world, undergoes orogenic processes that can be elucidated by the doubly vergent wedge model, explaining the extensive island-wide geological deformation. To provide a clearer depiction of its cross-island orogenic architecture, we apply ambient noise tomography across an east–west linear seismic array in central Taiwan, constructing the first high-resolution 2-D shear velocity model of the upper crust in the region. We observe robust fundamental- and higher-mode Rayleigh waves, with the latter being mainly present in the western Coastal Plain. We develop a multimode double-beamforming method to determine local phase velocities across the array between 2- and 5-s periods. For each location, we jointly invert all available fundamental- and higher-mode phase velocities using a Bayesian-based inversion method to obtain a 1-D model. All 1-D models are then combined to form a final 2-D model from the surface to ∼10 km depth. Our newly developed 2-D model clearly delineates major structural boundaries and fault geometries across central Taiwan, thereby corroborating the previously proposed pro-wedge and retro-wedge models while offering insight into regional seismic hazards. [ABSTRACT FROM AUTHOR]

This study focuses on improving the seafloor compliance noise removal method, which relies on estimates of the compliance transfer function frequency response (the deformation of the seafloor under long-period pressure waves). We first propose a new multiscale deviation analysis of broad-band ocean–bottom seismometer data to evaluate stationarity properties that are key to the subsequent analysis. We then propose a new approach to removing the compliance noise from the vertical channel data, by stacking daily estimated transfer function frequency responses over a period of time. We also propose an automated transient event detection and data selection method based on a cross-correlation criterion. As an example, we apply the method to data from the Cascadia Initiative (network 7D2011). We find that the spectral extent of long-period forcing waves varies significantly over time so that standard daily transfer function calculation techniques poorly estimate the transfer function frequency response at the lowest frequencies, resulting in poor denoising performance. The proposed method more accurately removes noise at these lower frequencies, especially where coherence is low, reducing the mean deviation of the signal in our test case by 27 per cent or more. We also show that our calculated transfer functions can be transferred across time periods. The method should allow better estimates of seafloor compliance and help to remove compliance noise at stations with low pressure-acceleration coherence. Our results can be reproduced using the BRUIT-FM Python toolbox, available at https://gitlab.ifremer.fr/anr-bruitfm/bruit-fm-toolbox. [ABSTRACT FROM AUTHOR]

Cross-correlations of seismic ambient noise are frequently used to image Earth structure. Usually, tomographic studies assume that noise sources are uniformly distributed and interpret noise correlations as empirical Green's functions. However, previous research suggests that this assumption can introduce errors in the estimated models, especially when noise correlation waveforms are inverted. In this paper, we investigate changes in subsurface models inferred from noise correlation waveforms depending on whether the noise source distribution is considered to be uniform. To this end, we set up numerical experiments that mimic a tomographic study in Southern California exploiting ambient noise generated in the Pacific Ocean. Our results show that if the distribution of noise sources is deemed uniform instead of being numerically represented in the wave simulations, the misfit of the estimated models increases. In our experiments, the model misfit increase ranges between 5 and 21 per cent, depending on the heterogeneity of the noise source distribution. This indicates that assuming uniform noise sources introduces source-dependent model errors. Since the location of noise sources may change over time, these errors are also time-dependent. In order to mitigate these errors, it is necessary to account for the noise source distribution. The spatial extent to which noise sources must be considered depends on the propagation distance of the ambient noise wavefield. If only sources near the study area are considered, model errors may arise. [ABSTRACT FROM AUTHOR]

We present the results of seismic profiling aimed to characterize the structure of shallow formation in the Geoncheon Valley (GCV), an area we interpreted as the north tip of the Miryang Fault This profile was deployed in the GCV, near the City of Gyeongju, Republic of Korea. The Miryang Fault is one of the major faults of the Yangsan Fault System in the Cretaceous Gyeongsang Basin, the southeastern Korean Peninsula. Despite local earthquake records showing scattered micro-seismicity along the trace of Miryang Fault, other geophysical and geological information is rare. The location of the GCV is tectonically critical: it is on the major stratigraphic boundary in the Cretaceous Gyeongsang Basin: the Hayang Group (dominantly sedimentary rocks) to the north and Yucheon Group (volcanic and volcanoclastic rocks) to the south. The surface expression of the Miryang Fault in this area is difficult to define by geomorphology since the surface is covered by highly altered cultivated farmlands. We tackle this problem by conducting seismic profiling. On a 1,200-m long linear profile, we deployed a dense array using 3-component seismic sensors and acquired both passive and active seismic data. The passive seismic data acquisition lasted about 12 days. At the end of the deployment period directly before withdrawing the array, an active survey using a sledgehammer source was also conducted. The integrated seismic cross-section displays significant segmentation in the texture of the seismic image, the sediment-bedrock interface is between less than 10 meters to slightly greater than 20 meters with a general trend of dipping to the southeast. The preliminary seismic results suggest that it is likely the Miryang Fault terminates inside the GCV, and the type of tip damage zone appears to be a group of horsetail splays. [ABSTRACT FROM AUTHOR]

We numerically investigate the mechanisms that resulted in induced seismicity occurrence associated withCO2 injection at the Illinois Basin-Decatur Project (IBDP). We build a geologically consistent model that honors key stratigraphic horizons and 3D fault surfaces interpreted using surface seismic data and microseismicity locations. We populate our model with reservoir and geomechanical properties estimated using well-log and core data. We then performed coupled multiphase flow and geomechanics modeling to investigate the impact of CO2 injection on fault stability using the Coulomb failure criteria. We calibrate our flow model using measured reservoir pressure during the CO2 injection phase. Our model results show that pore-pressure diffusion along faults connecting the injection interval to the basement is essential to explain the destabilization of the regions where microseismicity occurred, and that poroelastic stresses alone would result in stabilization of those regions. Slip tendency analysis indicates that, due to their orientations with respect to the maximum horizontal stress direction, the faults where the microseismicity occurred were very close to failure prior to injection. These model results highlight the importance of accurate subsurface fault characterization for CO2 sequestration operations. [ABSTRACT FROM AUTHOR]

Ambient noise interferometry has become a common technique for monitoring slight changes in seismic velocity in a variety of contexts. However, the physical origin of the resolved small velocity fluctuations is not well established for long‐term seasonal effects. Here we propose a physical forward model of scattered waves in a deformable medium that includes acousto‐elastic effect, which refers to non‐linear elasticity with third‐order elastic constants. The model shows that small pressure perturbations of a few kPa due to seasonal variations in the water table can induce seismic velocity changes compatible with those measured at the surface by ambient noise interferometry. The results are consistent with field observations near the deep geothermal site of Rittershoffen (France). They illustrate the capability in modeling the diffuse wavefield from scattering synthetic waves to reproduce ambient noise signals for monitoring environmental and/or deep reservoir signals. Plain Language Summary: Monitoring the fine evolution of the Earth's crust either prior to catastrophic events such as earthquakes or landslides, or georesource exploitation, is an important objective for risk management. Ambient noise interferometry is one of the emerging tools for assessing the minute evolution of seismic velocities in the subsurface. However, the physical origin of the observed small velocity changes is not well established. Here we propose a physical model of scattered waves in a deformable medium that includes non‐linear elastic effects, which are not conventionally considered. The model shows that small pressure perturbations of a few kPa due to seasonal variations in the water table can induce seismic velocity changes compatible with those measured at the surface by ambient noise interferometry. The results are consistent with field observations near the deep geothermal site of Rittershoffen (France). They illustrate the capability in modeling scattered synthetic waves to reproduce ambient noise signals. Key Points: We develop an acousto‐elastic model for seismic wave scattering in a deforming layered subsurfaceStress fluctuations induced by seasonal water table variations are shown to be responsible for significant changes in seismic velocitiesThe model is providing an interpretation tool for environmental monitoring signals obtained from ambient seismic noise [ABSTRACT FROM AUTHOR]

High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region. [ABSTRACT FROM AUTHOR]

High‐resolution seismic model is crucial for advancing our understandings on geological processes and enhancing seismic hazard mitigation programs. We construct a high‐resolution China Seismological Reference Model (CSRM‐1.0) in the top 100 km of the crust and uppermost mantle in continental China following a top‐down construction process. The employed seismic constraints include P‐wave polarization angle from tele‐seismic event, short‐period Rayleigh wave ellipticity from ambient noise, long‐period Rayleigh wave ellipticity from earthquake data, receiver function, empirical Green's function from ambient noise, Rayleigh wave phase/group velocity dispersion curves from regional earthquakes, and Pn‐wave travel time extracted from seismic data of 4,435 stations. CSRM‐1.0 has a spatial crustal resolution of ∼60 km beneath the north‐south seismic belt and trans‐North China orogen regions and ∼120 km beneath the rest of continental China, and a spatial mantle resolution of ∼300 km. CSRM‐1.0 exhibits prominent velocity heterogeneities in the crust and uppermost mantle and an eastward thinning of the crust, geographically correlating with geological settings. CSRM‐1.0 improvements include accurate estimation of shallow seismic structure, increased spatial resolution and improved model accuracy. Crustal composition inferred from CSRM‐1.0 exhibits a general transition from a felsic upper crust to a mafic lower crust. Mafic rocks in the lower crust are found predominantly along inter‐block boundaries and sporadically within the interiors of blocks, likely resulted from preferential inter‐block intrusions of magmas related to various oceanic plate subductions and the Emeishan mantle plume. This study contributes seismic constraints and CSRM‐1.0 to the CSRM product center (http://chinageorefmodel.org) as a backbone open‐access geophysical cyberinfrastructure. Plain Language Summary: High‐resolution seismic imaging of the crust and uppermost mantle offers crucial insights into regional geological histories and enhances seismic and volcanic hazard mitigation programs. We construct a high‐resolution China Seismological Reference Model (CSRM‐1.0) of the crust and mantle beneath continental China with various types of seismic constraints extracted from seismic data recorded by 4,435 stations deployed across continental China since 1990. CSRM‐1.0 has a spatial resolution of ∼60–120 km in the crust and 300 km in the uppermost mantle. CSRM‐1.0 improvements include accurate estimation of shallow seismic structure, increased spatial resolution and improved model accuracy. Crustal composition inferred from CSRM‐1.0 exhibits a general transition from a felsic upper crust to a mafic lower crust. Mafic rocks in the lower crust are found predominantly along inter‐block boundaries and sporadically within the interiors of blocks, likely resulted from preferential inter‐block intrusions of magmas related to various oceanic plate subductions and the Emeishan mantle plume. This study contributes seismic constraints and CSRM‐1.0 to the CSRM product center (http://chinageorefmodel.org) as a backbone open‐access geophysical cyberinfrastructure. Key Points: CSRM‐1.0 is constructed with various seismic constraints from data of 4,435 stations using a self‐consistent top‐down construction processCSRM‐1.0 improvements include accurate estimation of shallow seismic structure, increased spatial resolution, and enhanced model accuracyCSRM‐1.0 reveals major crustal mafic rocks related to the past subductions, an Emeishan mantle plume and geological inter‐block boundaries [ABSTRACT FROM AUTHOR]

SUMMARY: For seismographic stations with short acquisition duration, the signal-to-noise ratios (SNRs) of ambient noise cross-correlation functions (CCFs) are typically low, preventing us from accurately measuring surface wave dispersion curves or waveform characteristics. In addition, with noisy CCFs, it is difficult to extract relatively weak signals such as body waves. In this study, we propose to use local attributes to improve the SNRs of ambient noise CCFs, which allows us to enhance the quality of CCFs for stations with limited acquisition duration. Two local attributes: local cross-correlation and local similarity, are used in this study. The local cross-correlation allows us to extend the dimensionality of daily CCFs with computational costs similar to global cross-correlation. Taking advantage of this extended dimensionality, the local similarity is then used to measure non-stationary similarity between the extended daily CCFs with a reference trace, which enables us to design better stacking weights to enhance coherent features and attenuate incoherent background noises. Ambient noise recorded by several broad-band stations from the USArray in North Texas and Oklahoma, the Superior Province Rifting EarthScope Experiment in Minnesota and Wisconsin and a high-frequency nodal array deployed in the northern Los Angeles basin are used to demonstrate the performance of the proposed approach for improving the SNR of CCFs. [ABSTRACT FROM AUTHOR]

The use of the HVSR (Horizontal-to-Vertical Spectral Ratio) method on single-station microtremor measurements is well documented in small alluvial plains for bedrock mapping. In large sedimentary basins, like the Po Plain, its application is still debated. To shed some light on this issue, we investigated two seismogenic structures buried below the Po Plain Quaternary deposits: the Mirandola and Casaglia anticlines. We acquired and analysed a dense distribution of HVSR data covering the two areas and mapped the frequency and amplitude values of the observed resonance peaks. The top of both anticlines is highlighted by high amplitude peaks picturing E-W elongated sectors with high-impedance contrast, where Quaternary deposits are reduced in thickness to about 60–130 m and directly overlay the Pliocene (Mirandola) and Miocene (Casaglia) marine units. In Mirandola, the high-amplitude peaks also correspond to higher resonance frequencies, while in Casaglia, the distribution of resonance frequencies is relatively uniform suggesting a flatter crestal region and the lateral continuity of the resonance surface. The combination of peak frequency and amplitude information on a dense grid of measurement points is thus confirmed to be useful for identifying and mapping buried geological structures such as structural highs. Further modelling is being carried out to estimate the depth of the surface responsible for the observed resonances, through calibration with borehole information. [ABSTRACT FROM AUTHOR]

In recent years, self‐supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labelled data in the training stage, an unobtainable requirement for seismic data. However, current self‐supervised methods usually suppress simple noise types, such as random and trace‐wise noise, instead of the complicated, aliased ground roll. Here, we propose an adaptation of a self‐supervised procedure, namely, blind‐fan networks, to remove aliased ground roll within seismic shot gathers without any requirement for clean data. The self‐supervised denoising procedure is implemented by designing a noise mask with a predefined direction to avoid the coherency of the ground roll being learned by the network while predicting one pixel's value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate aliased ground roll. [ABSTRACT FROM AUTHOR]

The Pannonian-Carpathian-Alpine Seismic Experiment (PACASE) is a collaborative project based on a large, passive seismic network comprising 214 temporary stations. Among the primary aims are the imaging of the Earth's crustal, lithospheric and upper mantle structure, including joint inversions; monitoring and mapping of seismic activity; and interpretation of the data from seismotectonic and geodynamic perspectives. The base of the cooperation is a high-quality, broadband seismic network covering the very centre of Europe: the Eastern Alpine and Western Carpathian Mountain ranges, the Bohemian Massif, and the sedimentary Molasse and Pannonian Basins. In this overview, we focus on the implementation and achieved field goals of PACASE, such as seismic station configuration, general network organization, data availability and access to the dataset. With selected seismological examples, we demonstrate the good usability of the records of earthquake detection, and a first publication attests to the structural imaging capability of the PACASE data. We assess the background noise level at various stations and its variations in time and space. Our aim is to collect all practical information relevant to serve as a long-term reference for the PACASE. [ABSTRACT FROM AUTHOR]

Ambient seismic noise has proven to be a particularly effective tool for subsurface imaging in the last decades, with applications ranging from near surface imaging, to crustal or upper mantle tomography. Fundamentally, it relies on the cross correlations of continuous recordings of ground motion data at pairs of seismic stations. Processing steps have become more refined and promising in extracting meaningful signals that can further be used in a range of classic seismology tools. The processing, however, is usually cumbersome, time- and memory-consuming, as it requires years of continuous noise recorded at broadband seismic stations, to obtain high signal-to-noise ratio empirical Green's functions. In order to ease the research effort, we built a database of ambient seismic noise cross correlations between pairs of broadband seismic stations that operated in Central and Eastern Europe between 1999 and 2020. The database is part of the Eastern European Ambient Seismic Noise (EENSANE) research project hosted by the National Institute of Earth Physics in Romania and will progressively grow as more stations become available, such as those provided by the new AdriaArray network. Based on this database and using state-of-the-art inversion techniques, we developed a series of near-surface and crustal tomography models of the Eastern European craton, the Trans-European Suture Zone and younger accreted terranes from Central Europe. Our integrated models provide both isotropic and azimuthally anisotropic seismic velocities from surface wave dispersion and attenuation parameters from the decay envelopes of Rayleigh waves. Using horizontal-to-vertical ratios of ambient noise, we also recovered the fundamental frequency of resonance and near surface shear wave velocity models beneath stations located across the Carpathian Orogen. Based on cross-correlation functions, we were also able to retrieve the seismic wavefield and peak ground displacement amplitudes from past earthquakes decades after their occurrence, offering a chance for improving seismic hazard and risk models in seismically vulnerable and developing regions of Europe. Our multidisciplinary results prove the versatility of ambient noise uses and the importance of the EENSANE database for a wide range of seismological imaging and hazard applications. [ABSTRACT FROM AUTHOR]

The April 2010 earthquake (Mw = 7.2), which occurred about 40 km to the southeast of the city of Mexicali, Mexico, caused significant damage to buildings. To improve knowledge of the seismic response of the soil due to the occurrence of earthquakes, a response spectrum at 5% damping was calculated. A comparison between the spectral ordinates obtained in this study and the spectra proposed by the regulations of the Federal Electricity Commission (CFE for its acronym in Spanish) in its seismic design for civil works manual, which is currently used as the design standard throughout the country, was made. We calculated response spectra using records from the April 2010 earthquake and a stratigraphic profile of the city to calculate a transfer function. We first corrected the records for site effect due to stations being over sedimentary soil, and then used them as Green functions to perform a numerical simulation of propagation through the stratigraphic profile to obtain a simulated surface record from which response spectra were calculated. Additionally, ambient seismic noise was measured at the same site to get the dominant period (To). We observed that the transfer function was similar to the spectral quotient up to 5 Hz and that To calculated in both ways gave similar values. The comparison suggests that the design spectrum of the CFE regulation can be considered as a representative spectrum for Mexicali for periods greater than 1.3 s, but not for the zone of short periods. [ABSTRACT FROM AUTHOR]

Passive seismic monitoring (PSM) is emerging as a tool for detecting rockfall events and pre‐failure seismicity. In this paper, the potential of PSM for rockfall monitoring is assessed through a case study carried out in Gros‐Morne, Eastern Québec, in a region with prominent roadside cliffs, where more than 500 fallen rocks are found on the main regional road each year. The proposed method relies on using sensitive STA‐LTA windows to detect a very large number of seismic events and build a comprehensive catalog. In total, more than 70,000 seismic events were detected over one year. Gaussian mixtures are used to partition the data set. Based on visual inspection of the data, a main working hypothesis is that the seismic events can be clustered into three groups. After analyzing the spatio‐temporal distribution of the events in each group, we find that the events of one cluster can be associated with anthropogenic activity. The frequency of occurrence of the events of the different clusters and their link with meteorological data is also examined through a regression exercise, to assess the importance of the meteorological variables as explanatory variables. The results allow us to postulate on the physical origins of the signals in the different clusters, attributing them to rockfall activity and wind‐induced seismic noise. Plain Language Summary: This study explores the use of passive seismic monitoring (PSM) as a method to detect and track rockfall events, focusing on an area in Eastern Canada, where more than 500 rockfalls reach the main regional road each year. We utilized an automated seismic event detection algorithm to identify over 70,000 seismic events in a one‐year period. By analyzing these events with machine learning, we discovered patterns that allowed us to group the events into three categories. One of these groups is linked to human activity, while the other two are influenced by natural factors such as rainfall, strong temperature changes, and wind speed. We interpret the origins of the signals in the latter two groups as rock impacts during rockfalls for one, and strong winds hitting the rock cliff for the other. Our research shows that PSM, coupled with machine learning, is a powerful technique for understanding geomorphological dynamics at a small timescale. Key Points: We use feature engineering to automatically analyze seismic signals measured on a cliff with frequent rockfallsClustering proves useful to distinguish signals with different waveforms and frequency contentAnalysis of the relationships between meteorological variables with each cluster enables their physical interpretation, from geomorphological dynamics to anthropic activity [ABSTRACT FROM AUTHOR]

Imaging the detailed structure of sedimentary basins is crucial for natural resource exploration and essential for better analysis and correction of sediment responses when studying deeper interior earth structures using seismic data. In the Tarim Basin, previous studies on the sedimentary structures are mostly obtained by active-source seismic surveys, which can provide high-resolution underground interface information but are highly costly or environmentally unfriendly. In this paper, ambient noise tomography, an efficient and economical method based on background vibration, is employed to construct the sedimentary velocity structure at the southeast margin of the Tarim Basin. Based on ambient noise data collected from a linear dense short-period seismic array, we extract Love wave signals from T–T component cross-correlation functions (CCFs) and measure Love wave dispersion curves at a period band of ∼0.3–11.5 s. Then, we utilize a one-step direct surface wave tomography method to image a fine 2-D sedimentary shear wave velocity structure with a depth reaching 10 km. Our results reveal a clear layered sedimentary structure, the palaeo Tadong uplift and the thrust Cherchen fault. Our study provides reference sedimentary velocity models for the southeastern Tarim Basin, focusing on depths shallower than 10 km. This model is intended to serve as crucial input for studies requiring detailed shallow sedimentary velocity data. Moreover, our research demonstrates that the application of the ambient noise tomography method with dense arrays has great potential for enhancing resource exploration efforts in sedimentary basins. [ABSTRACT FROM AUTHOR]

Himachal Pradesh, a northern state in India, is located in seismic zones IV and V and is affected by several moderate to strong earthquakes. Given the region's susceptibility to seismic activity, it is always necessary to design structures according to the safety standards available in the country. Shear wave velocity (Vs) averaged up to a depth of 30 m is a crucial factor in designing earthquake-resistant structures in seismically active areas. The current study conducted comprehensive seismic exploration to determine in-situ Vs using active and passive methods at various locations covering the ongoing Bhanupalli–Bilaspur–Beri railway project. This facilitated earthquake hazard assessment and Vs30 calculations. In this work, to estimate a one-dimensional Vs profile, we utilised the joint fit of the horizontal-to-vertical spectral ratio (H/V) technique, a passive approach that utilises ambient noise data and an active seismic technique known as multichannel analysis of surface wave (MASW). This joint fit has provided a 1-D velocity structure. The method employed has demonstrated a consistent correspondence with bore log data and notably, there is a strong correlation between variations in Vs and lithology that is dependent on depth. It has been seen that wave velocity tends to rise with depth within similar formations. For quantifying this relationship, several regression models have been formulated and then validated using different sites, which are excluded in the formulation of the regression relation. We derived shear wave profiles for various stations using lithologs and compared them to existing shear wave profiles and we found a strong agreement between these two datasets. [ABSTRACT FROM AUTHOR]

The thick loess sediments produce a lot of noise during seismic acquisition, typically surface wave. Conventional methods are difficult while suppressing 3D surface waves since they are hyperbolic in the far-array. This study proposed a new model-based, data-driven surface wave attenuation method, which uses dispersion curve analysis of surface wave and joint inversion based on genetic algorithm (GA) and conjugate gradient algorithm, to build an accurate surface wave model, and then to subtract the model data from seismic data to improve the signal to noise ratio (S/N ratio). Located in the southern part of the Ordos Basin, the Loess Plateau has the characteristics of large thickness of loess sedimentary layer, diverse geomorphology, complex geological conditions and developed surface waves. After applying the method to the field seismic data, numerical tests show that joint inversion of dispersion waves was stable, accurate, and efficient in multi-type models, including models with a low-velocity layer and with a high-velocity layer. The result showed that the 3D surface wave attenuation method is effective in surface wave suppression. The low-frequency information and the reflected signal are efficiently preserved. Seismic faces are more apparent for reservoir characterization. This method provided credible data for the exploration of the loess tableland area. [ABSTRACT FROM AUTHOR]

Abstract The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments.

There was an evident increase in the number of earthquakes in the Xinfengjiang Reservoir from June to July 2014 after the landing of Typhoon Hagibis. To understand the spatial and temporal evolution of this microseismicity, we built a high-precision earthquake catalog for 2014 and relocated 2275 events using recently developed methods for event picking and catalog construction. Seismicity occurred in the southeastern part of the reservoir, with the preferred fault plane orientation aligned along the Heyuan Fault. The total seismic energy peaked when the typhoon passed through the reservoir, and seismicity correlated with typhoon energy. In contrast, a limited seismic response was observed during the later Typhoon Rammasun. Combining data regarding the water level in the Xinfengjiang Reservoir and seismicity frequency changes in the Taiwan region during these two typhoon events, we suggest that typhoon activity may increase microseism energy by impacting fault stability around the Xinfengjiang Reservoir. Whether a fault can be activated also depends on how close the stress accumulation is to its failure point.

The Eastern Himalayan Syntaxis (EHS) serves as a natural laboratory for the study of intense continental collision and lateral extrusion tectonics. By aiming at the intricate tectonic dynamics south and southeast of the EHS, we integrate seismic data from new broadband stations in central Myanmar with permanent stations in southeastern Tibet to establish a high‐resolution 3‐D shear wave velocity model through ambient noise surface wave tomography. Our imaging results reveal distinct differences in crustal seismic velocity structures between the West Burma Block, Chuan‐Dian Block, and the Shan Plateau, highlighting the extent of oblique subduction and restricted crustal extrusion. Notably, two north‐south oriented low‐velocity zones in the mid‐to‐lower crust of southeastern Tibet are mainly confined within the Chuan‐Dian Block and terminate near the Red River Fault, with limited extension into the Shan Plateau. Plain Language Summary: The Eastern Himalayan Syntaxis (EHS) marks the point where the Indian and Asian plates collide most intensely in the eastern Himalayas. Geoscientists have observed significant mountain‐building activities around the EHS extending into southeastern Tibet (SE Tibet), driven by the intense collision of these continental plates. In SE Tibet, materials are squeezed sideways, a process known as lateral extrusion due to the collision of these plates. This is evident from seismic studies showing slower wave speeds in two distinct regions of the middle to lower crust. We analyzed seismic data from new stations in central Myanmar and permanent stations in SE Tibet to better understand the zone transitioning from intracontinental collision to lateral extrusion. Using a technique called ambient noise tomography, we developed a high‐resolution 3‐D model of the crust and uppermost mantle. Our findings reveal significant differences in the crust's structure between areas of subduction, extrusion, and transition, enhancing our understanding of the geodynamic processes beneath the EHS and its surrounding areas. Particularly, two slow‐speed belts related to the lateral extrusion are confined within SE Tibet, supporting a model of limited crustal extrusion. Key Points: We obtain an integrated 3‐D Versus model by ambient noise tomography based on the data from both central Myanmar and southeastern TibetDistinct crustal structures in the West Burma Block, southeastern Tibet, and the Shan Plateau coincide with distinct tectonic responsesTwo north‐south low‐velocity zones in mid‐to‐lower crust end near Red River Fault, indicating restricted crustal extrusion [ABSTRACT FROM AUTHOR]

Abstract—The capabilities of a combination of passive seismic methods to study the geological structure of the upper part of the Earth's crust compared to active methods are analyzed using case examples. The passive methods include microseismic sounding, Nakamura's horizontal-to-vertical spectral ratio method (HVSR), seismic interferometry, and, for anthropogenic sites, ambient vibration testing using industrial signals. Three examples are considered: a zone of a platform tectonic earthquake, a kimberlite pipe, and a hydroelectric dam with foundation site. The results of the passive and active seismic methods agree well. Passive methods give more diffuse horizontal boundaries but clearly identify near-vertical heterogeneities. Combining passive methods is effective for reconnaissance studies and in the remote regions that are difficult to access by active observation techniques. Combination of passive methods enables simultaneous processing of seismic records obtained through different passive methods, with a minimum of two sensors required. [ABSTRACT FROM AUTHOR]

Seismic data acquired in seismic exploration is often contaminated by random noise, and it is necessary to develop effective seismic data denoising methods in seismic signal processing. The current seismic data denoising solutions mainly include traditional model-based methods and supervised deep learning methods. However, the traditional model-based methods cannot achieve satisfactory denoising accuracy, while the supervised deep learning methods have a demand for labeled data, which is difficult to collect in field seismic exploration. To address these difficulties in seismic signal denoising, we proposed an unsupervised deep learning method USDDM (Unsupervised Seismic Denoising Diffusion Model) based on diffusion model. USDDM utilizes the forward process of a diffusion model to assimilate the noise in seismic data, and then the backward process of the diffusion model is used to gradually remove the assimilated noise. Experiments on synthetic and field data are carried out to evaluate the denoising performance of USDDM, model-based methods f-x deconvolution and curvelet, and supervised deep learning methods CAE and DnCNN are used as comparison models. The results of the simulated experiments suggest that the proposed USDDM can effectively reduce the random noise in contaminated seismic data. USDDM can achieve better denoising accuracy compared to f-x deconvolution and curvelet, and the performance between USDDM and supervised deep learning methods is very close. The effectiveness and practicality of USDDM are demonstrated. [ABSTRACT FROM AUTHOR]

SUMMARY: The Pohang Basin sustained the most extensive seismic damage in the history of instrumental recording in Korea due to the 2017 Mw 5.5 earthquake. The pattern of damage shows marked differences from a radial distribution, suggesting important contributions by local site effects. Our understanding of these site effects and their role in generating seismic damage within the study area remains incomplete, which indicates the need for a thorough exploration of subsurface information, including the thickness of soil to bedrock and basin geometry, in the Pohang Basin. We measured the depth to bedrock in the Pohang Basin using dense ambient noise measurements conducted at 698 sites. We propose a model of basin geometry based on depths and dominant frequencies derived from the horizontal-to-vertical spectral ratio (HVSR) of microtremor at 698 sites. Most microseismic measurements exhibit one or more clear HVSR peak(s), implying one or more strong impedance contrast(s), which are presumed to represent the interface between the basement and overlying basin-fill sediments at each measurement site. The ambient seismic noise induces resonance at frequencies as low as 0.32 Hz. The relationship between resonance frequency and bedrock depth was derived using data from 27 boreholes to convert the dominant frequencies measured at stations adjacent to the boreholes into corresponding depths to the strong impedance contrast. The relationship was then applied to the dominant frequencies to estimate the depth to bedrock over the whole study area. Maps of resonance frequency and the corresponding depth to bedrock for the study area show that the greatest depths to bedrock are in the coastal area. The maps also reveal lower fundamental frequencies in the area west of the Gokgang Fault. The results indicate a more complex basin structure than previously proposed based on a limited number of direct borehole observations and surface geology. The maps and associated profiles across different parts of the study area show pronounced changes in bedrock depth near inferred blind faults proposed in previous studies, suggesting that maps of bedrock depth based on the HVSR method can be used to infer previously unknown features, including concealed or blind faults that are not observed at the surface. [ABSTRACT FROM AUTHOR]

This work presents a thorough reevaluation of soil amplification in the La Chana neighborhood of Granada through a pioneering application of the horizontal-to-vertical spectral ratio technique on seismic noise data using various spectral approaches. The research recycles old seismic noise data recorded at 34 stations with 2 Hz instruments in the year 2010, supplemented with additional measurements recorded with broadband seismometers at nearby locations in the years 2013 and 2017. Initial traditional processing identifies a narrowband dominant frequency around 1.5 Hz, attributed to artificial or anthropogenic sources. To address this, the Maximum Entropy Algorithm was implemented to smooth the spectral response below 1 Hz, and filter out frequency peaks with very narrow spectral bands, while preserving the narrowband frequency around 1.5 Hz in some records. The Thomson Multitaper method further refined the spectral ratio, emphasizing the detection and suppression of narrow frequency bands that may be related to industrial activity. The results demonstrated the reappearance of the 1.5 Hz frequency, but this time without narrow bandwidths, indicating its possible correlation with the natural ground movement. Fundamental periods, ranging from 0.45 s to 0.88 s, suggest a diverse lithological composition, indicating the presence of layers of sands, clays, conglomerates, and carbonates over a basement that represents the main impedance contrast in the area. The multispectral approach surpasses conventional methods in precision and reliability, providing valuable insights for earthquake risk assessment, urban planning, and engineering decisions in seismically active regions. [ABSTRACT FROM AUTHOR]

The seismic data acquisition design with 'two-wide and one-high' geometry effectively improves the imaging quality of seismic records. However, when data are acquired in the field, complex near surface conditions and environmental factors can introduce a variety of noises and gaps in seismic data, impacting the accuracy of seismic imaging. Currently, the method of low-rank matrix/tensor completion is commonly employed for data reconstruction after normal moveout (NMO). In a complex subsurface medium, common midpoint data processed with NMO may not satisfy the linear or quasi-linear assumptions within local data windows. Therefore, this paper exploits the inherent low-rank structure of high-dimensional data to propose a high-dimensional tensor completion method under the Frobenius-nuclear mixed norm constraint (FN-TC). This method unfolds the 4D data tensor into the frequency-space domain along its modes (m, n) and subsequently imposes a non-convex Frobenius-nuclear mixed norm constraint on the unfolded approximate matrices. This approach closely approximates the rank function of the factor matrices, thereby enhancing the accuracy of data modeling. Theoretical and practical studies demonstrate that the novel FN-TC approach can effectively reconstruct high-dimensional seismic data and suppress noise, thereby providing data support for subsequent high-precision seismic imaging. [ABSTRACT FROM AUTHOR]

Traditional denoising methods often lose details or edges, such as Gaussian filtering. Shearlet transform isa multi-scale geometric analysis tool which has the advantages of multi-resolution and multi-directivity. Compared with wavelet, curvelet, and contourlet transforms, it can retain more edge details while denoising, and the subjective vision and objective evaluation indexes are better than other multi-scale geometric analysis methods. Deep learning has made great progress in the field of denoising, such as U_Net, DnCNN, FFDNet, and generative adversarial network, and the denoising effect is better than BM3D, the traditional optimal method. Therefore, we propose a random noise suppression network ST-hFFDNet based on non-subsampled shearlet transform (NSST) and improved FFDNet. It combines the advantages of non-subsampled shearlet transform, Huber norm, and FFDNet, and has three characteristics. 1) Shearlet transform is an effective feature extraction tool, which can obtain the high and low frequency features of a signal at different scales and in different directions, so that the network can learn signal and noise features of different scales and directions. 2) The noise level map can improve the noise reduction performance of different noise levels. 3) Huber norm can reduce the sensitivity of the network to abnormal data and improve the robustness of network. The network training process is as follows. 1) BSD500 datasets are enhanced by flipping, rotating, scaling, and cutting. 2) AWGN with noise level ae[0,75] is added to the enhanced datasets to obtain the training datasets. 3) NSST multi-scale and multi-direction decomposition is performed on each pair of samples of the training datasets to obtain highand low-frequency images of different scales and directions. 4) Based on the decomposed high and low frequency images, the ST-hFFDNet network is trained by Adam algorithm. 5) All samples of the training data set are carried out in steps (3) and (4), and the trained model is thus obtained. Simulation experiments and real seismic data denoising show that for low noise, the proposed method is slightly better than NSST, DnCNN, and FFDNet and that it is superior to NSST, DnCNN, and FFDNet for high noise. [ABSTRACT FROM AUTHOR]

The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments. [ABSTRACT FROM AUTHOR]

The Hengill volcano and its associated geothermal fields represent Iceland's most productive harnessed high‐temperature geothermal fields, where resources are fueled by cooling magmatic intrusions connected to three volcanic systems. The crustal structure in this area is highly heterogeneous and shaped by the intricate interplay between tectonic forces and magmatic/hydrothermal activities. This complexity makes detailed subsurface characterization challenging. In this study, we aim to push the current resolution limits using a 500‐node temporary seismic array and perform an isotropic and, for the first time, radially‐anisotropic velocity model of the area. The high‐resolution isotropic velocity model reveals the characteristic N30ºE fissure swarm that crosses the area within the top 500 m and outlines a deep‐seated low‐velocity body composed of cooling magmatic intrusions at 5 km depth. This deeper body is located near the eastern part of the three volcanic centers and connected to a shallower body at 2–3 km depth that strikes westward toward Hengill volcano. Additionally, our study discovered that non‐induced earthquakes deeper than 2 km align with velocity contrasts that reflect structural variability, indicating the potential to identify deep permeable pathways using dense array imaging. The anisotropic model indicates that the shallow crust of Hengill within the top 2 km is dominated by vertical fractures or cracks, likely attributed to overall divergent deformation from rifting in the study area. This characteristic is diminished at depths greater than 2–3 km, replaced by a layering pattern where the lava flows and/or subhorizontal intrusions become the primary factors influencing the observed anisotropy. Plain Language Summary: In this study, we explore the subsurface beneath Iceland's most productive geothermal fields, situated on the flanks of the Hengill volcano, around 30 km to the east of Reykjavik. Using 500 temporary seismic sensors, we use seismic waves to image the subsurface and unveil structures with unprecedented detail. We observe both shallow and deep structures within the top 5 km. Specifically, we identify a shallow crust that coincides with a fractured region that is observed on the surface, along with regions where the seismic waves travel slower and we interpret to be hot magmatic intrusions at 4–5 km depth that power the geothermal fields above. Additionally, we observe that natural seismicity tends to occur near velocity contrasts that likely indicate structural boundaries. We interpret this observation as potential permeable pathways where hot fluids ascend to the shallow crust. Furthermore, our study sheds light on the structural fabrics influenced by the drifting of the tectonic plates and the magmatic deposits as distinct layering at the base of volcanoes. Key Points: Dense array seismic noise imaging can resolve iso‐ and anisotropic structures beneath the Hengill geothermal fields in the top 5 km depth with unprecedented detailThe VVOIGT isotropic image delineates the shallow fractured crust, deep‐seated solidified magmas, and potential deep permeable pathwaysThe anisotropic image illuminates structural fabrics related to rifting and lithology in Hengill [ABSTRACT FROM AUTHOR]

Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is used to efficiently simulate ambient noise data with quantities of local noise sources labelled as training data sets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area. [ABSTRACT FROM AUTHOR]

Investigating subsurface shear wave velocity (v s) structures using surface wave dispersion data involves minimizing a misfit function that is commonly solved through gradient-based optimization. Sensitivity kernels for model updates are commonly estimated using numerical differentiation, variational methods or implicit functions which however, may involve numerical instability and computational challenges when dealing with complex velocity models and large data sets. In this study, we propose a novel surface wave inversion framework in which error-free gradients are calculated by automatic differentiation (AD) and forward modelling is implemented by convenient computational graphs in the state-of-the-art deep learning framework. The AD-based inversion approach is first validated using two synthetic data sets. Then, the subsurface structures at three distinct locations, namely the Great Plains and the Long Beach in the US and Tong Zhou in China, are also derived using this method with seismic ambient noise data, which show nice consistency with those obtained using traditional methods. With the significantly improved computational efficiency, a great number of initial models can be inverted simultaneously to mitigate the impact of local minima and to estimate the uncertainty in the invert models. We have developed a new surface wave inversion package named ADsurf based on automatic differentiation and computational graphs in the deep learning framework, and its computational efficiency is also compared with the traditional finite-difference-based gradient estimation approach. While a great number of intriguing studies on the geophysical inverse problems have been conducted recently using deep learning for end-to-end mapping, the use of AD provided in the in the deep learning frameworks to assist and expedite the gradient computations are still underexploited in geophysics. Thus, it is expected that various geophysical inverse problems in many different areas beyond the surface wave inversion can also be tackled with this new paradigm in the future. [ABSTRACT FROM AUTHOR]

The basic assumption of low‐rank methods is that noise‐free seismic data can be represented as a low‐rank matrix. Effective noise reduction can be achieved through the low‐rank approximation of Hankel matrices composed of the data. However, selecting the appropriate rank parameter and avoiding expensive singular value decomposition are two challenges that have limited the practical application of this method. In this paper, we first propose symplectic geometric decomposition that avoids singular value decomposition. The symplectic similarity transformation preserves the essence of the original time sequence as well as the signal's basic characteristics and maintains the approximation of the Hankel matrix. To select an appropriate rank, we construct the symplectic geometric entropy according to the distribution of eigenvalues and search for high‐contributing eigenvalues to determine the needed rank parameter. Therefore, we provide an adaptive approach to selecting the rank parameter by the symplectic geometric entropy method. The synthetic examples and field data results show that our method significantly improves the computational efficiency while adaptively retaining more effective signals in complex structures. Therefore, this method has practical application value. [ABSTRACT FROM AUTHOR]

Purpose: The working environment of the mine is complex, and there are many interference noises. Microseismic signals and noises are mixed together, which interferes with the analysis of microseismic signals and affects the subsequent work of phase arrival picking and source location. Therefore, it is necessary to study the noise reduction of microseismic signals. Method: In view of the randomness and non-stationarity of microseismic signals, a signal denoising method based on the multi-layer joint is proposed in this paper. SVD (singular value decomposition) is used as the first layer denoising method to remove the white noise in the signal. EMD( Empirical Mode Decomposition) is used as the second layer noise reduction method to deal with random and non-stationary microseismic signals. As the third layer noise reduction method, the wavelet threshold eliminates the modal aliasing phenomenon generated by EMD. Result: The experimental analysis shows that the proposed method has a good noise reduction effect on the simulation signal and the actual microseismic signal with different signal-to-noise ratios. Conclusion: It overcomes the disadvantage of the poor noise reduction effect of a single noise reduction method, can greatly improve the signal-to-noise ratio of the signal, and has a high similarity with the original signal, which provides a new idea for microseismic signal noise reduction. [ABSTRACT FROM AUTHOR]

Ambient noise correlation analyses are largely used to monitor temporal medium changes generally associated with stress field variations and/or fluid movement. Here, we analyze the 2021 eruption of La Palma, the most active island in the Canary archipelago, to study its effects on the structure in a post-eruptive stage. To date, most of the studies, whether in volcanic environments or other geological systems, focus on determining seismic velocity changes that can be associated with precursory signals. In our study, we are interested in localizing the medium changes that permit constraining the depth of the most affected structure and suggesting possible mechanisms capable of inducing such alterations. The auto- and cross-correlation functions were computed using the phase cross-correlation strategy. The correlations were linearly stacked using a 3-day sliding window. The combination of these two approaches proved to render the best results. The analysis of 3 years of data resulted in the detection of occasional decorrelation before the eruption, followed by a well-defined decoherence period after the eruption. In addition, the relationship between the waveform correlation and lag time, using autocorrelations from before and after the eruption, permits identifying phase shifts and waveform distortion, which are sensitive to different parameters and, thus, have great importance in inferring the possible mechanism. Phase shifts occur when there is only velocity change without changing the structure, whereas waveform distortion is caused by a structural (geological) change. We also inferred the depths at which the most significant medium alterations occur. We observed that the decorrelation occurs at lag times corresponding to changes localized in depth. The surface structure appears to not have undergone significant medium changes for depths until approximately 8 km, either before or after the eruption. [ABSTRACT FROM AUTHOR]

Understanding glaciers structural heterogeneity is crucial for assessing their fate. Yet, places where structure changes are strong, such as crevasses fields, are often inaccessible for direct instrumentation. To overcome this limitation, we introduce an innovative technique that transforms seismic sources, here generated by crevasses, into virtual receivers using source‐to‐receiver spatial reciprocity. We demonstrate that phase interference patterns between well‐localized seismic sources can be leveraged to retrieve phase velocity maps using Seismic Michelson Interferometry. The obtained phase velocity exhibits sensitivity to changes in glacier structure, offering insights into the origins of mechanical property changes, with spatial resolution surpassing traditional methods by a factor of five. In particular, we observe sharp variations in phase velocity related to strongly damaged subsurface areas indicating a complex 3‐D medium. Applying this method more systematically and in other contexts will enhance our understanding of the structure of glaciers and other seismogenic environments. Key Points: We transform seismic sources from crevasses into virtual receivers using source‐to‐receiver spatial reciprocityWe derive phase velocity maps in previously inaccessible areas with a resolution five times larger than traditional approachesWe retrieve the influence of glacier geometry and structural heterogeneity on the glacier mechanical properties [ABSTRACT FROM AUTHOR]

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time. [ABSTRACT FROM AUTHOR]

The Pubugou dam is located on the Dadu River in China, with a height of 186 m. The fracturing body at 780 m upstream of its right bank poses a major threat to the downstream reservoir area's safety as well as the operation of the dam. In this study, a multi-source three-dimensional monitoring mode of space-ground-body is built using synthetic aperture radar, surface sensing, and rock mass stress monitoring. This makes it possible to keep an eye on the entire fracturing body in real time. This study proposes a GCPs selection method that combines the high coherence, small deformation reference points obtained by PS-InSAR inversion and stable deformation points, and realizes the high-precision bank slope deformation time series data acquisition method. It addresses the issue of strong subjectivity in the selection of ground control points (GCPs) in the SBAS-InSAR solution process in mountainous areas. The change in multiple scattering wave velocity and the evolution of the rock mass state inside the slope are both extracted using the seismic background noise cross-correlation technique. Through time series analysis and comparison of various monitoring data, the three-dimensional deformation characteristics, instability mechanism, and significant influence of rainfall on the deformation development of the fracturing body are summarized. Additionally, the adaptability, advantages, and disadvantages of various monitoring modes are evaluated. For the crucial slope of the project, a more trustworthy monitoring method will be available thanks to the mutual integration of the space-ground-body monitoring mode and data. [ABSTRACT FROM AUTHOR]

Two-dimensional dense seismic ambient noise array techniques have been widely used to image and monitor subsurface structure characterization in complex urban environments. It does not have limitations in the layout under the limitation of urban space, which is more suitable for 3D S-velocity imaging. In traditional ambient seismic noise tomography, the narrowband filtering (NBF) method has many possible dispersion branches. Aliases would appear in the dispersive image, and the dispersion curve inversion also depends on the initial model. To obtain high-accuracy 3D S-velocity imaging in urban seismology, we developed a robust workflow of data processing and S-velocity tomography for 2D dense ambient noise arrays. Firstly, differing from the NBF method, we adopt the continuous wavelet transform (CWT) as an alternative method to measure the phase velocity from the interstation noise cross-correlation function (NCF) without 2π ambiguity. Then, we proposed the sequential dispersion curve inversion (DCI) strategy, which combines the Dix linear inversion and preconditioned fast descent (PFD) method to invert the S-velocity structure without prior information. Finally, the 3D S-velocity model is generated by the 3D spatial interpolation. The proposed workflow is applied to the 2D dense ambient seismic array dataset in Changchun City. The quality evaluation methods include residual iteration error, horizontal-to-vertical spectral ratio (HVSR) map, and electrical resistivity tomography (ERT). All tests indicate that the developed workflow provides a reliable 3D S-velocity model, which offers a reference for urban subsurface space exploration. [ABSTRACT FROM AUTHOR]