Search Results (507)

In recent years, although a variety of deep learning models have been developed for magnitude estimation, the complex and variable nature of earthquakes limits the generalizability and accuracy of these models. In this study, we selected the waveform data of the Japan earthquake. We applied four deep learning techniques (MagNet combined with bidirectional long- and short-term memory network Bi-LSTM, DCRNN with deepened CNN layers, DCRNNAmp with the introduction of a global scale factor, and Exams with a multilayered CNN architecture) for real-time magnitude estimation. By comparing the estimation errors of each model in the first 3 s after the earthquake, it is found that the DCRNNAmp performs the best, with an MAE of 0.287, an RMSE of 0.397, and an R² of 0.737 in the first 3 s after the arrival of the P-wave, and the inclusion of S-wave seismic-phase information is found to significantly improve the accuracy of the magnitude estimation, which suggests that S-wave seismic-phase waveform features can enrich the model’s understanding of the relationship between the seismic phases. It shows that S-wave phase waveform features can enrich the model’s knowledge of the relationship between seismic fluctuations and magnitude. The epicentral distance positively correlates with the magnitude estimation, and the model can converge faster with the improved signal-to-noise ratio. Despite the shortcomings of model design and opaque internal mechanisms, this study provides important evidence for deep learning in earthquake estimation, demonstrating its potential to improve the accuracy of on-site earthquake early warning (EEW) systems. The estimation capability can be further improved by optimizing the model and exploring new features. Full article

Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscattering to detect and measure dynamic strain and vibrations over extended distances. It can operate using both pre-existing telecommunication networks and specially designed fibers. This review explores the principles of DAS, including Coherent Optical Time Domain Reflectometry (COTDR) and Phase-Sensitive OTDR (ϕ-OTDR), and discusses the role of optoelectronic interrogators in data acquisition. It examines recent advancements in fiber design, such as helically wound and engineered fibers, which improve DAS sensitivity, spatial resolution, and the signal-to-noise ratio (SNR). Additionally, innovations in deployment techniques include cemented borehole cables, flexible liners, and weighted surface coupling to further enhance mechanical coupling and data accuracy. This review also demonstrated the applications of DAS across earthquake detection, microseismic monitoring, reservoir characterization and monitoring, carbon storage sites, geothermal reservoirs, marine environments, and urban infrastructure surveillance. The study highlighted several challenges of DAS, including directional sensitivity limitations, vast data volumes, and calibration inconsistencies. It also addressed solutions to these problems, such as advances in signal processing, noise suppression techniques, and machine learning integration, which have improved real-time analysis and data interpretability, enabling DAS to compete with traditional seismic networks. Additionally, modeling approaches such as full waveform inversion and forward simulations provide valuable insights into subsurface dynamics and fracture monitoring. This review highlights DAS’s potential to revolutionize seismic monitoring through its scalability, cost-efficiency, and adaptability to diverse applications while identifying future research directions to address its limitations and expand its capabilities. Full article

The generation of an earthquake and a tsunami is a coupled process of radiating seismic waves and exciting tsunamis, and the two types of waves are simultaneously recorded by ocean-bottom pressure sensors. In order to constrain the earthquake source and evaluate the tsunami hazards, it is necessary to separate the tsunami waves. It is traditional to apply a low-pass filter such that the seismic waves are filtered and the tsunami waves remain. However, filtering may also cause distortion of the tsunami waves. In this study, we first use the finite-element method to simulate the generation of seismic and tsunami waves and show that the coupling is a linear superposition of the two waves. We then propose a new method to extract the tsunami waves. First, a low-pass filter with relatively high cutoff frequency that does not affect the tsunami waves is adopted, so that only tsunami waves and low-frequency seismic waves remain. The low-frequency seismic waves satisfy a theoretical equation $p=\rho ha$ (p pressure, $\rho$ water density, h water depth, and a seafloor vertical acceleration), and they can be predicted and removed by utilizing the records of ocean-bottom acceleration. We demonstrate the procedure by numerical simulations and show that the method successfully extracts clean tsunami signals, which is important for earthquake source characterization and tsunami hazard assessment. Full article

Distributed Acoustic Sensing (DAS) offers numerous advantages, including resistance to electromagnetic interference, long-range dynamic monitoring, dense spatial sensing, and low deployment costs. We initially deployed a water–land DAS system at the Xinfengjiang (XFJ) Reservoir in Guangdong Province, China, to monitor earthquake events. Environmental noise analysis identified three distinct noise zones based on deployment conditions: periodic 18 Hz signals near surface-laid segments, attenuated low-frequency signals (<10 Hz) in the buried terrestrial sections, and elevated noise at transition zones due to water–cable interactions. The system successfully detected hundreds of teleseismic and regional earthquakes, including a Mw7.3 earthquake in Hualien and a local ML0.5 microseismic event. One year later, the DAS system was upgraded with two types of spiral sensor cables at the end of the submarine cable, extending the total length to 5.51 km. The results of detecting both active (transducer) and passive sources (cooperative vessels) highlight the potential of integrating DAS interrogators with spiral sensor cables for the accurate tracking of underwater moving targets. This field trial demonstrates that DAS technology holds promise for the integrated joint monitoring of underwater acoustics and seismic signals beneath lake or ocean bottoms. Full article

Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined feature functions and threshold parameters. These limitations hinder effective noise removal, resulting in suboptimal signal-to-noise ratios (SNRs) and post-denoising waveform distortion. To address these shortcomings, this study introduces a novel denoising approach leveraging a DnCNN network. The DnCNN framework, which integrates batch normalization with residual learning, is adept at swiftly identifying and eliminating noise from seismic signals through its residual learning capabilities. To assess the efficacy of this DnCNN-based model, it was rigorously tested against a curated test set and benchmarked against other denoising techniques, including wavelet thresholding, empirical mode decomposition, and convolutional auto-encoders. The findings demonstrate that the DnCNN model not only significantly enhances the SNR and correlation coefficient of the processed seismic signals but also achieves superior noise reduction performance. Full article

As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in order to enhance the signal-to-noise ratio of the seismic data. At present, the prevailing denoising techniques are based on the assumption that the signal adheres to linear model. However, this assumption is frequently invalid in complex geological conditions. The main challenge lies in the fact that linear models, which are foundational to traditional signal processing, fail to capture the nonlinear components of seismic signals. The objective of this paper is to present a methodology for the detection of nonlinear signal structures, with a particular focus on nonlinear time differences. We propose a method for detecting nonlinear time differences based on template matching, wherein the seismic wavelet is treated as the template. Template matching, a fundamental pattern recognition technique, plays a key role in identifying nonlinear structures within signals. By employing a local signal as a template, the template matching technique can identify all the structure of the signal, thereby enabling the detection of nonlinear features. By employing template matching, the nonlinear time differences in the signal are identified and corrected, thus enabling the signal to align with the assumption of linearity. Subsequently, linear denoising methods are employed to effectively remove noise and enhance the signal-to-noise ratio. The results of numerical experiments demonstrate that the proposed template matching method is highly accurate in detecting nonlinear time differences. Furthermore, the method’s efficacy in removing random noise from real seismic data is evident, underscoring its superiority. Full article

The deployment density of surface sensors can significantly impact the accuracy of subsurface shallow seismic field energy inversion. With finite budget constraints, it is often not feasible to deploy a large number of sensors, resulting in limited seismic signal acquisition that hinders accurate inversion of the shallow subsurface explosions. To address the challenge of insufficient sensor signals needed for inversion, we conducted a study on a subsurface shallow wavefield data interpolation method based on transfer learning. This method is designed to increase the overall signal acquisition by interpolating signals at target locations from limited measurement points. Our research employs neural networks to interpolate real seismic data, supplementing the sampled signals. Given the lack of extensive samples from actual data collection, we devised a training approach that combines numerically simulated signals with real collected signals. Initially, we performed conventional interpolation training using a deep interpolation network with complete synthetic gather images obtained from numerical simulations. Subsequently, the feature extraction part was frozen, and the interpolation network was transferred to real datasets, where it was trained using incomplete gather images. Finally, these incomplete gather images were re-input into the trained network to obtain interpolated results at the target locations. Our study demonstrates the superiority of our method by comparing it with two other interpolation networks and validating the effectiveness of transfer learning through four sets of ablation experiments in the actual test. This method can also be applied to other shallow geological structures to generate a large number of seismic signals for energy inversion. Full article

The three-component accelerometer array has garnered significant attention in seismic wave detection. In this paper, we designed a three-dimensional optical fiber accelerometer based on a circular cross-section cantilever beam and distributed optical fiber strain interrogator. An externally modulated optical frequency domian reflectometry (OFDR) system with centimeter-level spatial resolution is developed to demodulate the dynamic strain on fiber. An algorithm to reconstruct the three-component acceleration from the strain of the optical fiber was derived, and the factors affecting the errors in reconstruction were also investigated. The developed accelerometer exhibits comparable performance to an electrical accelerometer in the experiment. The correlation coefficient between the reconstructed signal waveforms from the two accelerometers exceeded 0.9, and the angular error was less than 8°. The proposed accelerometer is highly compatible with distributed optical fiber sensing technology, presenting significant potential for long-distance array deployment of three-component seismic wave monitoring. Full article

Hydrophone is a key component of marine seismic exploration systems, divided into a scalar hydrophone and vector hydrophone. The electrochemical vector hydrophone has attracted much attention due to its high sensitivity and low-frequency detection capability. With the development of noise reduction technology, high-frequency noise has been effectively suppressed, while low-frequency noise is still difficult to control, which has become a key issue in the monitoring of underwater target radiation noise. The traditional electrochemical vector hydrophone based on the molecular electron transfer (MET) principle is limited in the working bandwidth in the low-frequency band, which affects the detection capability of low-frequency radiation signals from underwater targets. In order to solve this problem, a frequency response extension method of a MET electrochemical vector hydrophone based on dynamic feedback network is proposed. By introducing a dynamic force balance negative feedback system based on a digital signal processor (DSP), the working bandwidth of the hydrophone is extended, and the detection capability of low-frequency signals is enhanced. At the same time, the system has field adjustability and can resist the long-term system frequency characteristic drift. Experimental results show that the proposed method effectively improves the frequency response performance of the electrochemical vector hydrophone, providing a new technical solution for its application in the monitoring of low-frequency radiation noise from underwater targets. Full article

Radon gas anomalies have been investigated as potential earthquake precursors for many years. In this work, we have studied the possible correlations between radon emissions and the seismic activity rate for a given region to test if the existing correlation may be later used to forecast the occurrence of earthquakes larger than a given magnitude. The Vrancea region (Romania) was chosen as a study area since it is being surveilled by a multidisciplinary real-time monitoring network, and at least seven earthquakes with magnitudes greater than 4.5 Mw have occurred in this area in the period from 2016 to 2020. Our research followed several steps: First, the recorded radon signals were preprocessed (detrended, deseasoned and smoothed). Then, the station’s signals were correlated in order to check which stations are recording radon anomalies due to the same regional tectonic process. On the other hand, the seismic activity rate was computed using the earthquakes in the main catalogue of the region that are able to generate radon emissions and can be registered at several stations. The obtained results indicate a significant correlation between the seismic activity rate and the temporal series of radon anomalies. A temporal lag between the seismic activity rate and the radon anomalies was found, which can be related to the proximity to the epicentre of the main earthquake in each of the studied subperiods. Changes in the regional tectonic stress field could explain why the seismic activity rate and radon anomalies are correlated over time. Further research could focus on obtaining a function to forecast the seismic activity rate using the following as dependent variables: the radon anomalies recorded at several stations, the distance from the stations, and tectonic factors such as the fault system, azimuth, type of soil, etc. Full article

The use of a dense network of commercial high-cost seismographs for earthquake monitoring is often financially unfeasible. A viable alternative to address this limitation is the development of a network of low-cost seismographs capable of monitoring local seismic events with a precision comparable to that of high-cost instruments within a specified distance from the epicenter. The primary aim of this study is to compare the performance of an advanced, contemporary low-cost seismograph with that of a commercial, high-cost seismograph. The proposed system is enhanced through the integration of a 24-bit analog-to-digital converter board and an optimized architecture for a low-noise signal amplifier employing active components for seismic signal detection. To calibrate and assess the performance of the low-cost seismograph, an installation was deployed in a region of high seismic activity in Evgiros, Lefkada Island, Greece. The low-cost system was co-located with a high-resolution 24-bit commercial digitizer, equipped with a broadband (30 s—50 Hz) seismometer. An uninterrupted dataset was collected from the low-cost system over a period of more than two years, encompassing 60 local events with magnitudes ranging from 0.9 to 3.2, epicentral distances from 5.71 km to 23.45 km, and focal depths from 1.83 km to 19.69 km. Preliminary findings demonstrate a significant improvement in the accuracy of earthquake magnitude estimation compared to the initial configuration of the low-cost seismograph. Specifically, the proposed system achieved a mean error of ±0.087 when benchmarked against the data collected by the high-cost commercial seismograph. These results underscore the potential of low-cost seismographs to serve as an effective and financially accessible solution for local seismic monitoring. Full article

Ocean Bottom Node (OBN) is a seismic data acquisition technique, comprising a hydrophone and a three-component geophone. In practice, the vertical component is susceptible to high-amplitude, low-velocity, and low-frequency shear wave noise, which negatively impacts the subsequent processing of dual-sensor data. The most commonly used method is adaptive matching subtraction, which estimates shear wave noise in the vertical component by solving an optimization problem. Neural networks, as robust nonlinear fitting tools, offer superior performance in resolving nonlinear mapping relationship and exhibit computational efficiency. In this paper, we introduce a self-supervised shear wave suppression approach for 3D OBN seismic data, using a neural network in place of the traditional adaptive matching subtraction operator. This method adopts the horizontal components as the input to the neural network, and measures the output and the noisy vertical component to establish a loss function for the network training. The network output is the predicted shear wave noise. To better balance signal leakage and noise suppression, the method incorporates a local normalized cross-correlation regularization term in the loss function. As a self-supervised method, it does not need clean data to serve as labels, thereby negating the tedious work of obtaining clean field data. Extensive experiments on both synthetic and field data demonstrate the effectiveness of the proposed method on shear wave noise suppression for 3D OBN seismic data. Full article

Linear noise, a significant type of interference in exploration seismic data, adversely affects the signal-to-noise ratio (SNR) and imaging resolution. As seismic exploration advances, the constraints of the acquisition environment hinder the ability to acquire seismic data in a regular and dense manner, complicating the suppression of linear noise. To address this challenge, we have developed an anti-aliasing and anti-leakage frequency–wavenumber (f-k) filtering method. This approach effectively mitigates issues of spatial aliasing and spectral leakage caused by irregular coarse data acquisition by integrating linear moveout correction and anti-leakage Fourier transform into traditional f-k filtering. The efficacy of our method was demonstrated through examples of linear noise suppression on both irregular coarse synthetic data and field seismic data. Full article

The study of the Earth’s ionosphere is a topic that has increased in relevance over the past few decades. The ability to predict the ionosphere’s behavior, as well as to mitigate the effects of its rapid changes, is a matter of primary importance in satellite communications, positioning, and navigation applications at present. Ionosphere perturbations can be produced by geomagnetic storms correlated with the solar activity or by earthquakes, volcanic activities, and so on. The monitoring of space weather is achieved through analyzing the Vertical Total Electron Content (VTEC) and its anomalies by means of time series, maps, and other derived parameters. In this study, we outline a strategy to estimate the VTEC in real-time, its rate of change, and the corresponding Signal-to-Noise Ratio (SNR) based on dual-frequency GNSS Doppler observations. We describe how to compute these parameters from GNSS data for a regional network using Adjusted Spherical Harmonic Analysis (ASHA) applied to a local model. The proposed method was tested to study ionospheric anomalies for two seismic events: the 2015 Nepal and 2023 Turkey earthquakes. In both cases, anomalies were detected in the maps of the differential VTEC (DTEC), differential VTEC rate, and SNR of the VTEC produced close to the earthquake zone. The robustness of the results is strongly related to the availability of a dense Ionosphere Pierce Point (IPP) cloud on the ionospheric layer and surrounding the studied area. At present, the distribution of Continuously Operating Reference Stations (CORSs) around the world is insufficiently dense and homogeneous in certain regions (e.g., the oceans). Robustness can be improved by increasing the number of CORSs and developing new models involving measurements over ocean surfaces. Full article

Single-channel seismic (SCS) methods play a crucial role in offshore wind farm assessments, offering rapid and continuous imaging of the subsurface. Conventional SCS methods often fall short in resolution and signal completeness, leading to potential misinterpretations of geological structures. In this study, we propose the application of seismic interferometry as a powerful tool to address these challenges by utilizing multiple reflections that are usually considered as noise. First, we demonstrate the feasibility of using seismic interferometry to approximate the primary wavefield. Then, we evaluate a series of seismic interferometry applied in SCS data, including cross-correlation, deconvolution, and cross-coherence, and determine the most appropriate one for our purpose. Finally, by comparing and analyzing the differences in amplitude, continuity, time–frequency properties, etc., between conventional primary wavefield information and reconstructed primary wavefield information by seismic interferometry, it is proved that incorporating multiples as supplementary information through seismic interferometry significantly enhances data reliability and resolution. The introduction of seismic interferometry provides a more detailed and accurate geological assessment crucial for optimal site selection in offshore wind farm development. Full article