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The extraction of geothermal energy is associated with induced seismicity. Depending on the extraction parameters, such as injection pressure and volume, the induced seismicity is time-dependent. We investigate the case of the two geothermal power plants of Insheim and Landau, which are located in the Upper Rhine Graben in Southwest Germany. The induced seismicity was observed by a local seismic network consisting of a total of 19 stations, resulting in an earthquake catalog comprising 930 events for the Landau reservoir and 1985 events for the Insheim reservoir, both between 2012 and 2022. Based on this earthquake catalog, seismic source areas are defined for both reservoirs, and a probabilistic seismic hazard assessment (PSHA) is performed. Using temporal subsets of the earthquake catalog, PSHA can also be performed for shorter time ranges, resulting in larger expected PGV values in times of higher induced seismicity. The seismic velocity profiles obtained by site effect studies based on ambient seismic noise measurements highlight relatively large variations of the site effects on short scales in the area. The consequences of these lateral variations on the seismic hazard assessment are also discussed. [ABSTRACT FROM AUTHOR]

Single-channel sparker seismic reflection systems are currently preferred in offshore geo-engineering studies due to their cost-effectiveness, ease of use in shallow areas, their high-resolution data, and straightforward data processing. However, the distinctive characteristics of sparker data introduce specific challenges in the processing of single-channel seismic datasets. These include (i) unavailability of the stacking process for single-channel seismic data, (ii) inability to derive subsurface velocity distribution from single-channel seismic profiles, (iii) limitations imposed by ghost reflections and bubble effects as well as random noise amplitudes, and (iv) the suitability of only predictive deconvolution for suppressing multiple reflections. Applications demonstrate that the inability to apply the stacking process to single-channel seismic data poses a significant challenge in suppressing both random and coherent noise, and increasing the signal-to-noise (S/N) ratio. The F-X prediction filter has proven highly effective in mitigating random noise in sparker data. Appropriate determination of operator length and prediction lag parameters allows predictive deconvolution to effectively suppress multiple reflections, despite some residual multiple amplitudes in the output. Spiking deconvolution significantly eliminates ghost reflections and bubble effects, enhancing temporal resolution by eliminating the ringy appearance of the input signal. Trace mixing is a crucial data processing step for enhancing sparker data resolution. The method can be applied as weighted mix for random noise suppression or as trimmed mix for suppressing high-amplitude spike-like noises. This study incorporates a comprehensive analysis of the various noise components embedded in sparker seismic data. It delineates the processing flow and parameters utilized to effectively mitigate these specific noise types. [ABSTRACT FROM AUTHOR]

Seismic data analysis often faces the challenge of random noise contamination from various sources. To overcome this, innovative noise attenuation methods utilizing seismic signal properties are needed. This study focuses on efficiently suppressing random noise in the domain of time and frequency by accurately estimating instantaneous frequency using the single-valued group delay characteristic of seismic signals. The time-reassigned synchrosqueezing transform (TSST) and its second-order variant (TSST2) offer high-resolution time-frequency representations (TFRs) for noise suppression. Expanding on these advancements, we propose an efficient noise suppression method that integrates the adaptive thresholding model into the TSST2 framework and employs sparse representation of the TFR through low-rank estimation. This method effectively attenuates noise while preserving essential signal information. The proposed approach operates trace by trace on recorded data, initially transforming it into a sparse subspace using TSST2. The adaptive thresholding model then decomposes the resulting TFR into sparse and semi-low-rank components, achieving a high-resolution and sparse TFR for efficient separation of noise and signal. After noise suppression, the seismic data can be fully reconstructed by inversely transforming the semi-low-rank component data into the time domain. This method addresses previous limitations in noise attenuation techniques and provides a practical solution for enhancing seismic data quality. [ABSTRACT FROM AUTHOR]

The Xinfengjiang Reservoir in Guangdong, China, triggered one of the largest reservoir-induced earthquakes in the world, with a magnitude of 6.1 in 1961 March. Frequent local seismic events have been recorded in the vicinity of the reservoir, posing a potential threat to the dam infrastructure and surrounding regions. In this study, we obtain a high-resolution S -wave velocity model of the upper crustal structure underlying the Xinfengjiang Reservoir, using double beamforming tomography method from newly deployed dense array across the reservoir. Our results reveal that several high-velocity structures are distributed beneath the Xinfengjiang Reservoir. These structures are robust, facilitating a greater accumulation of stress before fault slip. Earthquakes beneath the reservoir are primarily concentrated at the boundaries of the high-velocity bodies, indicating their controlling role on the location of seismic events. Low-velocity zones, acting as pathways for fluid migration, extend from the surface to the depths of seismic sources, thereby inducing earthquakes by elevating pore pressure within fault and fracture systems. Due to significant differences in the fluid diffusion coefficient, the delay times between the onset of earthquakes and the peak water levels vary considerably with the different crustal structures. [ABSTRACT FROM AUTHOR]

Signal and noise classification can add an extra level of constraint for earthquake phase picking by pinpointing the signal waveforms from continuous seismic data for more accurate arrival picking. However, the continuously increasing data collected by worldwide stations exceeds the ability of manual analysis. Moreover, manual earthquake data analysis depends on seismologists' expert knowledge, resulting in inconsistent analysis results. To address this, we proposed a generalized deep learning (DL) network architecture to discriminate earthquake signal and noise waveforms. The proposed DL framework is a novel architecture comprising a feature extractor, a classifier and two hybrid attention modules. It utilizes different kernel sizes for more detailed feature extraction, and the hybrid attention mechanism module can guide the network to focus more on the waveform characteristics. To illustrate the power of the proposed DL network, we applied it to classify the earthquake signal and noise of the 3-C Texas Earthquake Dataset. The results demonstrate that the accuracy of the proposed method in the testing set reaches 99.83 per cent. We further utilize the transfer learning strategy to demonstrate the transferability of the proposed network with the Stanford earthquake data set, showing an encouraging classification accuracy of 95.03 per cent. Additionally, we conducted an additional experiment on arrival picking by integrating decoder blocks into the classification network, which achieves remarkable P - and S -wave arrival picking accuracy. [ABSTRACT FROM AUTHOR]

This study analyzed ambient seismic noise using the MSNoise package to monitor temporal changes in the underground seismic wave velocity in Mining Area 6 of the Dongtan Coal Mine in China. The data was recorded continuously over 76 days by 7 three-component stations and 10 single-component microseismic stations deployed in Dongtan Coal Mine, with station spacing ranging from 0.1 km to approximately 3 km. Using the causal and non-causal components of the Z-component cross-correlation function, along with moving-window cross-spectrum analysis and cumulative calculations with a 5-day window overlay, stable seismic velocity changes were obtained in the frequency band of 0.1 to 2 Hz. We found a correlation between the timing of average velocity changes and seismic events caused by underground mining processes. In particular, when the relative seismic velocity increased by 0.23%, larger energy minequakes typically occurred. This study shows that ambient noise correlation has great potential for predicting minequakes, guiding pressure-relief production, and providing warnings about the impact of overburden pressure. [ABSTRACT FROM AUTHOR]

Italy has been majorly affected by the COVID-19 pandemic. The government declared a full lockdown and limited human and commercial activities to keep the pandemic under control. The limited human activity reduced the spread of the virus and the cultural noise it created. The effect of the lockdown is detected by the Italian strong motion network, which covers the entire country with their stations mostly located in the settlements. To assess the effect of the lockdown, background noise information up to 1 s from 2022 is used as a comparison. It is found that the background noise levels dropped around 1.46 dB during the lockdown, with a nationwide reduction in almost all of the stations. Noise levels have dropped both in the daytime and nighttime during the lockdown, with a more significant noise drop during the nighttime, which can be linked to the ban on dining in restaurants and bars and the curfew. A similar trend is found in weekday and weekend comparisons; in both time ranges, 2022 was noisier regarding the lockdown period. Stations located in public spaces such as schools and city halls observed noise reduction of up to 7.99 dB, and this noise level reduction is visible in major cities. We analyzed the 10 most populated Italian cities and their surroundings and found noise reduction of up to 5.5 and 2.1 dB in the median. [ABSTRACT FROM AUTHOR]

Unstable rock slopes, prone to collapse, pose an increasingly severe threat to both people and infrastructures, necessitating effective monitoring for risk mitigation. While many techniques rely on surface displacements to assess slope stability, seismic indicators such as resonance frequency, variations in seismic wave velocity and site amplification offer valuable insights into the structural integrity of the slope, aspects not captured by surface deformation alone. Research has demonstrated that these site-response parameters can serve as monitoring tools to detect precursory signs of failure, such as a drop in resonance frequency and relative seismic wave velocity prior to collapse. Still, environmental factors like temperature, precipitation, snow melt, earthquakes and freeze-thaw cycles transiently influence the seismic response. Our main objective is to understand the correlations and drivers between environmental parameters and seismic response, distinguishing between reversible and irreversible changes in dynamic behaviour. Over a 5-yr monitoring period, we continuously recorded ambient vibration data at the Brienz/Brinzauls instability and monitored three different site-response parameters (resonance frequency, site amplification, relative seismic wave velocity variation) using enhanced frequency domain decomposition, site-to-reference spectral ratio and single station ambient vibrations correlation techniques. Our results highlight a long-term increase in site amplification and a long-term decrease in first and second resonance frequencies, indicating ongoing structural weakening. Temperature was found to correlate with seasonal variations of seismic wave velocity with a few day's time lag. Snow melting and rainfalls exerted a secondary influence, temporarily reducing relative seismic wave velocity during snowmelt and rainfall. Our findings suggest that single-station relative seismic velocity variations are mainly influenced by the shallow subsurface (depth of about 30 m), limiting its application to study the stability this deep structure. [ABSTRACT FROM AUTHOR]

A large portion of the stress release on seismic faults remains silent and undetected, requiring the development of novel observation techniques. Measuring traveltime perturbations from the correlation of ambient seismic noise at different stations is a well-known approach to assess temporal changes in seismic velocities, which can provide insights into hydrologic, tectonics and volcanic dynamic processes. In this work, we study the specific case of a P -wave phase retrieved from the correlation of freight train noise in Southern California and evaluate its potential to detect localized velocity changes along the San Jacinto Fault. We use a full waveform modelling approach to simulate this P -wave interference and further assess its sensitivity to the position of the train source, near-surface velocity changes and localized velocity changes in the fault zone. Our results show that the uncertainty in trains location can induce large traveltime biases which can be mitigated by averaging over many trains. Our results also highlight the weak sensitivity of these correlation P waves to near-surface velocity changes, while they show significant sensitivity to localized changes at depth. This modelling highlights the potential of monitoring traveltime perturbations of this ballistic P -wave interference to detect hidden slow-slip events on the San Jacinto Fault, particularly in identifying subtle velocity anomalies associated with fault zone changes that may otherwise go unnoticed by conventional seismic monitoring techniques. [ABSTRACT FROM AUTHOR]

The advancement of seismic methods is vital for mineral exploration in the ongoing energy transition. In this study, we investigate the application of ambient noise seismic interferometry and surface-wave analysis to characterize the subsurface in a mineral exploration context. We then confirm the results of the passive seismic investigation through an active source experiment. We collected ambient noise data using a 2-D seismic line initially deployed for an active source reflection seismic study. By cross-correlating the signals, we retrieved the surface waves and constructed a 2-D shear-wave velocity profile using conventional surface-wave analysis. We utilized the active source data to establish initial assumptions about the surveyed medium and then validated the passive seismic experiment. The passive seismic results are concordant with the active source results and allow for the interpretation of geological contacts and fault zones. Our work demonstrates the potential of passive seismic methods for investigating local tectonic settings and their role in hardrock mineral exploration. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) turns a fibre optic into a very dense network of equally distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyse DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatiotemporal and similarity features for the event classification, and Markov random field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalogue. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fibre optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fibre optic path, the proposed method succeeded in detecting earthquakes of magnitude >0.4 and quarry blasts of magnitude >1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fibre optic systems. [ABSTRACT FROM AUTHOR]

An accurate and efficient method for picking the first arrival of microseismic signals is crucial for processing microseismic monitoring data. However, the weak magnitude and low signal-to-noise ratio (SNR) of these signals make picking arrivals challenging. Recent advancements in deep learning-based methods for picking the first arrivals of microseismic signals have effectively addressed the inefficiencies and inaccuracies of traditional methods. Nevertheless, these methods often require many training samples, and the substantial size and labelling effort significantly hinder the development of deep learning-based first-arrival picking methods. This study introduces Semi-Picking: a semi-supervised method for picking the first arrival of microseismic signals, utilizing the TransUGA network and SimMatch. This approach automatically labels microseismic signals following sample augmentation by establishing a semi-supervised learning framework, significantly reducing the time required for sample labelling. Initially, the TransUNet model is enhanced by incorporating the Self-Supervised Predictive Convolutional Attention Block (SSPCAB) module to create a Deep-TransUNet architecture, which more effectively separates signal from noise in microseismic signals with low SNR and improves the accuracy of first-arrival picking. Subsequently, the data sets for this study are compiled from microseismic traces collected from field monitoring records. Finite-difference forward modelling is applied to the microseismic data to train the network, and hyperparameter tuning is performed to optimize the UGATIT and Deep-TransUNet architecture. The outcomes of the arrival-picking experiments, conducted under conditions of low SNR using both synthetic and real microseismic records, demonstrated that Semi-Picking offers robust resistance to incorrect labels. This resilience stems from the synergistic use of the semi-supervised learning framework and self-attention mechanisms. The proposed method demonstrates superiority over the TransUNet, the SSPCAB-TransUNet, the UNet++ and the traditional short-term average/long-term average method, respectively, with the picking error rate of the Semi-Picking Net being less than 0.1 s. The proposed method outperforms the commonly used deep learning-based approaches for picking the first arrivals of microseismic signals, exhibiting superior performance. [ABSTRACT FROM AUTHOR]

Post‐stack seismic profiles are images reflecting geological structures which provide a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post‐stack profiles suffer from low resolution and strong noise issues, which severely affects subsequent seismic interpretation. To better enhance the spatial resolution and signal‐to‐noise ratio of post‐seismic profiles, a multi‐scale attention encoder–decoder network based on generative adversarial network is proposed. This method improves the resolution of post‐stack profiles and effectively suppresses noises and recovers weak signals as well. A multi‐scale residual module is proposed to extract geological features under different receptive fields. At the same time, an attention module is designed to further guide the network to focus on important feature information. Additionally, to better recover the global and local information of post‐stack profiles, an adversarial network based on a Markov discriminator is proposed. Finally, by introducing an edge information preservation loss function, the conventional loss function of the Generative Adversarial Network is improved, which enables better recovery of the edge information of the original post‐stack profiles. Experimental results on simulated and field post‐stack profiles demonstrate that the proposed multi‐scale attention encoder–decoder network based on generative adversarial network method outperforms two advanced convolutional neural network‐based methods in noise suppression and weak signal recovery. Furthermore, the profiles reconstructed by the multi‐scale attention encoder–decoder network based on generative adversarial network method preserve more geological structures. [ABSTRACT FROM AUTHOR]

Fault detection is a crucial step in seismotectonic interpretation and oil–gas exploration. In recent years, deep learning has gradually proven to be an effective approach for detecting faults. Due to complex geological structures and seismic noise, detection results of such approaches remain unsatisfactory. In this study, we propose a hybrid network (NRA‐SANet) that integrates a self‐attention mechanism into a nested residual attention network for a three‐dimensional seismic fault segmentation task. In NRA‐SANet, the nested residual coding structure is designed to fuse multi‐scale fault features, which can fully mine fine‐grained fault information. The two‐head self‐attention decoding structure is designed to construct long‐distance fault dependencies from different feature representation subspaces, which can enhance the understanding of the model regarding the global fault distribution. In order to suppress the interference of seismic noise, we propose a fault‐attention module and embed it into the model. It utilizes the weighted and the separate‐and‐reconstruct channel strategy to improve the model sensitivity to fault areas. Experiments demonstrate that NRA‐SANet exhibits strong noise robustness, while it can also detect more continuous and more small‐scale faults than other approaches on field seismic data. This study provides a new idea to promote the development of seismic interpretation. [ABSTRACT FROM AUTHOR]

In this study, the shallow S-wave velocity structures beneath the Tainan area, Taiwan are investigated using microtremor array measurements at 18 sites. An F-K spectrum method was used to calculate the dispersion curves for these sites, and surface-wave inversion was used to estimate the S-wave velocity structures beneath the Tainan area. The results indicated that, at frequencies between 0.2 and 1.1 Hz, propagation is concentrated between the northwest and south quadrants, corresponding to the coast of the Taiwan Strait. CHM, a hard central site on the Tainan tableland, was found to have higher phase velocities than the soft sites do, such as site SIM, on the Anping Plain, particularly at frequencies of 0.3–5.0 Hz. Given that the tertiary basement exhibits an S-wave velocity of 1500 m/s, the depth of the Quaternary alluvium in the Tainan area ranges from 400 m at site CHM to 1350 m at site FUP on the Anping Plain. In addition, the depth of the alluvium gradually increases radially from the central CHM site (on the Tainan tableland) to the surrounding areas. These results are consistent with the geolpogy of the Tainan area. Because our shallow S-wave velocity structures are comparable to those proposed in three other studies, our inversion results are concluded to be reasonable and valid. According to the horizontal/vertical (H/V) ratios of our microtremor array data from 18 sites, the predominant frequencies range from 1.0 to 3.8 Hz. Compared with the eastern part (i.e., Dawan lowland), the central part (i.e., Tainan tableland) has higher predominant frequencies. [ABSTRACT FROM AUTHOR]

Airwave interference presents a major source of noise in seismic exploration, posing significant challenges to the quality control of raw seismic data. With the increasing data volume in 3D seismic exploration, manual identification methods fall short of meeting the demands of high-density 3D seismic surveys. This study employs the YOLOv5 model, a widely used tool in object detection, to achieve rapid identification of airwave noise in seismic profiles. Initially, the model was pre-trained on the COCO dataset—a large-scale dataset designed for object detection—and subsequently fine-tuned using a training set specifically labeled for airwave noise data. The fine-tuned model achieved an accuracy and recall rate of approximately 85% on the test dataset, successfully identifying not only the presence of noise but also its location, confidence levels, and range. To evaluate the model's effectiveness, we applied the YOLOv5 model trained on 2D data to seismic records from two regions: 2D seismic data from Ningqiang, Shanxi, and 3D seismic data from Xiushui, Sichuan. The overall prediction accuracy in both regions exceeded 90%, with the accuracy and recall rates for airwave noise surpassing 83% and 90%, respectively. The evaluation time for single-shot 3D seismic data (over 8000 traces) was less than 2 s, highlighting the model's exceptional transferability, generalization ability, and efficiency. These results demonstrate that the YOLOv5 model is highly effective for detecting airwave noise in raw seismic data across different regions, marking the first successful attempt at computer recognition of airwaves in seismic exploration. [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]

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]

Many traditional denoising methods, such as Gaussian filtering, tend to blur and lose details or edge information while reducing noise. The stationary wavelet packet transform is a multi-scale and multi-band analysis tool. Compared with the stationary wavelet transform, it can suppress high-frequency noise while preserving more edge details. Deep learning has significantly progressed in denoising applications. DnCNN, a residual network; FFDNet, an efficient, flexible network; U-NET, a codec network; and GAN, a generative adversative network, have better denoising effects than BM3D, the most popular conventional denoising method. Therefore, SWP_hFFDNet, a random noise attenuation network based on the stationary wavelet packet transform (SWPT) and modified FFDNet, is proposed. This network combines the advantages of SWPT, Huber norm, and FFDNet. In addition, it has three characteristics: First, SWPT is an effective feature-extraction tool that can obtain low- and high-frequency features of different scales and frequency bands. Second, because the noise level map is the input of the network, the noise removal performance of different noise levels can be improved. Third, the Huber norm can reduce the sensitivity of the network to abnormal data and enhance its robustness. The network is trained using the Adam algorithm and the BSD500 dataset, which is augmented, noised, and decomposed by SWPT. Experimental and actual data processing results show that the denoising effect of the proposed method is almost the same as those of BM3D, DnCNN, and FFDNet networks for low noise. However, for high noise, the proposed method is superior to the aforementioned networks. [ABSTRACT FROM AUTHOR]

Southeast Tibet provides a weak window for continental extrusion and plateau expansion, yet there is still limited understanding regarding specific deformation patterns in this region. Here, we construct a 3‐D crustal anisotropic S‐wave velocity model for the SE Tibetan Plateau. Our new observation retrieved from ambient noise interferometry sheds light on the belt of high‐velocity in the Emeishan Large Igneous Province. This belt is strengthened by the potential trajectory of the mantle plume and serves as a relatively rigid block, which obstructs the possible weak crustal materials from the northwest and transmits compressive stress onto the Xiaojiang fault system. With lower strength, the Xiaojiang fault region accommodates continental extrusion by preferentially deforming, while partial melting of the felsic mid‐lower crust shapes extensive low‐velocity anomalies. Within the context of the continental extrusion, lateral heterogeneity in lithospheric rigidity dominates the non‐uniform deformation of SE Tibet. Plain Language Summary: There is ongoing debate regarding the growth and deformation of the Tibetan plateau, with crustal flow and rigid block extrusion models being widely proposed. Southeast Tibet provides a natural laboratory for understanding the mechanism of plateau expansion. Using a technique called ambient noise tomography, which utilizes continuous seismic ambient noise to recover surface wave signals and invert for the velocity structure at different depths, we developed a high‐resolution 3‐D model at the crustal scale in SE Tibet. Our findings reveal that two independent low‐velocity anomalies in the middle crust are separated by a belt‐like high‐velocity zone. By combining pre‐existing geophysical, geodetic, geological, and geochemical observations, we suggest that the low‐velocity anomaly below the Xiaojiang fault system is generated by partial melting of local felsic crust, and the belt‐like high‐velocity zone is caused by the modification of the lithosphere by magma upwelling from the hot spots of the Permian mantle plume. Therefore, there is strong heterogeneity within the Yangtze craton, which resulted in non‐uniform deformation during subsequent Cenozoic plateau expansion. This mechanism is essential for understanding the plateau expansion and the plateau‐craton interactions. Key Points: A 3‐D azimuthally S‐wave velocity model of the crust was constructed from dense array ambient noise tomography in southeast TibetLow‐velocity anomaly within the Xiaojiang fault system primarily results from the partial melting of the in‐situ felsic mid‐lower crustIntense lateral heterogeneity in lithospheric rigidity dominates the non‐uniform deformation within the context of continental extrusion [ABSTRACT FROM AUTHOR]

In recent years, ambient seismic noise has gained considerable attention in seismology due to its potential to advance understanding of Earth's subsurface dynamics and monitor anthropogenic activities. This study presents a comprehensive review of ambient seismic noise research (ASNR), by exploring its current status, key research hotspots, and emerging trends through a bibliometric and visual analysis of 3,028 articles indexed in the Web of Science database (1984–2023). Using CiteSpace, the study systematically examines key authors, institutions, and countries, as well as thematic keywords and foundational references. Techniques such as cluster analysis, co-citation network analysis, and burst detection are employed to map the evolution of research fields and identify significant collaboration patterns. The analysis reveals a dramatic increase in research output, particularly since 2004, underscoring the expanding role of ASNR in geophysics, geochemistry, and engineering applications. Additionally, the rising interest in detecting human activities through seismic noise, especially in response to events like the COVID-19 pandemic, highlights the broadening scope of ASNR. Notably, the findings emphasize the pivotal importance of ambient noise tomography, a method that has transformed subsurface imaging techniques. This review not only synthesizes the current research landscape but also highlights critical gaps and emerging opportunities, providing a roadmap for future studies. In particular, it emphasizes advancements in seismic risk mitigation, geotechnical investigations, and the monitoring of human activities, offering a timely review and valuable insights that aligns with the interests of researchers in these fields. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

Distributed acoustic sensing (DAS) is an innovative technology with great potential for acquiring seismic data sets in urban areas. In this work, we check the suitability of a DAS data set acquired in Granada (Spain) for retrieving subsurface reflectivity from ambient noise. The fiber‐optic is a pre‐existing underground telecommunication cable that crosses the city from Northwest to Southeast. We use a 10 hr recording of strain rate from a 2020 experiment to obtain seismic reflections using the autocorrelation method. We compare the DAS results with reflections obtained from seismic ambient noise recorded in nine seismometers deployed close to the fiber‐cable for 7 days in November 2022. The novel approach proposed in this study for the identification of the reflections is to use autocorrelations after bandpass filtering for specific central frequencies and to check the stability of the signals over a broad frequency band. Microtremor Horizontal to Vertical Spectral Ratio (MHVSR) measurements at a total of 14 stations, five of them outside the city, help to constrain the reflection interpretation. These include one station at the borehole that reaches the basement in the Granada Basin crossing all the Cenozoic units. We use the legacy sonic log to obtain a relationship between frequencies of MHVSR peaks and depth. Autocorrelation and MHVSR methods give consistent results delineating bedrock depth deeper than 1,000 m in Granada. These results confirm that DAS can provide valuable subsurface information in urban areas. Plain Language Summary: Urban subsurface information is of paramount importance to understand how the underground affects the development of cities (groundwater or hazards such as earthquakes). One of the most useful methods to improve the knowledge of the city underground is seismic technique but installation of vibration sensors can be difficult in an urban area. We use in this work a cutting‐edge technology called Distributed Acoustic Sensing (DAS) that uses fiber‐optic cables in the city, such as preinstalled telecommunication cables, as a high number of seismic sensors. We compare the seismic results obtained from the measurements in the fiber‐optic cable and from conventional seismometers. The analysis of the vibrations acquired with DAS and seismometers provide reflections, like echo signals, from the subsurface changes. In particular we obtain a reflection coming from the contact between sediments and bedrock. The depth of this contact is similar with the two type of measurements reaching 1,400 m depth below the city. This confirms that this new technology can be used in cities to study their subsurface. Key Points: Distributed Acoustic Sensing holds great potential for urban seismic data acquisitionAutocorrelation and Microtremor Horizontal to Vertical Spectral Ratio methods consistently reveal bedrock depth exceeding 1,000 m in GranadaA novel approach is developed to analyze autocorrelation results for retrieving subsurface reflections [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]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic caused by the SARS-CoV-2 virus has brought many, mostly negative, effects and consequences on human lives, health, and economic issues. However, one of the very few positive side effects of lockdown measures was a less noisy and less loaded natural environment on a global scale. Many accurate geophysical instruments are negatively affected by the noise forced by all kinds of human activities, such as traffic, mining, construction, and others. In this paper, we present our attempt to estimate the in uence of anthropogenic noise in the seismic band on five superconducting gravimeters and one spring gravimeter incorporated in International Geodynamics and Earth Tide Service (IGETS) using a comparison of gravity records before and during the lockdown period. For quantification of a noise, the mean power spectral density (PSD) was used. Based on this experiment we can compare and quantify how much particular IGETS stations and instruments are affected by the anthropogenic noise. For our experiment, we used Level 1 IGETS data on selected stations with a 1-second sampling rate or 1-minute time resolution where 1-second was not available. For selected stations we estimated the contribution of anthropogenic noise to the total noise in a seismic frequency band for a particular station and instrument. [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]

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]

The Crati valley (Calabria, Italy) is a densely populated area characterized by high seismic hazard, as inferred from the strong earthquakes that occurred in the past centuries and from the tectonic structures that border the graben. The valley extends in the NS direction between Sila massif to the east and Catena Costiera to the west. In recent years the area has been characterized by low to medium magnitude earthquakes, mostly located in the upper crust. In this work we analyzed local and regional earthquakes and seismic noise to study the seismic response and the upper crust structure in the investigated area. We analyzed 12 h of seismic noise and 24 regional earthquakes to compute the horizontal-to-vertical spectral ratio (HVSR) at all seismic stations available in the area. Results show that the two sites located on the sediments within the low-velocity anomaly are characterized by a high HVSR peak of amplitude 4 at frequency of about 0.27 Hz, which likely corresponds to the seismic resonance of the sediment-filled basin. At the same two sites, the amplification factor computed with the Standard Spectral Ratio (SSR) method applied to regional earthquakes shows values up to 6 in the frequency range 0.3–1.0 Hz. We used a selection of 332 local earthquakes in the magnitude range 0.6–4.4 to compute a high-resolution P-wave 3D velocity model of the Crati valley and surrounding terranes. The tomographic analysis shows results with a good resolution down to at least 15 km depth. A strong low-velocity anomaly at shallow depths in the well resolved sector of the valley coincides with the sediment-filled graben, the most important geological feature of the area. The results of this work provide a valuable information to be considered in the estimation of local seismic hazard. [ABSTRACT FROM AUTHOR]

Complex measurements being performed with a network of stations at different epicenter distances allowed to register numerous effects in various geophysical fields at the moment of peak activity of the Tonga Volcano 15/01/2022. Monitoring was also performed at the "Mikhnevo" Geophysical Observatory of Sadovsky Institute of Geospheres Dynamics of Russian Academy of Sciences. The aim was to detect the links between disturbances in the atmosphere, changes in ambient seismic noise, and variations in underground water level. For the first time in an aseismic region the reaction of unevenly aged aquifers to a global disturbance in the form of propagating Lamb atmospheric waves (direct and antipodal ones) as they passed around the Earth have been registered. The responses recorded in different geophysical fields were compared to background values of probability density function of power spectral density. A difference in the intensities of the responses of the confined and weakly confined aquifers to two passages of atmospheric fronts from the Tonga volcanic eruption was established. This difference corresponds to the values of barometric efficiency. Delays of the hydrogeological responses with respect to the passage of Lamb waves were observed. Synchronous measurements of hydrogeological responses to passing atmospheric disturbances produced by the Tonga volcanic eruption performed in an aseismic region add to the world database of interacting geospheres and require further consideration involving the analysis of available experimental data. [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]

Submarine seismic ambient noise imaging combines current marine and on-land seismic detection technologies. Based on data from several broadband shallow-sea type ocean bottom seismometers (SOBSs) deployed in the Bohai Sea and north Yellow Sea, this paper analyzes the submarine seismic ambient noise characteristics. It explores the theory, technology, method and application of the submarine seismic ambient noise imaging using the single-point horizontal and vertical spectral ratio method (HVSR). The observations yield the following results: 1) Submarine seismic ambient noise has consistent and constant energy, making it an appropriate passive seismic source for submarine high-frequency surface wave investigation. 2) Using the HVSR approach, a single threecomponent OBS could differentiate between the basement and sediments. Array seismic observation could be utilized to extract the frequency dispersion curve and invert it to obtain the velocity structure for more accurate stratification. 3) The SOBS we use is suitable for submarine surface wave exploration. 4) Tomography results with greater resolution and deeper penetration could be obtained by combining active and passive sources in a simultaneous inversion of the HVSR and frequency dispersion curve. Seamless land-to-ocean seismic research can be accomplished with submarine seismic ambient noise imaging technologies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]