Seismic Waveform Research Articles

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

AbstractThe application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor.

Inner core backtracking by seismic waveform change reversals

The solid inner core, suspended within the liquid outer core and anchored by gravity, has been inferred to rotate relative to the surface of Earth or change over years to decades based on changes in seismograms from repeating earthquakes and explosions1,2. It has a rich inner structure3–6 and influences the pattern of outer core convection and therefore Earth’s magnetic field. Here we compile 143 distinct pairs of repeating earthquakes, many within 16 multiplets, built from 121 earthquakes between 1991 and 2023 in the South Sandwich Islands. We analyse their inner-core-penetrating PKIKP waves recorded on the medium-aperture arrays in northern North America. We document that many multiplets exhibit waveforms that change and then revert at later times to match earlier events. The matching waveforms reveal times at which the inner core re-occupies the same position, relative to the mantle, as it did at some time in the past. The pattern of matches, together with previous studies, demonstrates that the inner core gradually super-rotated from 2003 to 2008, and then from 2008 to 2023 sub-rotated two to three times more slowly back through the same path. These matches enable precise and unambiguous tracking of inner core progression and regression. The resolved different rates of forward and backward motion suggest that new models will be necessary for the dynamics between the inner core, outer core and mantle.

Bayesian Network Inference for Low-Magnitude Nonnatural Seismic Event Discrimination

Abstract In response to the gaps in understanding the causal relationship between seismic waveform features and the types of seismic events, this research is focused on seismic events of low magnitude (ML≤3.0) in the North China region. Using the Bayesian network theory, we conduct an analysis to infer event types for natural earthquakes, artificial explosions, and mining collapses, and the outcomes achieved notable efficacy for the discrimination of seismic events. Through the analysis of seismic waveforms from 1818 events, we systematically extracted and quantified 55 features in temporal, spectral, and energy domains, which were then recoded as node variables for subsequent analysis. The new data set was subject to select nodes with strong associations to the node type. Subsequently, Bayesian network topologies were constructed using three different algorithms to reconstruct the custom network, calculating posterior probabilities and marginal probabilities. Simultaneously, an extensive evaluation with precision–recall curves of the network structure was carried out, encompassing accuracy, precision, recall, and F1-score. Ultimately, sensitivity analysis was performed on each node to reveal the extent of the influence of node variations on the inference of the node type. The findings showed that the sensitivity of discrimination of seismic events was notably high for several features, including high-frequency P/S spectral ratio values (11 to ∼20 Hz), central frequency, dominant frequency, average frequency, rise and decay average frequency, the real part of the complex cepstral coefficients, peak ground acceleration, and zero crossing. In the classification of natural earthquakes, artificial explosions, and mining collapses, it was observed that the probability of mining collapses was maximized when peak ground acceleration was less than 1526.08, and concurrently, the P/S spectral ratio (11 to ∼20 Hz) fell within the range of −0.25 to −0.02.

Cold Diffusion Model for Seismic Denoising

AbstractSeismic waves contain information about the earthquake source, the geologic structure they traverse, and many forms of noise. Separating the noise from the earthquake is a difficult task because optimal parameters for filtering noise typically vary with time and, if chosen inappropriately, may strongly alter the original seismic waveform. Diffusion models based on Deep Learning have demonstrated remarkable capabilities in restoring images and audio signals. However, those models assume a Gaussian distribution of noise, which is not the case for typical seismic noise. Motivated by the effectiveness of “cold” diffusion models in speech enhancement, medical anomaly detection, and image restoration, we present a cold variant for seismic data restoration. We describe the first Cold Diffusion Model for Seismic Denoising (CDiffSD), including key design aspects, model architecture, and noise handling. Using metrics to quantify the performance of CDiffSD models compared to previous works, we demonstrate that it provides a new standard in performance. CDiffSD significantly improved the Signal to Noise Ratio by about 18% compared to previous models. It also enhanced Cross‐correlation by 6%, showing a better match between denoised and original signals. Moreover, testing revealed a 50% increase in the recall of P‐wave picks for seismic picking. Our work show that CDiffSD outperforms existing benchmarks, further underscoring its effectiveness in seismic data denoising and analysis. Additionally, the versatility of this model suggests its potential applicability across a range of tasks and domains, such as GNSS, Lab Acoustic Emission, and Distributed Acoustic Sensing data, offering promising avenues for further utilization.

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter.

A 3D computational model for ground motion simulation in Peninsular India

Due to the gradual and constant accumulation of seismic energy, Peninsular India (PI) is typically considered seismically stable with low to moderate seismicity. The seismic studies in Peninsular India always resorted to synthetic ground motion simulations, because of the limited instrumentation and hence lack of recorded data. In the absence of a well-defined medium model for PI, the usual practice is to use simple site proxies or one-dimensional velocity structures for ground motion simulations. However, the region consists of multi-scale geometric complexities, significant topography, and sedimentary basins and is surrounded by deep oceans. Thus, the radiated seismic wave field in the region is influenced by the medium properties and in the absence of a well-defined tomography model the reliable estimation of seismic hazard is a challenging problem in PI. Therefore, the seismic wave propagation in PI can be investigated using numerical simulation with reliable 3D computational model for PI, incorporating the knowledge of the underlying Earth structure. Hence, the present study attempts to develop a sophisticated three-dimensional (3D) medium model of Peninsular India for physics-based ground motion simulations for regional earthquakes. This is aided by the availability of one-dimensional (1D) velocity models and the crustal structure from the receiver function analysis which provides valuable insight into the variation of material properties in the region. In the present study, >100 s of 1D velocity profiles are collected from various literature, which is then grouped under 23 different geological regions identified in PI (as per GSI (2000)). The averaged material properties are assigned per each geological region and the information on sediment depths, basin geometry, topography, and bathymetry are incorporated. We use the spectral element method (SEM) to calibrate our 3D computational model by simulating synthetic seismograms and comparing them to recorded ground motions for two past earthquakes: the 2001 Mw 7.6 Bhuj earthquake and the 1997 Mw 5.8 Jabalpur earthquake. Further, the seismic waveforms at the near field of 2001 Mw 7.6 Bhuj event are simulated using a refined regional model. The spatial variability of associated seismic intensities and peak ground velocity (PGV) amplification are investigated. In addition, a study of the impact of model depth truncation and sphericity on ground motion is also conducted. The implemented medium model is the first of its kind for Peninsular India and can reliably be used in seismic wave propagation studies in the region. The simulated outcomes from the model are of engineering importance as these results can be used for seismic hazard assessment of the region.

SeismoNet: A proximal policy optimization-based earthquake early warning system using dilated convolution layers and online data augmentation

In seismic safety, Earthquake Early Warning (EEW) systems are indispensable for mitigating earthquake hazards. These systems strive to quickly evaluate earthquake magnitudes to determine which events warrant immediate alerts. Despite their importance, conventional approaches to rapid earthquake classification face significant hurdles, particularly with the skewed data distribution where instances requiring alerts are far less common. This study proposes an EEW method, called SeismoNet, that leverages a 7-second snapshot of three-dimensional seismic waveforms from the China Earthquake Network Center (CENC). SeismoNet incorporates a trio of dilated convolution layers to distill essential features for effective classification, addressing the critical challenge of data imbalance. Our novel integration of the Off-policy Proximal Policy Optimization (Off-policy PPO) algorithm enriches the model by rewarding accurate classifications, refining its sensitivity to less frequent yet critical seismic events. The training approach is conceptualized as a sequence of decision-making steps, with each seismic sample treated as a unique scenario. The model, acting as a decision-making agent, is rewarded or penalized based on its ability to differentiate between less and more common events. To further enhance classification accuracy, we employ a generative adversarial network (GAN) for dynamic data augmentation and a regularization technique to prevent common pitfalls like mode collapse and training instability. The efficacy of SeismoNet is underscored by its superior performance metrics, including an F-measure of 88.14% and a geometric mean of 90.66%, outperforming existing deep-learning solutions, traditional algorithms, and transfer learning methods. Our exhaustive evaluations, including ablation studies that remove key components like dilated convolutions and PPO, affirm the vital contribution of these elements to SeismoNet’s exceptional capability in EEW. SeismoNet can successfully issue timely warnings for significant seismic events, allowing for essential preventative measures and significantly reducing the potential impact on these communities.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the structural health monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the earthquake early warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time (RT) mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in RT mode was investigated in the frequency and time domains, accepting the displacements from the linear variable differential transformer sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in RT with the SF-VA technique.

Joint Inversion of Regional Waveform, First-Motion Polarity, and Synthetic Aperture Radar Surface Displacement for the Fourth and Sixth North Korean Declared Nuclear Explosions

ABSTRACT This study analyzed the Democratic People’s Republic of Korea’s (DPRK) fourth (DPRK4, 6 January 2016 Mw 4.49) and sixth (DPRK6, 7 September 2017 Mw 5.2) declared nuclear tests, employing a joint seismic and Interferometric Synthetic Aperture Radar (InSAR) inversion to improve understanding of these events and enhance moment tensor (MT) inversion capabilities. The recent efforts have focused on employing seismic waveform and InSAR geodetic deformation data separately to analyze these and the previous nuclear tests (e.g., Chiang et al., 2018; Myers et al., 2018; Wang et al., 2018). Building upon our previous work (Chi-Durán et al., 2021), we performed a joint regional waveform, first-motion (FM) polarity, and surface displacement inversion, which demonstrated improved source-type discrimination, a revised MT solution with reduced scalar moment uncertainty, and an independently constrained location. In this article, we build on the previous results for DPRK6 by including an analysis using a four-layered velocity model with free-surface topography to compute the near-source static deformation Green’s functions. The model consists of a 50 m basalt layer (VP=2.07 km/s, VS=1.2 km/s), a 250 m stratified volcanic deposit layer (VP=1.73 km/s, VS=1.0 km/s), a 700 m weathered granodiorite layer (VP=2.5 km/s, VS=1.3 km/s), and a granodiorite half-space (VP=5.35 km/s, VS=3.09 km/s). The half-space shares the velocity of the regional MDJ2 velocity model (Ford et al., 2010), which has proven effective for waveform inversion in the region. This model considers the range of reported values for various lithologies and weathering effects. Our findings show that using the layered velocity model enhances the recovery of source location and depth for both the explosions by improving fits and reducing uncertainties. The joint inversion also improves source-type discrimination and better constrains the scalar seismic moment necessary for downstream yield estimation.

Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

AbstractWe test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Low‐Frequency Earthquakes Downdip of Deep Slow Slip Beneath the North Island of New Zealand

AbstractWe report the first catalog of low‐frequency earthquakes in the Hikurangi subduction zone, located beneath the Kaimanawa Range of the North Island at 50 km depth, downdip of regularly recurring (every 4–5 years) deep M7 slow slip events. To systematically detect low‐frequency earthquakes within the regional continuous seismic data, we utilized a matched‐filter approach with template waveforms derived from previous observations of tectonic tremor. We built our catalog of 36 low‐frequency earthquake sources, that produced almost 21,000 events over more than a decade, with two matched‐filter search iterations. In each iteration, the detections were gathered into families and their coherent waveforms processed and stacked to extract high‐quality waveforms, allowing us to pick seismic phase arrivals to locate the low‐frequency earthquakes. We highlight three characteristic features to validate that our detected events are indeed low‐frequency earthquakes: the eponymous deficit of high frequencies in their seismic waveforms, the episodic swarms of activity that define their activity through time, and their location at the plate boundary with a double‐couple source mechanism and geometry consistent with the subduction interface. Considering the observed low‐frequency earthquakes' relationship to neighboring slow slip, we observe the event swarms to occur much more frequently than the M7 slow slip events located just updip. Similar to other deep low‐frequency earthquakes in other subduction zones, we suggest that this characteristic clustering in time is driven by more frequent, smaller slow slip events that are not clearly observable at the surface.

Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra

Geometry and frictional states of the megathrust in subduction zones play critical roles on the rupture extent, hence the magnitude, of great earthquakes. However, their details are often difficult to obtain, primarily due to limited offshore geophysical observations. Here we overcome this challenge by precisely determining source parameters (i.e., location and focal mechanism) of medium-sized earthquakes using global broadband seismic waveform data. The dip angles of 108 Mw 5.0+ plate interface events from 1990 to 2017 in the central Sumatran subduction zone are tightly constrained by waveform inversion, and their locations are refined with surface-wave relocation and depth phases modeling. Our results reveal compact trench parallel earthquake belts at the up-dip and down-dip boundaries of the ruptures of the Mentawai 2007 Mw 8.4 and Mw 7.9 earthquakes. The down-dip seismicity belt marks ∼8º bending of the plate interface, suggesting that the change of fault geometry may have played a role in halting the megathrust rupture along with thermal effects. The up-dip seismicity belt shows a clear spatial complementarity to the largest events’ slip distribution. This belt is quite narrow (∼20 km) for the Mw 8.4 rupture but much broader (∼50 km) for the Mw 7.9 event. The combination of the up-dip seismicity belt and slip distributions matches well with the positive residual bathymetry and gravity anomalies, suggesting that these events resulted from long-term coupling on the plate boundary. Our findings suggest the importance of considering both up-dip and down-dip seismicity belts, along with residual bathymetry and gravity data, in understanding megathrust rupture behaviors and assessing the associated hazards.

Seismicity and Focal Mechanism Analysis of The Local Earthquakes in the Zagros Fold-Thrust Belt of the Iraqi Kurdistan Region

During the period of 2020 to May 2022, the Iraqi Kurdistan region experienced several small to moderate size earthquakes. The seismic waveform data of these earthquakes were collected from regional and local broadband seismic stations. Waveform data was analyzed for seventy earthquakes to determine their source parameters, which may contribute to the active tectonics in those areas. Moment tensor inversion is estimated for the detected local earthquakes by analysis of observed seismic waveforms in different selected areas. The waveform inversion determines the seismic moment, depth, and kind of rupturing fault at the source. A grid search is employed as part of the inversion procedure across the strike, dip, and rake angles for each depth between 0.5 and 40 Km in increments of 1 Km. Waveform modeling is used to correlate the observed and created waveforms by applying bandpass filters and a suitable velocity model for Green's function using computer programs in seismology and the ISOLA code" Isolated asperities" for multiple-point source modeling. The results show that the earthquakes with M ≥ 4 have a focal depth ranging between 7 and 20 Km. Furthermore, the regional stresses were identified and the mechanism for the optimum fit was determined. The source mechanisms in the study area are most likely a combination of strike-slip, oblique-slip, and thrust.

Three‐Dimensional Teleseismic Elastic Reverse‐Time Migration With Deconvolution Imaging Condition and Its Application to Southwest Japan

AbstractWe have developed a novel deconvolution‐based reverse time migration method to image lithospheric structures using teleseismic data recorded by dense seismic arrays. Unlike traditional approaches that rely on the retrieval of Green's functions (or receiver functions), the new method directly utilizes the recorded three‐component (3‐C) seismic waveforms to reconstruct subsurface wavefields, which has the advantage of avoiding the estimation and removal of source time function. Importantly, it enables the asymptotic estimation of P‐to‐S transmission conversion coefficients at the elastic discontinuities and enhances resolving power for the fine‐scale heterogeneities. Taking these improvements, we have obtained a full three‐dimensional (3‐D) high‐resolution image of the subduction zone beneath southwest Japan. This image provides a comprehensive configuration of the severely deformed subducting plate beneath the Kii Channel and Kinki Peninsula.

Acoustic 2-D Full-Waveform Inversion with Non-Balanced Finite differences and Adaptive Weight Decay Methods

Full waveform inversion (FWI) is a powerful seismic imaging technique that aims to achieve high-resolution subsurface models by iteratively comparing recorded seismic waveforms with modeled waveforms. The success of FWI relies on the precision of forward modeling algorithms and the effectiveness of optimization strategies. In this study, we propose a FWI methodology that leverages non-balanced staggered-grid finite differences schemes (NBFD) in the forward modeling process. By employing operators with spatial differencing of varying lengths, these schemes enhance the efficiency of forward modeling.For the optimization process, we employ adaptive gradient-based optimization with weight decay, a widely-used approach in machine learning and deep learning algorithms for model training. These methods demonstrate efficient optimization processing for large data volumes with high precision. The objective of adaptive gradient-based optimization is to dynamically adjust the update rate model parameters during the iterative process. This facilitates improved convergence speed, enhances stability, and overcomes challenges associated with traditional fixed-rate adjustment methods.To ensure accurate inversion results and avoid local minima in the cost function minimization, we implement a Multiscaling inversion strategy within the workflow. The obtained results are compared with those achieved through conventional FWI using standard staggered finite differences and various optimization methods. Our findings demonstrate that employing non-balanced staggered-grid finite differences scheme leads to significant improvements compared to the previously reported results in the literature.

Multiparameter shallow-seismic waveform inversion based on the Jensen–Shannon divergence

SUMMARY Seismic full-waveform inversion (FWI) or waveform inversion (WI) has gained extensive attention as a cutting-edge imaging method, which is expected to reveal the high-resolution images of complex geological structures. In this paper, we regard each 1-D signal in the inversion system as a 1-D probability distribution, then use the Jensen–Shannon divergence from information theory to measure the discrepancy between the predicted and observed signals, and finally implement a novel 2-D multiparameter shallow-seismic WI (MSWI). Essentially, the novel approach achieves an implicit weighting along the time-axis for each 1-D adjoint source defined by the classical WI (CWI), thus enhancing the extra illumination for a deeper medium compared with the CWI. By evaluating the inversion results of the two-layer model and fault model, the reconstruction accuracy for S-wave velocity and density of the new method is increased by about 30 and 20 per cent compared with that of the CWI under the same conditions, respectively. The reconstruction performance for P-wave velocity of these two methods is almost equal. In addition, the new 2-D MSWI is also resilient to white Gaussian noise in the data. Numerically, the inversion system has almost the strongest sensitivities to the S-wave velocity and density, performing the poorest sensitivity to the P-wave velocity. Finally, we test the novel method with a detection case for a power tunnel.

Numerical modelling of impact seismic sources using the stress glut theory

SUMMARY Meteorite impacts have proved to be a significant source of seismic signal on the Moon, and have now been recorded on Mars by InSight seismometers. Understanding how impacts produce seismic signal is key to the interpretation of this unique data, and to improve their identification in continuous seismic records. Here, we use the seismic Representation Theorem, and particularly the stress glut theory, to model the seismic motion resulting from impact cratering. The source is described by equivalent forces, some resulting from the impactor momentum transfer, and others from the stress glut, which represents the mechanical effect of plasticity and non linear processes in the source region. We condense these equivalent forces into a point-source with a time-varying single force and nine-component moment tensor. This analytical representation bridges the gap between the complex dynamics of crater formation, and the linear point-source representation classically used in seismology. Using the multiphysics modelling software HOSS, we develop a method to compute the stress glut of an impact, and the associated point-source from hypervelocity impact simulations. For a vertical and an oblique impact at 1000 m s−1, we show that the moment tensor presents a significant deviatoric component. Hence, the source is not an ideal isotropic explosion contrary to previous assumptions, and draws closer to a double couple for the oblique impact. The contribution of the point force to the seismic signal appears negligible. We verify this model by comparing two signals: (1) HOSS is coupled to SPECFEM3D to propagate the near-source signal elastically to remote seismic stations; (2) the point-source model derived from the stress-glut theory is used to generate displacements at the same distance. The comparison shows that the point-source model is accurately simulating the low-frequency impact seismic waveform, and its seismic moment is in trend with Lunar and Martian impact data. High-frequencies discrepancies exist, which are partly related to finite-source effects, but might be further explained by the difference in mathematical framework between classical seismology and HOSS’ numerical modelling.

Direct Evidence for Diverse Source Complexity in Small Earthquakes (Mw 3.3–5.0) Obtained from Near-Source Borehole Seismic Data

Abstract Small earthquakes (Mw <5) may have a similar degree of complexity as large earthquakes. However, their seismic waveforms are strongly distorted during wave propagation, making their complexity challenging to resolve. In many cases, the source parameters of small events are determined based on models that assume their source patterns are simple. In this study, to directly examine the source complexities in small events, we examined high-quality near-source (<8 km) seismic waveforms recorded by two excellent downhole sensors in Japan. The results show that the P waveforms of microearthquakes (Mw <2) are always simple at the sensors and agree well with the synthetic waveforms based on a 1D structure up to 20 Hz. The microearthquake waveforms in this frequency band essentially represent path effects besides the static source effect, suggesting that the contribution of structural inhomogeneity to the observed waveforms is small. Taking advantage of this, we inferred the moment rate functions of 164 Mw 3.3–5.0 events from the shapes of the direct P waves. They showed diversity in their complexity, and even conservatively estimated, 25% of the events had multiple subevents. The results suggest that methods that account for complexity, rather than those that assume a simple source pattern, are required to characterize even small events.

Petrophysical parameter estimation using Biot-poroelastic full-waveform inversion

SUMMARY The increasing need for petrophysical parameter inversion has spurred the advancement of full-waveform inversion (FWI). This study introduces a new inversion method that can directly estimate petrophysical parameters from seismic waveform data. Specifically, we modified the regular elastic wave equation by including Biot poroelastic parameters and designed an inversion workflow based on the framework of elastic FWI. The newly introduced inversion algorithms have demonstrated their effectiveness and accuracy using the Marmousi model and a synthetic model related to the gas hydrate deposits based on the Shenhu area of the South China Sea. Our work demonstrates the feasibility of directly inverting petrophysical parameters and assessing if a deposit contains gas hydrate.

Non‐Double‐Couple Components of Seismic Source: Method and Application to the 2014–2015 Bárðarbunga Volcanic Event Sequence, Iceland

AbstractGenuine non‐double‐couple (non‐DC) components of a seismic source, defined here as the non‐DC components that are not due to summation of pure double‐couple (DC) components, provide important insight into special physical processes in non‐earthquake sources such as explosion, volcano eruption and collapse etc. Yet they remain challenging to be resolved. To address the issue and explore the physical mechanism of those special events, we develop a waveform‐polarity‐based moment tensor (WPMT) inversion method and employ it to study physical process in the 2014–2015 Bárðarbunga volcano event sequence. The WPMT method incorporates P‐wave polarity data and seismic waveforms in the source inversion, designs a source simplicity test to check possible complex rupture in the seismic source, and employs a simulated annealing algorithm to search the best source solution. The simplicity test checks consistency of the source processes in the initiation stage of the event and the major energy release process of the event, thus ensuring that the inferred non‐DC components are genuine to the seismic source. Real event and synthetic tests indicate that the WPMT method can identify and resolve genuine non‐DC components in a seismic source. The WPMT inversions of the Bárðarbunga sequence yield many genuine non‐DC source components and reveal that the eruptions are accompanied by seismic activities in depths of 1–5 km with magma migrations out of chambers, collapses of conduits, failures of normal faults, and a magma recharge at a depth of 9 km accompanied by a failure on a nearby normal fault.