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One of the significant challenges in processing and analyzing seismic data is the contamination of the seismic signal with noise from various sources. Coherent or incoherent noise, multiples, and other types of noise can all be eliminated using a number of techniques, but it is still complicated to attenuate random noise to the desired level. The non-homogeneous curvelet transform is first applied in this study, and the regularized calculation is then carried out using the inverted operator. Following the application of the linear calculation method, the threshold value is modified to remove the coefficient noise during the iteration process, allowing the homogeneous coefficients and noise-free seismic data to be obtained. We choose some real data as an example and apply the suggested curvelet theory to obtain homogeneous data in order to verify the denoising effect. Tests on real data show that, when the suggested approach interpolates sampled data to be homogeneous, random noise can be effectively reduced. The important benefit of this approach is that features like multi-resolution, and locality introduce minimal overlapping between coefficients representing signal and noise in curvelet domain. [ABSTRACT FROM AUTHOR]

This study uses ambient noise and earthquake waveform data recorded over ~25 broadband seismic networks in the Himalayas, Tibet, and the Pamir-Hindu Kush to compute a 3-D shear wave velocity (Vs) model of the crust beneath the Kohistan and Ladakh arcs. The velocity model, with a lateral resolution of ~30 km, is derived using a Bayesian hierarchical trans-dimensional inversion of fundamental mode Rayleigh wave group velocity dispersion measurements. The result shows evidence of a uniform upper and middle crust (Vs ~ 3.5-3.6 km/s) underlain by an anomalous high-velocity layer (HVL) with Vs >4 km/s at depths exceeding 40 km beneath the Kohistan arc, consistent with laboratory-derived crustal models showing its pre-collisional structures. The thickness of the HVL is maximum (>20 km) in southern Kohistan, which exposes the mafic and ultramafic arc crust. In contrast, the Ladakh arc, which has seismic structures similar to those in southern Tibet, is characterized by significantly low velocities (Vs < 3.4 km/s) in the middle crust (20-40 km depth) and a relatively thin HVL (~5-10 km) above a Moho depth, which mostly varies between 60 and 72 km. The contrasting velocity structure indicates that the Kohistan arc preserves a typical arc-like crust, whereas the Ladakh arc has undergone significant modifications since the India-Asia collision. [ABSTRACT FROM AUTHOR]

Shale anisotropy has a significant impact on the data processing and interpretation of microseismic monitoring in shale gas reservoirs. A geology- and rock-physics-constrained approach to estimating shale anisotropy using down-hole microseismic data sets is proposed in this study and is applied to the case of Horn River shale. A priori knowledge of shale anisotropy is obtained by integrating geological analyses and rock physics studies. This knowledge serves as an important constraint when building the initial model, minimizing the uncertainties and evaluating the results. The application to Horn River shale shows that the optimized anisotropic velocity model reduces the time misfit by about 65% compared to the originally provided velocity model. As the relocated perforation shot indicates, the event locations are significantly improved. The results also show that a high fraction of clay mineral results in strong fabric anisotropy in the Fort Simpson formation, whereas the quartz-rich shale gas reservoirs (Muskwa and Otter Park formations) show weaker fabric anisotropy. The percentage of velocity anisotropy in Horn River shale can be up to 40%. The fabric anisotropy of shale derived from the downhole microseismic data set is comparable with that of laboratory experiments. This demonstrates that downhole microseismic monitoring, as a quasi in situ experiment, has the potential to contribute to a better understanding of subsurface anisotropy beyond the laboratory. In addition, microseismic measurements of shale anisotropy are conducted in the seismic frequency band and are thus more applicable for further seismic applications. [ABSTRACT FROM AUTHOR]

Linear arrays are popularly used for passive surface wave imaging due to their high efficiency and convenience, especially in urban applications. The unknown characteristics such as azimuth of noise sources, however, make it challenging to extract accurate phase-velocity dispersion information by employing a 1-D linear array. To solve this problem, we proposed an alternative passive surface wave method to capture the dominant azimuth of noise sources and retrieve the phase-velocity dispersion curve by polarization analysis with multicomponent ambient noise records. We verified the proposed method using synthetic data sets under various source distributions. According to the calculated dominant azimuth, it is deduced that noise sources are mainly classified as either inline or offline distribution. For inline noise source distribution, we are able to directly obtain the unbiased phase-velocity measurements; for offline noise source distribution, we should correct the velocity overestimation due to azimuthal effects using the proposed method. Results from two field examples show that the distributions of noise sources are predominantly offline. We eliminated the velocity bias caused by offline source distribution and picked phase velocities following higher amplitude peaks along the trend. After the azimuthal correction, the picked phase-velocity dispersion curves in dispersion images generated from passive source data match well with those from active source data, demonstrating the practicability of the proposed technique. [ABSTRACT FROM AUTHOR]

Elevated seismic noise for moderate‐size earthquakes recorded at teleseismic distances has limited our ability to see their complexity. We develop a machine‐learning‐based algorithm to separate noise and earthquake signals that overlap in frequency. The multi‐task encoder‐decoder model is built around a kernel pre‐trained on local (e.g., short distances) earthquake data (Yin et al., 2022, https://doi.org/10.1093/gji/ggac290) and is modified by continued learning with high‐quality teleseismic data. We denoise teleseismic P waves of deep Mw5.0+ earthquakes and use the clean P waves to estimate source characteristics with reduced uncertainties of these understudied earthquakes. We find a scaling of moment and duration to be M0 ≃ τ4, and a resulting strong scaling of stress drop and radiated energy with magnitude (Δσ≃M00.21 ${\Delta }\sigma \simeq {M}_{0}^{0.21}$ and ER≃M01.24 ${E}_{R}\simeq {M}_{0}^{1.24}$). The median radiation efficiency is 5%, a low value compared to crustal earthquakes. Overall, we show that deep earthquakes have weak rupture directivity and few subevents, suggesting a simple model of a circular crack with radial rupture propagation is appropriate. When accounting for their respective scaling with earthquake size, we find no systematic depth variations of duration, stress drop, or radiated energy within the 100–700 km depth range. Our study supports the findings of Poli and Prieto (2016, https://doi.org/10.1002/2016jb013521) with a doubled amount of earthquakes investigated and with earthquakes of lower magnitudes. Plain Language Summary: The vibration of the Earth's ground recorded at seismometers carries the seismic signatures of distant earthquakes superimposed to the Earth's natural or anthropogenic noise surrounding the seismic station. We use artificial intelligence technology to separate the weak signals of distant earthquakes from other sources of ground vibrations unrelated to the earthquakes. The separated signal provides new insights into earthquakes, especially those within the Earth's deep interior, most of which have not been investigated due to noise levels. In contrast with shallow earthquakes, deep earthquakes are less efficient at radiating energy, though they exhibit a higher rate of increase in both stress drop and radiated energy as they grow. This may suggest that deep earthquakes tend to be more confined fault surfaces. A dual mechanism between nucleation in the subduction‐zone core and propagation of larger events in the dry mantle explains our observations. Key Points: A neural network is used to double the number of earthquakes for source analyses compared to previous studies by improving the data qualityDenoising teleseismic waves improves the source signature in Mw5.0–6.0 events and reduces uncertainties over all magnitudesLarge deep earthquake ruptures are dissipative and compact relative to crustal earthquakes [ABSTRACT FROM AUTHOR]

Monitoring groundwater levels in aquifers is crucial for water resources management on a global scale. In Greece, water stress is particularly high due to the expansion of agricultural land, urbanization and tourism, leading to aquifer over‐exploitation. In this study, we investigate the possibility of monitoring groundwater reservoirs from seismic velocity changes (δv/v) measured using noise autocorrelations. The results obtained at short periods (0.5–3 s) indicate that in several regions, seismic wave velocity varies according to an annual cycle mainly controlled by aquifer loading and discharge. We also predict seismic velocity variations from a simple model using precipitation records. In addition, this model provides a proxy for the characteristic time of aquifer discharge rate and allows us to distinguish shallow alluvial aquifers (∼100 m) from deep karst systems (∼1 km). Finally, we study the effect of water pumping from groundwater reservoirs. To that end, we combine Global Positioning System (GPS), hydraulic heads and velocity variations measurement. The results indicate that confined and unconfined aquifers respond differently. While for unconfined aquifers, pumping water implies an uplift of the surface, we show that for confined aquifers a subsidence occurs, due to the consolidation of the sediments at depth. Being an in‐situ and volumetric measurement, the δv/v is primarily sensitive to the amount of water stored in the entire aquifer system, whereas GPS measurements provide the deformation at the surface induced by both deep sediment compaction and water level variations. These observations show that GPS, hydraulic head, and δv/v are complementary tools to monitor aquifers. Plain Language Summary: Monitoring the evolution of groundwater resources is an issue for the twenty‐first century. The expansion of agricultural land, urbanization and tourism are depleting aquifers. In this study, we propose to monitor groundwater reservoirs from the evolution of seismic wave velocity. The results obtained in Greece indicate that in several regions, the seismic wave velocity varies according to an annual cycle mainly controlled by aquifer loading and discharge. Hence by using continuous seismic noise records it is possible to track the evolution of aquifers over time and to distinguish shallow (∼100 m) from deep aquifers (∼1 km). Finally, we study the effect of water pumping from groundwater reservoirs. To that end, we combine Global Positioning System surface deformation measurements with seismic velocity variations. The results indicate that confined and unconfined aquifers respond differently. While for unconfined aquifers, pumping water implies an uplift of the surface, we show that for confined aquifers a subsidence occurs, due to the consolidation of the sediments at depth. While for unconfined aquifers, these surface variations are transient, the drainage of confined aquifers due to pumping is permanent. Key Points: Seismic velocity at short periods are influenced by the hydrologic cycle and the amount of water within aquifersA simple linear model can predict the evolution of seismic wave velocity from precipitationWe show that Global Positioning System measurement, hydraulic heads and velocity variations are complementary tools to monitor aquifers [ABSTRACT FROM AUTHOR]

Q-compensated reverse time migration (Q-RTM) is a crucial technique in seismic imaging. However, stability is a prominent concern due to the exponential increase in high-frequency ambient noise during seismic wavefield propagation. The two primary strategies for mitigating instability in Q-RTM are regularization and low-pass filtering. Q-RTM instability can be addressed through regularization. However, determining the appropriate regularization parameters is often an experimental process, leading to challenges in accurately recovering the wavefield. Another approach to control instability is low-pass filtering. Nevertheless, selecting the cutoff frequency for different Q values is a complex task. In situations with low signal-to-noise ratios (SNRs) in seismic data, using low-pass filtering can make Q-RTM highly unstable. The need for a small cutoff frequency for stability can result in a significant loss of high-frequency signals. In this study, we propose a multi-task learning (MTL) framework that leverages data-driven concepts to address the issue of amplitude attenuation in seismic records, particularly when dealing with instability during the Q-RTM (reverse time migration with Q-attenuation) process. Our innovative framework is executed using a convolutional neural network. This network has the capability to both predict and compensate for the missing high-frequency components caused by Q-effects while simultaneously reconstructing the low-frequency information present in seismograms. This approach helps mitigate overwhelming instability phenomena and enhances the overall generalization capacity of the model. Numerical examples demonstrate that our Q-RTM results closely align with the reference images, indicating the effectiveness of our proposed MTL frequency-extension method. This method effectively compensates for the attenuation of high-frequency signals and mitigates the instability issues associated with the traditional Q-RTM process. [ABSTRACT FROM AUTHOR]

Extreme weather events threaten life and property in populated areas. Timely and precise weather event monitoring and risk assessment are critical, but can be hampered by limited meteorological stations in cities. Recent studies have shown that seismic stations are sensitive to storm-induced noise. This study aims to investigate the sensitivity of distributed acoustic sensing (DAS), a technology capable of turning existing optical fibres into dense seismic sensors, for recording storm-induced seismic noise. We analyse 4-month continuous DAS recordings (June–September 2021) from a 4.2-km-long underground fibre-optic array in State College, PA. We calibrate the DAS data by comparing it to various meteorological data (rainfall and wind speed) from nearby weather stations. We first show DAS-recorded low-frequency wind-induced noise (0.5–8 Hz) probably caused by light poles swaying in the wind, as observed resonant frequencies agree with theoretical natural frequencies of nearby light poles. We find a strong linear correlation between DAS energy and wind speed. We further characterize rain-induced noise. Detailed observations from two rain events: a moderate rain and a heavy rain from Hurricane Ida, suggest that rain-induced noise is not generated by direct raindrops hitting the ground. Instead, the low-frequency noise (2–8 Hz) is attributed to the acoustic noise generated by water flow in stormwater drainage systems. Strong high-frequency noise up to 125 Hz is likely related to the rapid rainwater filling from the surface to the drainage system during heavy rain. We show linear relations between rain-induced DAS energy and rainfall rate, where the slopes of relations are related to the volume of rainwater, suggesting the influence of surface water and rainwater flow in the drainage system on DAS signals. Our results show the possibility of using DAS-equipped pre-existing telecom fibre-optic cables for sensing windstorms and rainstorms in urban areas and their interactions with urban infrastructures. [ABSTRACT FROM AUTHOR]

Seismic interferometry (SI) is a technique that allows one to estimate the wavefields accounting for the wave propagation between seismometers, any of which can act as a virtual source (VS). Interferometry, particularly noise interferometry, has been applied to several geophysical disciplines such as passive monitoring and distributed acoustic sensing. In practice, one requires long recordings of seismic noise for noise interferometry. Additionally, one can have missing seismic interferometric traces because some receivers in seismic arrays may be absent or inoperative due to issues of receiver installation and malfunction. Thus, filling the gap of seismic interferometric profile requires wavefield reconstruction and regularization techniques. Compressive sensing (CS) is one such method that can reconstruct seismic interferometric wavefields and help mitigate the limitations by exploiting the sparsity of seismic waves. In our work, we use CS to reconstruct missing seismic interferometric wavefields. One can interpolate interferometric wavefields using correlograms provided by one VS. We call this method of reconstructing an individual VS gather single-source wavefield reconstruction. We propose an alternative technique called multi-source wavefield reconstruction , which applies CS to reconstruct multiple interferometric wavefields using a volume of VS gathers provided from all available VSs. Using numerical examples, we show that one can apply CS to recover interferometric wavefields resulting from interferometry of a linear seismic array. To exploit the sparsity of interferometric wavefields, we apply the Fourier and Curvelet transforms to the two reconstruction schemes. Using the signal-to-noise ratio (SNR) to compare reconstruction of interferometric wavefields, the Fourier multi-source method improves the recovery of interferometric wavefields by approximately 50 dB compared to the Fourier and Curvelet single-source wavefield reconstructions. [ABSTRACT FROM AUTHOR]

Applications of acoustic noise interferometry to passive remote sensing of the ocean rely on retrieval of empirical Green's functions (EGFs) from cross-correlations of ambient sound at spatially separated points. At ranges of tens of ocean depths, obtaining stable and accurate EGF estimates usually requires noise averaging periods of hours or days. Using data acquired in the Shallow Water 2006 experiment on the continental shelf off New Jersey, it is found that at ranges of 40–70 ocean depths, the EGFs can be retrieved with noise averaging times as short as 64 s. The phenomenon is observed for various receiver pairs but does not occur simultaneously in all azimuthal directions. The rapidly emerging EGFs have a wider frequency band and a richer normal mode content than the EGFs obtained in previous studies using long averaging times and are better suited for monitoring physical processes in the water column. Available acoustic and environmental data is examined to understand the conditions leading to rapid EGF emergence from diffuse noise. Strong intermittency is observed in the horizontal directionality of ambient sound. Rapid emergence of EGF in shallow-water waveguide is found to occur when the directionality of diffuse ambient noise is favorable. [ABSTRACT FROM AUTHOR]

In the stratified environment, systematic experiments are carried out to understand the impacts of rigid vegetation on the evolution of lock–release gravity currents, including the propagation process and entrainment characteristics. A theoretical model is developed to predict the gravity currents completely entering the vegetation-drag-dominated regime and verified by the experimental data. The adjustment of rigid vegetation on the current propagation is limited by the strongly stratified ambient fluid. With the coupling of the stratified ambient fluid and rigid vegetation, responses of current frontal velocity to the change of any single environmental factor are not monotonic. The phenomenon of finger-like intrusion, excited by the stratified environment and promoted by the rigid vegetation, provides a nonnegligible contribution to the current entrainment. In the homogeneous environment, the entrainment occurring in the current head caused by the wakes behind submerged patches is stronger than that of the Rayleigh–Taylor instability, while the situation is reversed when the ambient fluid is stratified. [ABSTRACT FROM AUTHOR]

Seismic data collected from desert areas contain a large amount of low-frequency random noise with similar waveforms to the effective signals. The complex noise characteristics make it difficult to effectively identify and recover seismic signals, which will adversely affect subsequent seismic data processing and imaging. In order to recover the complex seismic events from low-frequency random noise, we propose an attention mechanism guided deep convolutional autoencoder network (ADCAE) to assign different importance to different features at different spatial position. In ADCAE, an attention module (AM) is connected to the deep convolutional autoencoder network (DCAE) with soft-thresholded symmetric skip connection that helps to enhance the ability of feature extraction. By combining the global features of the input data and the output local features of DCAE, AM generates an attention weight matrix, which assigns different weights to the features associated with the seismic events and random noise during the training process. In this way, AM can guide the update of the target gradient, thus retains the complex structure of the seismic events in the denoised results and improves the training efficiency of the model. The ADCAE is applied to the synthetic data and field seismic data, and denoised results show that ADCAE has achieved satisfactory denoising performance in signals recovery and low-frequency random noise suppression at the low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Comprehensive research methods such as literature research, theoretical analysis, numerical simulations and field monitoring have been used to analyze the disasters and characteristics caused by the linkage failure and instability of the residual coal pillars-rock strata in multi-seam mining. The effective monitoring area and monitoring design method of linkage instability of residual coal pillar-rock strata in multi-seam mining have been identified. The evaluation index and the risk assessment method of disaster risk have been established and the project cases have been applied and validated. The results show that: ①The coal pillar will not only cause disaster in singleseam mining, but also more easily cause disaster in multi-seam mining. The instability of coal pillars can cause not only dynamical disasters such as rock falls and mine earthquakes, but also cause surface subsidence and other disasters. ②When monitoring the linkage instability of residual coal pillar-rock strata, it is not only necessary to consider the monitoring of the apply load body (key block), the transition body (residual coal pillar) and the carrier body (interlayer rock and working face), but also to strengthen the monitoring of the fracture development height (linkage body). ③According to the principles of objectivity, easy access and quantification, combined with investigation, analysis, and production and geological characteristics of this mining area, the main evaluation indexes of the degree of disaster caused by linkage instability of residual coal pillar-rock strata are determined as: microseismic energy, residual coal pillar damage degree, fracture development height. And the evaluation index classification table was also given. ④According to the measured value of the evaluation index, the fuzzy comprehensive evaluation method was used to calculate the disaster risk degree in the studied mine belongs to class III, that is, medium risk level. The corresponding pressure relief technology was adopted on site, which achieved a good control effect, and also verified the accuracy and effectiveness of the risk evaluation results. [ABSTRACT FROM AUTHOR]

In this paper, a method for seismic random noise detection and suppression using a denoising convolutional neural network (DnCNN) is presented. Thanks to the residual learning and batch normalization, deep learning networks can converge faster, the gradient descent and disappearance due to the increase of network layers are solved, and the residual results can be predicted more accurately. For seismic data, the variance estimation method is useful for obtaining an accurate noise distribution model and statistical parameters that provide a useful assessment of the noise level. With the variance estimation method based on weak texture blocks, a noise distribution model and statistical parameters can be derived with high accuracy, and this method effectively estimates seismic noise levels. The DnCNN networks are trained, and non-Gaussian noise reduction technology is used to achieve blind noise reduction at unknown levels, improving the noise reduction of seismic data. In addition, stratigraphic dip characteristics related to layer structure are used as DnCNN training network constraints to prevent effective signals in seismic data from being corrupted by conventional DnCNN noise reduction methods. Geological features such as faults and fracture-cavities can be effectively protected. Carbonate faults in the Tarim Basin in China are affected by the desert surface and the depth at which reservoirs are buried. The seismic data has a low signal-to-noise ratio, and the effective signals of the reservoir are low resolution. The seismic data can be effectively enhanced with this method for noise reduction in this area, the fracture-cavity is effectively displayed, and the fault features are also highlighted. [ABSTRACT FROM AUTHOR]

This paper is devoted to the features of seismological observations in the Arctic seas, which are complicated by harsh climatic conditions, the presence of ice cover, stamukhi and icebergs, and limited navigation. Despite the high risk of losing expensive equipment, the deployment of local networks of bottom seismographs or stations installed on ice is still necessary for studying the seismotectonic characteristics and geodynamic processes of the region under consideration, the deep structure of the crust and upper mantle, seismic hazards, and other marine geohazards. Various types of seismic stations used for long-term and short-term deployments in the Russian sector of the Arctic Ocean, as well as various schemes and workflows for their deployment/recovery, are described. The characteristics of seafloor seismic noise and their features are also considered. The results of deployments demonstrate that the characteristics of the stations make it possible to reliably record earthquake signals and seismic noise. Based on the experience gained, it was concluded that the preferred schemes for deploying ocean-bottom seismographs are those in which their subsequent recovery does not depend on their power resources. Usually, such schemes allow for the possibility of dismantling stations via trawling and are suitable for the shelf depths of the sea. The advantages of such schemes include the possibility of installing additional hydrophysical and hydrobiological equipment. When using pop-up ocean-bottom seismographs, special attention should be paid to the careful planning of the recovery because its success depends on the possibility of a passage to the deployment site, which is not always possible due to changing meteorological and ice conditions. Seismic records obtained on the seafloor are characterized by a high noise level, especially during periods of time when there is no ice cover. Therefore, it is recommended to install bottom stations for periods of time when ice cover is present. The frequency range of the prevailing noise significantly overlaps with the frequency range of earthquake signals that must be taken into account when processing bottom seismic records. [ABSTRACT FROM AUTHOR]

Abstract We examined the seismic noise data collected from coastal and inland observatories in India, affected by the super cyclonic storm Amphan in the Indian Ocean, to understand the storm dynamics. Prominent disturbances in the 0.05–0.50 Hz frequency range were observed at the seismic stations, arising due to ocean‐continent interactions. The coastal stations displayed more pronounced ground motions contrary to the inland stations, with spindle‐shaped seismic wave envelopes intensifying as Amphan approached. The maximum ground displacements and energy occurred hours after the cyclone's eye, with maximum wind speed, moved away from the stations and not when it was close to the station. We observed significant variations in primary (0.05–0.10 Hz) and secondary microseism (0.10–0.50 Hz) energy during Amphan's directional changes. Secondary microseisms in short and long periods were found at 0.20–0.50 Hz and 0.10–0.20 Hz, respectively. Primary microseisms exhibited a simple pattern and were the weakest among the three energy bands. The CAL seismic station's seismic wave envelope showed an en‐echelon feature with increasing amplitude as Amphan approached, indicating the influence of ocean resonance and coastal wave reflection. This study demonstrates monitoring of the tropical cyclone paths based on seismic signatures obtained using microseisms recorded at seismic stations, a cost‐effective tool. Integrating these seismic signals with atmospheric observations in near real‐time would probably enable an effective monitoring of cyclones and timely issuance of their alerts.

The basic fundamentals of spectral analysis of horizontal and vertical components of microseismic noise for studying the geological structure of dams are presented. The issues of constructing deep sections of three dynamic parameters of microseisms are considered. The aim of the work is to study the seismic reaction of soils during microseismic sounding of hydraulic structures. The main objectives of the research include the comparison of deep sections of soil reinforcement coefficients with sections of transmission ratios and data from other geophysical methods. As a result of microseismic sounding of three hydraulic structures, it was found out that the seismic reaction of the clay core of dams differs significantly from the surrounding soils. The amplification coefficients in the bedrock and dispersed soils of the aeration zone are characterized by minimal values. For water-saturated clays, these values are 5–10 times higher, which may indicate that the main reason for the increase in the level of microseismic noise is an increase in the overall porosity and moisture of soils. It is noted that the amplification coefficients of horizontal microseisms are more noise-resistant and detailed compared to the parameters of vertical microseisms. It is stated that deep sections of dynamic microseism parameters allow us to confidently distinguish the main elements of the structure of soil dams, as well as to identify probable zones of increased filtration.

Effective monitoring of human activity in urban areas is essential for social stability and urban development. Traditional monitoring methods include wearable devices, survey sensor networks, and satellite remote sensing, which may be affected by privacy and weather conditions. Ambient seismic noise recorded by seismometers contains rich information about human activity and exhibits significant temporal and spatial variations, which provides valuable insights into social mobility. In this study, we investigated the correlation between human activity and ambient seismic noise in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using the data recorded by 138 seismometers. Our results indicate that ambient seismic noise produced by human activity in the GBA is mainly concentrated between 2 and 20 Hz. The spatial distribution of ambient seismic noise exhibits a strong correlation with population and economy. Our results show that the analysis of ambient seismic noise can reveal the spatial and temporal impacts of different factors on human activity in the GBA, such as day and night, holidays, weather changes, national policies, and the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, the analysis of 12-year-long ambient seismic noise at the Hong Kong seismic station shows a close connection between long-term changes in ambient seismic noise and local social development. This study suggests that the analysis of ambient seismic noise represents a novel method to gather critical information about human activity. Seismometers, which are widely deployed worldwide, have great potential as innovative tools for sensing human activity. [ABSTRACT FROM AUTHOR]

We analyse ambient noise seismic data from 23 three-component seismic nodes to study firn velocity structure and seismic anisotropy near the EastGRIP camp along the Northeast Greenland Ice Stream (NEGIS). Using 9-component correlation tensors, we derive dispersion curves of Rayleigh and Love wave group velocities from 3 Hz to 40 Hz. These velocity distributions exhibit anisotropy along and across the flow. To assess these variations, we invert dispersion curves for shear wave velocities (Vsh and Vsv) in the top 150 m of NEGIS using a Markov Chain Monte Carlo approach. The reconstructed1-D shear velocity model reveals radial anisotropy in the firn, with Vsh 12 %–15 % greater than Vsv, peaking at the critical density (550 kg m–3). We combine density data from firn cores drilled in 2016 and 2018 to create a new density parameterisation for NEGIS, serving as a reference for our results. We link seismic anisotropy in the NEGIS to effective and intrinsic causes. Seasonal densification, wind crusts, and melt layers induce effective anisotropy, leading to faster Vsh waves. Changes in firn recrystalisation cause intrinsic anisotropy, altering the Vsv to Vsh ratio. We observe a shallower firn-ice transition across flow (≈ 50 m) compared to along flow (≈ 60 m), suggesting increased firn compaction due to the predominant wind direction and increased deformation towards the shear margin. We demonstrate that short-duration (nine-day minimum), passive, seismic deployments, and noise-based analysis can determine seismic anisotropy in firn, and reveal 2-D firn structure and variability. [ABSTRACT FROM AUTHOR]

Random noise, which has a significant impact on subsequent processing and interpretation, easily interferes with seismic data. Current convolutional neural networks (CNN) use a single-stage technique to boost network capacity by exploiting the complicated network structure, but the performance of the network becomes saturated and prone to overfitting at a certain stage. Hence, we propose a two-stage U-Net denoising network with a supervised attention module (UNet-SAM). In this supervised algorithm, the first stage obtains the pre-denoising results, while the second stage achieves more accurate data. The supervised attention module (SAM) block is inserted in the first stage, extracting features with supervised attention to utilise as a priori information and guide the fine denoising in the second stage. The combination of the attention mechanism and two-stage strategy provides prior information that helps to train a network with better denoising performance. Experiments on synthetic and field data illustrate that the proposed UNet-SAM not only has a superior denoising effect but also retains more of the original effective signal. [ABSTRACT FROM AUTHOR]

Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining. [ABSTRACT FROM AUTHOR]

The Makran subduction zone is unique in its wide onshore thick accretionary prism, and volcanic arc not parallel to the E–W trend of the Makran accretionary prism. To investigate the internal structure of the accretionary prism, the crustal nature of Jaz Murian Depression, and the trend of the subducting plate hinge, we have calculated a 3D shear‐wave velocity model for a region around the border between eastern and western Makran using ambient‐noise adjoint tomography and data from IASBS/CAM Makran temporary seismic network. In close agreement with previous works, our velocity model shows that the onshore accretionary prism consists of a low‐velocity zone in the south and a high‐velocity zone in the north with an average thickness of accreted sediments of 22 and 30 km, respectively. The young age of the surface rocks of the high‐velocity part of the prism suggests the presence of a significant volume of igneous rocks and metamorphosed sedimentary rocks. The velocity model indicates a continental crust of ∼40 km with a thick sedimentary cover of ∼20 km for the eastern part of Jaz Murian Depression. The presence of a NE‐SW trending low‐velocity region at a depth interval of 40–60 km subparallel with the trend of the volcanic arc, intermediate‐depth earthquakes, and geometry of the overriding plate might be related to the trend of the subducting plate hinge. This implies that the observed NE‐SW trending volcanic arc might be related to the geometry of the subducting plate hinge and not the eastward reduction of the subduction angle. Plain Language Summary: We use ambient‐noise tomography to study shear‐wave velocity variations in the onshore part of the Central Makran subduction zone using data from a 4‐year‐long temporary seismic network installed at the border of western and eastern Makran. The variations of shear‐wave velocity are related to the main geological and structural features of the crust. Knowledge of the crustal structures present in the overriding plate and the accretionary prism are essential inputs for accurate estimation of earthquake hazard in the study area. The calculated 3D shear‐wave velocity model shows that the wide onshore accretionary prism of Makran subduction zone is made up of southern low‐ and northern high‐velocity regions, and the fore‐arc Jaz Murian Depression is underlined by a continental crust of ∼40 km thick with a sedimentary cover of ∼20 km. We also found a NE‐SW low‐velocity zone centered at a depth of ∼50 km and subparallel to the trend of volcanic arc and intermediate‐depth earthquakes. We attributed the trend of the low‐velocity zone to the trend of the subducting plate hinge. Key Points: We calculate a 3D shear‐wave model in Central Makran using Ambient‐noise adjoint tomographyThe onshore accretionary prism consists of a southern low‐velocity and a northern high‐velocity zonesJaz Murian Depression is underlined by a continental crust of ∼40 km thick with a sedimentary cover of ∼20 km [ABSTRACT FROM AUTHOR]

Tectonic activity, geothermal fluids, and microseismic events (MSEs) tend to occur in similar locations as a result of spatiotemporal changes in the subsurface stress state. To quantify this association, we analyze data from a dense seismic array deployed at the San Emidio geothermal field, Nevada for 1 week in December 2016 to coincide with a 19.45‐hr shutdown of all injection and production pumping operations. 123 MSEs were detected, of which 101 occurred during the shutdown. The spatial association of the MSEs with the production wells suggests a causal relationship between the production cessation and the MSEs. Here we performed a detailed analysis to investigate reservoir material properties, distribution of seismically activated faults, and local stress state. We determined the hypocenters, magnitudes, and focal mechanisms for the MSEs, P‐wave tomographic velocity model, and local stress tensor. The results show that most MSEs occurred near the production wells. Magnitudes fall between −2.2 and 0.0 with larger events located closer to the production wells. Most MSEs occurred within a westward‐dipping normal fault zone in the reservoir associated with anomalously low P‐wave velocity values. The focal mechanism and stress inversion results show predominantly normal faulting with the maximum horizontal stress oriented north‐south. We suggest that the MSEs during shutdown were triggered on pre‐existing, small‐scale, critically stressed fault patches in the reservoir as the pore pressure increased around the production wells when the production pumping ceased. We interpret the larger MSE magnitudes closer to the production wells as a result of higher pore pressure increase. Plain Language Summary: Microseismic events (MSEs) associated with production and injection of oil, gas, and water have been observed in many locations. In December 2016, a seismic array with 1,302 instruments was deployed at the geothermal field at San Emidio, Nevada, USA to coincide with a 19.45‐hr time interval when all pumping operations were temporarily shut down. The seismic network detected 123 MSEs, of which 101 occurred during the shutdown. To understand the physical mechanism driving these MSEs, we perform a detailed seismic and stress analysis with the seismic data collected at San Emidio in 2016. Our analysis includes estimating the hypocentral locations, magnitudes, and focal mechanisms of the microseismic events, forming 3‐dimensional images of P‐wave velocity via seismic tomography, and calculating the orientations of the principal axes of the local stress tensor. Our results show that most MSEs occurred within a normal fault zone in a fluid‐filled reservoir near the production wells. We suggest that the temporary cessation of production pumping increased the pore pressure, decreased the effective stress on the pre‐existing, critically stressed fault patches and fractures, and thereby enhanced the microseismicity. Key Points: We performed a detailed seismic analysis of microseismicity during a short, planned pumping cessation at the San Emidio geothermal fieldThe microseismicity likely occurred on critically stressed faults and fractures due to pore pressure increase when production pumping ceasedThere may be a positive correlation between pore pressure increase and microseismic event magnitude [ABSTRACT FROM AUTHOR]

Imaging the structure of major fault zones is essential for our understanding of crustal deformations and their implications on seismic hazards. Investigating such complex regions presents several issues, including the variation of seismic velocity due to the diversity of geological units and the cumulative damage caused by earthquakes. Conventional migration techniques are in general strongly sensitive to the available velocity model. Here we apply a passive matrix imaging approach which is robust to the mismatch between this model and the real seismic velocity distribution. This method relies on the cross‐correlation of ambient noise recorded by a geophone array. The resulting set of impulse responses form a reflection matrix that contains all the information about the subsurface. In particular, the reflected body waves can be leveraged to: (a) determine the transmission matrix between the Earth's surface and any point in the subsurface; (b) build a confocal image of the subsurface reflectivity with a transverse resolution only limited by diffraction. As a study case, we consider seismic noise (0.1–0.5 Hz) recorded by the Dense Array for Northern Anatolia that consists of 73 stations deployed for 18 months in the region of the 1999 Izmit earthquake. Passive matrix imaging reveals the scattering structure of the crust and upper mantle around the North Anatolian Fault zone over a depth range of 60 km. The results show that most of the scattering is associated with the Northern branch that passes throughout the crust and penetrates into the upper mantle. Plain Language Summary: Investigating the structure of major fault zones is important to understand the deformations of the Earth's crust and their potential impact on future earthquakes. However, the large variations of seismic velocity between different geological units drastically hampers the ability to image those areas. The North Anatolian Fault is no exception since it splits into two branches separating three major geological blocks in the region of the 1999 Izmit earthquake. To image the deep structure of this fault, seismic noise is here exploited to retrieve information on the body waves reflected by the underground heterogeneities. A three‐dimensional map of the subsurface reflectivity is then built by applying methods originally developed in optical microscopy and ultrasound for deep imaging inside complex media. The inner structure of the Earth around the North Anatolian Fault is revealed. In particular, the Northern branch is shown to exhibit a strong damage pattern and a deep penetration inside the upper mantle. Key Points: A matrix approach of passive seismology provides 3D imaging of the North Anatolian Fault, Turkey, over a depth range of 60 kmTransmission matrix between the Earth surface and the underground is extracted by means of spatio‐temporal correlations of seismic noiseTime reversal of this transmission matrix enables a sharp compensation of the seismic velocity variations in the fault area [ABSTRACT FROM AUTHOR]

The Granada Basin in southeast Spain is an area of moderate seismicity. Yet, it hosts some of the highest seismic hazards in the Iberian Peninsula due to the presence of shallow soft sediments amplifying local ground motion. In urban areas, seismic measurements often suffer from sparse instrumentation. An enticing alternative to conventional seismometers is the distributed acoustic sensing (DAS) technology that can convert fibre-optic telecommunication cables into dense arrays of seismic sensors. In this study, we perform a shallow structure analysis using the ambient seismic field interferometry method. We conduct a DAS array field test in the city of Granada on the 26 and 27 August 2020, using a telecommunication fibre. In addition to the existing limitations of using DAS with unknown fibre-ground coupling conditions, the complex geometry of the fibre and limited data recording duration further challenge the extraction of surface-wave information from the ambient seismic field in such an urban environment. Therefore, we develop a processing scheme that incorporates a frequency–wavenumber (f − k) filter to enhance the quality of the virtual shot gathers and related multimode dispersion images. We are able to use this data set to generate several shear-wave velocity (VS) profiles for different sections of the cable. The shallow VS structure shows a good agreement with different geological conditions of soil deposits. This study demonstrates that DAS could provide insights into soil characterization and seismic microzonation in urban areas. In addition, the results contribute to a better understanding of local site response to ground motion. [ABSTRACT FROM AUTHOR]

The seismometer Seismic Experiment for Interior Structure (SEIS) onboard the InSight lander was used to continuously record the seismicity on Mars from February 2019 to December 2022. To maximize the information that can be extracted from the seismic data, it is critical to identify and to suppress undesired features (e.g., environmental noise, scattered waves, seismic imprint of lander vibrations) and non‐seismic noise (e.g., instrument related artifacts). We present an advanced polarization filtering workflow in the time‐frequency domain to suppress undesired features and to enhance the signal‐to‐noise ratio of the SEIS recordings. We estimate time‐frequency‐dependent polarization attributes such as the ellipticity, directionality of the particle motion, and the degree of polarization to identify and filter out undesired data parts. After filtering in the time‐frequency domain, the seismic data are transformed back to the time domain, yielding broadband waveform data that can be used for further seismological analysis. We illustrate the benefits of our filtering approach with three use cases. Firstly, we show how polarization filtered data can help to constrain the source mechanism of the sol 1,222 event, the largest marsquake detected so far. Using the proposed polarization filtering techniques, we are able to enhance the S‐wave arrival by suppressing interfering randomly polarized scattered waves to successfully infer on the moment tensor of this event. Secondly, we show that polarization filters can be used to suppress instrument‐related glitches and, thirdly, to remove the seismic imprint left by the vibrating lander (mechanical resonances of the lander). Plain Language Summary: The NASA InSight mission brought a seismometer to the surface of Mars that continuously monitored ground vibrations at the landing site. The goal was to record vibrations caused by marsquakes. Since seismic waves from marsquakes travel through the planet, such recordings contain information that allows scientists to image the interior structure of Mars. The signals from most of the quakes recorded by InSight are faint due to their low magnitudes and the large epicentral distance. Additionally, the recordings are contaminated by noise from local winds and the harsh temperature conditions on Mars lead to sudden stress relaxations inside the seismometer that are visible as distinct pulses (so‐called glitches) in the data stream. To accurately image the interior structure of the planet, it is of crucial importance to distinguish and separate such local, distorting signals from signals that originate from deeper parts of the planet. To do so, we make use of a method called polarization filtering, which is based on the analysis of the motion of the seismometer in three‐dimensional space as a function of time and frequency. We show that this motion is distinct for environmental noise, glitches, and marsquake signals and exploit it to clean the data from local disturbances. Key Points: InSight's Seismic Experiment for Interior Structure (SEIS) data are contaminated by various types of seismic and non‐seismic noise componentsTime‐frequency domain polarization filtering allows one to automatically identify and suppress undesired noise componentsPolarization‐filtered waveform data enable a more stable moment tensor inversion and facilitate the interpretation of SEIS data [ABSTRACT FROM AUTHOR]

Microseismic signals are characterized by a low signal-to-noise ratio and a high degree of non-stationary noise. Therefore, attenuation of noise in the microseismic signal is a very challenging task. In this article, a hybrid denoising technique is proposed based on variational mode decomposition (VMD) with adaptive non-local means filtering. VMD is an adaptive signal processing technique that divides a signal into bandlimited intrinsic mode functions (BIMFs). To improve the algorithm's flexibility and finesse, sample entropy is applied to distinguish the levels of the BIMFs. These BIMFs are filtered adaptively using NLM. Further, these new BIMFs are added and filtered using soft thresholding. Initially, evaluation is carried out on a synthetic seismic signal to reveal the dominance of the suggested method against the other described denoising approaches. Then, the performance of the proposed method is verified on a real microseismic signal. The results reveal that the proposed method can effectively remove the noise and is superior to other described methods. [ABSTRACT FROM AUTHOR]

During the RESOLVE project ("High‐resolution imaging in subsurface geophysics: development of a multi‐instrument platform for interdisciplinary research"), continuous surface displacement and seismic array observations were obtained on Glacier d'Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d'Argentière. Using multiple bandpass‐filtered copies of the continuous seismic waveforms, we compute energy‐based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on‐ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity. Plain Language Summary: Alpine glaciers are a major component in the dynamic cryospheric environment. They are characterized by a multitude of processes occurring side by side, including but not limited to melt water flow, crevasse formation, and frictional basal sliding of the ice mass over the rigid and obstructive bedrock. Each of these processes generates distinctive acoustic signals that can be recorded by seismic instruments and the changing on‐ice motions are resolvable with Global Positioning System. Considering the rapidly changing glacial environment, there is an increasing need for reliable models to predict glacial dynamics to properly assess any associated hazard. Understanding basal sliding is of particular interest to this problem. Investigated here is how to overcome the challenge of describing glacier sliding using seismic signals since the records often contains multiple "loud" signals originating from associated surface processes within the glacier. To uncover specific processes occurring at the ice‐bedrock interface, we design a machine learning model to incorporate signals recorded on the glacier to predict the on‐ice surface motions. The results provide valuable insights into the spatiotemporal dynamics of an active Alpine glacier with the potential to contribute to a better understanding of the driving mechanisms of glacier sliding. Key Points: Seismic and Global Positioning System (GPS) data are examined to study physical processes controlling glacial basal motionDecision tree model uses beamforming catalog and statistical features of time series to constrain correlations with GPS recorded motionsModel features indicate glacial on‐ice velocity is modulated by basal motions [ABSTRACT FROM AUTHOR]

Slow earthquakes occur at deep and shallow plate boundaries along the Nankai Trough. Deep slow earthquakes are continuously distributed along the 30–40 km depth contours of the upper surface of the subducted Philippine Sea Plate. In contrast, shallow slow earthquakes occur in limited regions: Hyuga-nada, off Cape Muroto, and southeast off the Kii Peninsula. This review provides an overview of the up-to-date seismological, geodetic, geological, and experimental results in the shallow Nankai area for a unified understanding of the spot-like occurrence of shallow slow earthquakes. Shallow slow earthquakes tend to be distributed in transitional regions between the frictionally locked and stably sliding zones on the plate boundary. Based on geological and experimental studies, the lithology of incoming sediments and their friction coefficients can be variable along the Nankai Trough. Laboratory friction experiments revealed that sediments under shallow plate boundary conditions often exhibit positive (a − b) values, while negative (a − b) is possible via several processes. Subducted seamounts create complex fracture networks and stress shadows in their surrounding areas; however, not all subducted seamounts are related to shallow slow earthquake activities. This incomplete correlation suggests that alternative factors are required to explain the spot-like distribution of shallow slow earthquakes in the Nankai subduction zone. High pore fluid pressure conditions around shallow slow earthquake zones were interpreted based on seismological structural studies. In addition, ambient noise monitoring revealed temporal changes in seismic velocity structures associated with shallow slow earthquake migrations. This result suggests a close link between pore fluid migration and shallow slow earthquake episodes. Because transient changes in pore fluid pressure can lead to various slip behaviors, the episodic migration of pore fluid around the plate boundary could promote shallow slow earthquake activity along the Nankai Trough. [ABSTRACT FROM AUTHOR]

We present the 1D subsoil structure and local site effects at KUMA strong ground motion station in Kumamoto City, Japan. We analyze data from a field campaign conducted in the framework of the Blind Prediction BP1 test of the 6th IASPEI/IAEE International Symposium: Effects of Surface Geology on Seismic Motion. In parallel with other participants of the BP1 test, we process data from passive and active source measurements aiming to determine the shear-wave velocity, Vs, structure and the site response at KUMA station. Passive measurements are associated to five microtremor arrays. In each array, seven seismometers have been deployed in a common-center triangle shape, recording microtremors simultaneously for 1 to 2 h. The vertical component of microtremors was analyzed using the spatial autocorrelation (SPAC) method. Cross-correlation coefficients were computed for all station pairs available for each array. By fitting the average SPAC's coefficients to the first-kind zero-order Bessel function, J0, and assuming that microtremors primarily comprise fundamental mode Rayleigh waves, phase velocity dispersion curves were determined. Phase velocity values for frequencies > 15 Hz were obtained from data of a close-by active source geophone profile. We integrated the data with those of the passive measurements and obtained an experimental phase velocity dispersion curve. The resulting curve shows low velocity variation, from 150 to 200 m/s, in the surface layers, whereas significant dispersion appears in frequencies below 2.5 Hz. By inverting this curve, we achieved to determine the 1D shear-wave velocity structure at KUMA station. Site response characteristics were determined by applying the Horizontal-to-Vertical-Spectral-Ratios method. Significantly amplified peaks in the frequency range between 0.3 to 1.5 Hz dominate HVSR spectral ratios. These peaks correspond to resonant frequencies of soils and originate from different impedance contrasts within the substratum of the site. [ABSTRACT FROM AUTHOR]

Fault reactivation and associated microseismicity pose a potential threat to industrial processes involving fluid injection into the subsurface. In this research, fracture criticality, defined as the gradient of critical fluid pressure change to trigger seismicity (Δpc/h), is proposed as a novel reservoir depth-independent metric of fault slip susceptibility. Based on statistics of the fracture criticality, a probabilistic evaluation framework for susceptibility to injection-induced seismicity was developed by integrating seismic observations and hydrogeological modelling of fluid injection operations for faulted reservoirs. The proposed seismic susceptibility evaluation method considers the injection-driven fluid pressure increase, the variability of fracture criticality, and regional fracture density. Utilising this methodology, the probabilistic distribution of fracture criticality was obtained to evaluate the potential for injection-induced seismicity in both fault and off-fault zones at the Hellisheiði geothermal site, Iceland. It has been found that the fracture criticality within both fault and off-fault zones shows natural variability (mostly ranging between 0.001 and 2.0 bar/km), and that fault zones tend to be characterised by larger fracture criticality values than the off-faut zones. Fracture criticality values estimated within each zone roughly follow a Gaussian distribution. Fault zones around five geothermal fluid re-injection wells at the site were estimated to have relatively high probability of seismic event occurrence, and these regions experienced high levels of induced seismicity over the microseismic monitoring period. The seismotectonic state estimated for each zone is generally consistent with the forecasted susceptibility to seismicity based on statistics of fracture criticality. Highlights: A probabilistic seismic susceptibility evaluation framework which integrates seismic observations and hydrogeological modelling is proposed. Fracture criticality is proposed as a novel reservoir depth-independent metric for fracture slip susceptibility. Fracture criticality values estimated at Hellisheiði roughly follow a Gaussian distribution ranging between 0.001 and 2.0 bar/km. Satisfactory probabilistic seismic susceptibility evaluation results were achieved at Hellisheiði. [ABSTRACT FROM AUTHOR]

Seismic signals are inevitably disturbed by random noise in the acquisition process, which greatly degrades seismic data. In order to improve the quality of seismic data, we propose a self-similarity convolutional neural network (SS-Net) for seismic data denoising by introducing the coherence of seismic events into convolutional neural network (CNN). The SS-Net consists of two modules, the directional matching module (DMM) and the denoising module. The DMM stacks similar seismic data blocks to generate three-dimensional (3D) groups by calculating the similarity between seismic data blocks with the same directional characteristics. For the 3D groups with redundant structural information, the following denoising module with the multi-channel convolution adaptively extracts and squeezes the structural feature characteristic of each 3D group, which enhances the characteristics of seismic signals and avoids confusion caused by local similarity of seismic signals and random noise. In addition, the skip connection is adopted by SS-Net to transport the sparse feature to the following denoising process, to reduce the loss of signal features extracted by multi-channel convolutional layers due to increased network depth. We validate the denoising performance of the SS-Net on the synthetic and field desert seismic data. The filtered results confirm that the SS-Net can suppress seismic random noise more thoroughly and recover the seismic events with complex morphology better than the competitive denoising methods. [ABSTRACT FROM AUTHOR]

In this paper, a method is proposed for estimating the intensity distribution of microtremors for the arrival direction, from simultaneous observations of vertical motion, using an array of simple geometry consisting of only three receivers. This method is based on the fact that the complex coherence function, which is defined as the normalized cross-spectrum of microtremor recordings observed at two sites, contains information about the azimuthal intensity distribution of the microtremor field. To obtain the azimuthal intensity distribution from a limited number of conditions, the functional form of the azimuthal intensity distribution of the microtremor field was assumed and its parameters were estimated. Information on the arrival direction of the microtremor field was specifically obtained using particle swarm optimization (PSO), one of the metaheuristic methods, and then employed to identify the parameters. The proposed method was applied to ideal synthetic data to clarify the potential problems that may arise from using PSO as the core algorithm. Despite PSO-induced errors, the proposed method was able to accurately infer directional information over a wide frequency range. The proposed method was also applied to actual field data to verify its effectiveness and applicability. The results indicate that the proposed method can provide similar results to the frequency–wavenumber (FK) method in a wide frequency range. The quasi-FK spectrum, an effective mode representation of the FK spectrum, was displayed to visualize the directionality. [ABSTRACT FROM AUTHOR]

The Ţicău area (NW Romania) is part of the collisional zone between the Tisza and Dacia mega‐units. Imaging of Ţicău area's subsurface geological structure will contribute to the understanding of the processes, which controlled its tectonic evolution. Analysis of outcrops provides accurate information for the building of kinematic sections used to explain the small‐scale deformations of sedimentary formations, such as those developed south of the Ţicău mountains. Imaging of large‐scale deformations requires analysis of data recorded and/or measured over a wider area. We use Seismic Interferometry by cross‐correlation on ambient noise recorded along with a line located north of the Ţicău mountains. The passive seismic section obtained after virtual body‐wave reflections processing displays folded and faulted Paleogene formations, which are in good agreement with the observations on outcrops found to the south of the Ţicău mountains. The active‐source data recorded along with the same line are too noisy for interpretation. [ABSTRACT FROM AUTHOR]

The article discusses the Southwest China Community Velocity Model (SWChinaCVM) and its significance in understanding active tectonics and seismic hazards in the Sichuan-Yunnan region. The SWChinaCVM 2.0, an improved version of the model, offers better spatial coverage, accuracy, and resolution compared to the previous version. By incorporating data from seismic stations and arrays, the 2.0 version provides a more refined velocity model and a deeper understanding of seismogenic structures. The article also suggests future upgrades for the model, which holds promise for comprehensive studies on earthquake physics and mitigation of strong earthquake disasters in the region. [Extracted from the article]

The Sichuan-Yunnan area is located at the southeastern margin of the Tibetan Plateau, where tectonic movement is strong with deep and large faults distributed in a staggered manner, which results in strong seismic activities and severe earthquake hazards. Since the 21st century, several earthquakes of magnitude 7.0 or above occurred in this region, which have caused huge casualties and economic losses, especially the 2008 Ms8.0 Wenchuan earthquake. At present, earthquake monitoring and source parameter inversion, strong earthquake hazard analysis and disaster assessment are still the focus of seismological researches in the Sichuan-Yunnan region. Regional high-precision 3D community velocity models are fundamental for these studies. In this paper, by assembling seismic observations at permanent seismic stations and several temporary dense seismic arrays in this region, we obtained about 7.06 million body wave travel time data (including absolute and differential travel times) using a newly developed artificial intelligence body wave arrival time picking method and about 100,000 Rayleigh wave phase velocity dispersion data in the period range of 5–50 s from ambient noise cross-correlation technique. Based on this abundant dataset, we obtained the three-dimensional high resolution Vp and Vs model in the crust and uppermost mantle of southwest (SW) China by adopting the joint body and surface wave travel time tomography method considering the topography effect starting from the first version of community velocity model in SW China (SWChinaCVM-1.0). Compared to SWChinaCVM-1.0, this newly determined velocity model has higher resolution and better data fitness. It is accepted by the China Seismic Experimental Site as the second version of the community velocity model in SW China (SWChinaCVM-2.0). The new model shows strong lateral heterogeneities in the shallow crust. Two disconnected low velocity zones are observed in the middle to lower crust, which is located in the Songpan-Ganzi block and the northern Chuandian block to the west of the Longmenshan-Lijiang-Xiaojinhe fault, and beneath the Xiaojiang fault zone, respectively. The inner zone of the Emeishan large igneous province (ELIP) exhibits a high velocity anomaly, which separates the two aforementioned low velocity anomalies. Low velocity anomaly is also shown beneath the Tengchong volcano. The velocity structures in the vicinity of the 2008 Ms8.0 Wenchuan earthquake, the 2013 Ms7.0 Lushan earthquake and the 2017 Ms7.0 Jiuzhaigou earthquake mainly show high Vp and Vs anomalies and the mainshocks are basically located at the transition zone between the high and low velocity anomalies. Along with the segmentation characteristics of seismic activity, we suggest that areas with significant changes in velocity structures, especially in active fault zones, might have a greater potential to generate moderate to strong earthquakes. [ABSTRACT FROM AUTHOR]

We performed laboratory tests and field surveys to evaluate the performance of a low‐cost 3‐component seismometer, consisting of three passive electromagnetic spring‐mass sensors, whose 4.5 Hz natural frequency is extended down to 0.5 Hz thanks to hyper damping. Both lab and field datasets show that the −3 dB band of the seismometer ranges approximately from 0.7 to 39 Hz, in agreement with the nominal specifications. Median magnitude frequency response curves obtained from processing field data indicate that lower corner of the −3 dB band could be extended down to 0.55 Hz and the nominal sensitivity may be overestimated. Lab results confirm the non‐linear behavior of the passive spring‐mass sensor expected for high‐level input signals (a few to tens of mm/s) and field data confirm relative timing accuracy is ±10 ms (1 sample). We found that absolute timing of data collected with USB GPS antennas can be affected by lag as large as +0.5 s. By testing two identical units, we noticed that there could be differences around 0.5 dB (i.e., about 6%) between the components of the same unit as well as between the same component of the two units. Considering shallow passive seismic applications and mainly focusing on unstable slope monitoring, our findings show that the tested seismometer is able to identify resonance frequencies of unstable rock pillars and to generate interferograms that can be processed to estimate subsurface velocity variations. Plain Language Summary: This study describes some tests that we did to evaluate a seismometer that is cheaper than similar products on the market. A seismometer is able to sense and collect seismic waves and can be used for several applications including global seismology and hydrocarbon exploration. In our work, we consider passive seismic applications, that is, we focus on seismic waves generated by non‐controlled sources (aka seismic noise). Either the seismic sources are natural or man‐made, a valuable seismometer should allow to record weak signals in a wide frequency band, especially at relatively low frequency (<5 Hz). The results of our tests show that the cheap seismometer can record frequencies down to approximately 0.5 Hz, while, to keep costs low, the highest frequency is limited to about 40 Hz. Field tests show that the seismometers can retrieve information from seismic noise as weak as a few micrometers per second, while lab test with higher inputs shows that the response of the seismometer is dependent upon the input velocity. Overall, we found that the nominal specifications of the seismometers are met, thus the tested unit is a valuable tool for shallow passive‐seismic applications with relatively‐low‐frequency, low‐amplitude signals, and a limited budget. Key Points: A low‐cost 3‐component seismometer has been tested for passive shallow applicationsThe seismometer has non‐linear response for high‐amplitude (mm/s) excitation signalsLab and field tests confirm the −3 dB band ranges from 0.7 to 39 Hz and an offset of about 0.5 s was found in the filed data whose timing was provided by a USB GPS antenna [ABSTRACT FROM AUTHOR]

On September 16, 2021, a MS6.0 earthquake struck Luxian County, one of the shale gas blocks in the Southeastern Sichuan Basin, China. To understand the seismogenic environment and its mechanism, we inverted a fine three-dimensional S-wave velocity model from ambient noise tomography using data from a newly deployed dense seismic array around the epicenter, by extracting and jointly inverting the Rayleigh phase and group velocities in the period of 1.6-7.2 s. The results showed that the velocity model varied significantly beneath different geological units. The Yujiasi syncline is characterized by low velocity at depths of ~ 3.0-4.0 km, corresponding to the stable sedimentary layer in the Sichuan Basin. The eastern and western branches of the Huayingshan fault belt generally exhibit high velocities in the NE-SW direction, with a few local low-velocity zones. The Luxian MS6.0 earthquake epicenter is located at the boundary between the high- and low-velocity zones, and the earthquake sequences expand eastward from the epicenter at depths of 3.0-5.0 km. Integrated with the velocity variations around the epicenter, distribution of aftershock sequences, and focal mechanism solution, it is speculated that the seismogenic mechanism of the main shock might be interpreted as the reactivation of pre-existing faults by hydraulic fracturing. [ABSTRACT FROM AUTHOR]

In active underground mining environments, monitoring mine vibrations has important implications for both safety and productivity. Microseismic data processing is crucial for subsurface real-time monitoring during mineral mining processes. Microseismic events are difficult to detect due to their small magnitudes and low signal-to-noise ratios (SNRs). Useful microseismic signals are usually obscured by long-period microseisms, random noise and artificial strong noise. We propose a useful microseismic denoising algorithm based on the normal time–frequency transform (NTFT) to determine the instantaneous frequency, amplitude and phase information from useful microseismic signals. The energy difference in the time–frequency domain between useful microseismic signals and strong noise is small. Therefore, based on the different phase characteristics of microseismic signals and noise in the NTFT phase spectrum, noise can be filtered out by reconstructing the microseismic signals in useful real-time frequency bands. The proposed simple bandpass filtering (SBPF) method is advantageous because the denoising result does not produce phase shifts, energy leakage or artefacts. The only parameter of the proposed method that needs to be defined is the instantaneous cutoff frequency; thus, the denoising operation is simple. We use both synthetic and real data to demonstrate the feasibility of the method for denoising complicated microseismic datasets. [ABSTRACT FROM AUTHOR]

The underground pressure disaster caused by the exploitation of deep mineral resources has become a major hidden danger restricting the safe production of mines. Microseismic monitoring technology is a universally recognized means of underground pressure monitoring and early warning. In this paper, the wavelet coefficient threshold denoising method in the time–frequency domain, STA/LTA method, AIC method, and skew and kurtosis method are studied, and the automatic P-phase-onset-time-picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized, the reliability of the P-phase-onset-time-picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity in the underground mine, which is of great significance to the data processing and analysis of microseismic monitoring. [ABSTRACT FROM AUTHOR]

The Italian strong-motion network monitors the seismic activity in the region, with more than 585 stations with continuous data acquisition. In this study, we determine the background seismic noise characteristics of the network by using the data collected in 2022. We analyse the spatial and temporal characteristics of the background noise. It is found that most of the stations suffer from anthropogenic noises, since the strong-motion network is designed to capture the peak ground motions in populated areas. Hence, human activities enrich the low periods of noise. Therefore, land usage of the area where the stations are located affects the background noise levels. Stations can be noisier during the day, up to 12 dB, and during the weekday, up to 5 dB, in short periods. In long periods (≥ 5 s), accelerometric stations converge to similar noise levels and there are no significant daily or weekly changes. It is found that more than half of the stations exceed the background noise model designed for strong-motion stations in Switzerland by in at least one of the calculated periods. We also develop an accelerometric seismic background noise model for periods between 0.0124 and 100 s for Italy by using the power spectral densities of the network. The model is in agreement with the background noise model developed by using broadband data for Italy in short periods, but in long periods there is no correlation among studies. [ABSTRACT FROM AUTHOR]

Some of the most representative approaches for evaluating advanced technologies in seismic and volcanic monitoring are covering some of these topics: Machine Learning and AI: Advanced machine learning and artificial intelligence techniques are employed to process and analyze vast amounts of seismic data. Over more than a century of systematic observational seismology, the instrumental capabilities to detect, record, and analyze elastic waves and other physical variables associated with the source, medium, and recording site of earthquakes have evolved substantially. Blockchain for Data Security: Blockchain technology is being explored to ensure seismic data security and integrity, especially in critical applications such as early warning systems [[37], [39]]. Advanced Data Visualization: Data visualization tools, including virtual reality (VR) and augmented reality (AR), create immersive experiences for researchers and emergency responders, allowing them to explore seismic data in three dimensions [[28], [30]]. [Extracted from the article]

Hydraulic fracturing plays a crucial role in enhancing reserves and production of unconventional oil and gas resources. Injecting fracturing fluids into the ground to improve reservoirs also introduces the risk of inducing earthquakes; thus, monitoring the migration of these fluids is crucial. The microseismic positioning method determines the fracturing fluid by locating microseismic events generated by the fractured rock strata; however, this method is susceptible to errors. Low-resistivity subsurface fluids can directly change electromagnetic field signals, making the electromagnetic method a technically advantageous approach for monitoring the migration of hydraulic fracturing fluids. The monitoring test data of Well Eyangye-2HF show that the time-lapse controlled-source electromagnetic (CSEM) method is suitable for hydraulic fracturing and has good monitoring effects. The results of CSEM method can also compensate for deficiencies in microseismic monitoring. The electric field (Ex) observed using the CSEM method can directly predict the distribution edge of the fracturing fluid, and the anomalous zone of the Ex rate of change is consistent with the fracturing-fluid injection parameters and microseismic monitoring results. The analysis of field data and forward simulation, based on electrical logging results, led to the conclusion that hydraulic fracturing operations can cause changes in the resistivity of the target layer and surrounding strata. These changes are attributed to the synergistic effects of formation stress, temperature, and the fracturing fluid. The electric-field changes observed using the CSEM method may be caused by multiple factors; however, use of time-lapse CSEM for monitoring hydraulic fracturing is still feasible. [ABSTRACT FROM AUTHOR]

The waveform distortion exists in the noise-reduced microseismic signal, in order to achieve better noise reduction effect, a novel denoising method based on U-Net is proposed in this paper. The dilated convolution with expansion coefficient variation is introduced to established the U-Net convolutional neural networks noise attenuation model. The envelope entropy is introduced as a new loss function so as to realize unsupervised processing of field microseismic signals. The results of U-Net convolutional neural networks for noise attenuation(U-NetNA method) is compared with wavelet thresholding, time-frequency peaking, and convolutional neural network. The results show that: when the U-NetNA method is applied to noise attenuation of synthetic and real microseismic data, it is feasible and effective. Compared with other noise reduction methods, the U-NetNA can obtain richer effective signal features, more effectively suppress the noise, and improve the signal-to-noise ratio of microseismic signals. The result is informative for microseismic event identification, inversion localization and fracture interpretation. [ABSTRACT FROM AUTHOR]

A strong earthquake occurred on May 26, 2006, at 22:53:58 UTC, with a magnitude of Mw6.4. The shock was felt with an intensity of VI-VII MMI around Yogyakarta, Indonesia. The earthquake was presumably caused by the movement of the Opak Fault. Following the strong earthquake, seismic activity along the fault has remained high to this day. In order to explain the progression of seismic activity and understand the mechanism of the Opak fault, we conducted catalog relocation, focal mechanism inversion, and statistical analysis of the earthquake events from 2009-2021. The events were relocated using the Double Difference Method. To improve the accuracy of the focal mechanism inversion, we updated the 1-D velocity model from Crust 1.0 to a local velocity model. We inverted the mechanism of earthquakes with a magnitude of M=3.0. The results indicate that the recent hypocenters are clustered in the southeastern part of the Opak Fault. This cluster is located within the rupture zone of the Mw6.4 2006 mainshock, providing further evidence that postearthquake deformation from 2006 is still ongoing and primarily involves left-lateral oblique-slip faulting. The mechanism results are consistent with the observable morphological contrast on the surface. Cross-section plots of seismicity and dip angle, perpendicular to the mainshock strike, reveal a flower structure pattern, indicating a complex mechanism. The fault system is believed to be in the interseismic period, supported by the low bvalue. The suspicion is further strengthened by an increase in microseismic activity and a decrease in M>3.0. This evidence suggests that the Opak Fault is currently experiencing strain accumulation. [ABSTRACT FROM AUTHOR]

Ocean swell activities excite body‐wave microseisms that contain information on the Earth's internal structure. Although seismic interferometry is feasible for exploring structures, it faces the problem of spurious phases stemming from an inhomogeneous source distribution. This paper proposes a new method for inferring seismic discontinuity structures beneath receivers using body‐wave microseisms. This method considers the excitation sources of body‐wave microseisms to be spatially localized and persistent over time. To detect the P‐s conversion beneath the receivers, we generalize the receiver function analysis for earthquakes to body‐wave microseisms. The resultant receiver functions are migrated to the depth section. The detected 410‐ and 660‐km mantle discontinuities are consistent with the results obtained using earthquakes, thereby demonstrating the feasibility of our method for exploring deep‐earth interiors. This study is a significant step toward body‐wave exploration considering the sources of P‐wave microseisms to be isolated events. Plain Language Summary: The ocean waves excite persistent and random ground motions called microseisms. Since this excitation is independent of seismic activities, this wavefield has information about seismic structures that earthquakes never have. For the deep structure, such as the mantle and core, body‐wave microseisms are more suitable than surface‐wave microseisms because body‐wave microseisms have better sensitivity. Previous studies using body‐wave microseisms mainly adopted the cross‐correlation analysis known as seismic interferometry. This method assumes that the microseisms are excited everywhere. However, the inhomogeneous source distribution of body‐wave microseisms causes artifacts for exploration by seismic interferometry. We developed a new method which circumvents this problem. Assuming that the body‐wave microseisms are spatially isolated, this method extracted the P‐s converted waves beneath receivers from body‐wave microseisms. The 3‐Dimensional imaging result of extracted P‐s converted waves shows both 410‐ and 660‐km mantle discontinuities, consistent with results using earthquakes. This study shows the potential of body‐wave microseisms for exploring the deep earth structure. Key Points: The P‐S waves at mantle discontinuities were extracted from the ambient noise excited by the ocean swellsWe developed the source deconvolution method to generalize a receiver function method to P‐wave microseismsThe migration result of P‐S waves was consistent with previous studies, showing the potential of P‐wave microseisms to seismic structures [ABSTRACT FROM AUTHOR]

We would like to thank Wei and Shen (2023, https://doi.org/10.1029/2022GL102596) (W23) for their comments on our recent article on GRL. In their comments, Wei and Shen argued that tremor signal on the right side of the cross‐correlation function (CCF) of PAUD‐STCD is from Kilauea crater, and they suggested that the observed time‐delay after eruption might be attributed to the noise source change and other factors. After checking the data and the code carefully, we recognize that signal on the right side of CCF of PAUD‐STCD may be due to seismicity from the Kilauea crater. However, we demonstrate here that it does not affect our conclusions. Plain Language Summary: Wei and Shen (2023, https://doi.org/10.1029/2022GL102596) question that our velocity change measurement might be affected the volcanic tremor source change. By processing more data, we found that our results are stable. Key Points: Reply to comment by Wei and Shen (2023, https://doi.org/10.1029/2022GL102596) about the influence of the noise source for seismic velocity change measurement [ABSTRACT FROM AUTHOR]