Earthquake Source Research Articles

Analysis of the Stress Field Around Concealed Active Fault From Minor Faults‐Slip Data Collected by Geological Survey: An Example in the 1984 Western Nagano Earthquake Region

AbstractEarthquakes with magnitudes of 6–7 have been reported even in various active tectonic settings where fault deformation topography have not been detected. Therefore, delineating concealed active faults generating such earthquakes is necessary to reduce earthquake damage; however, few studies exist to provide its clues regarding such faults. The 1984 Western Nagano Earthquake in Japan was a main shock with a magnitude of Mj 6.8 and a depth of 2 km at the source. Solid bedrocks are well‐exposed in the earthquake source region; however, no surface rupture have been identified, and the active fault is known to be concealed. In this study, we collected data on striations observed in fractures by geological survey around the source area of the 1984 Western Nagano Earthquake. Using the collected data, the multiple inverse method was used to estimate the stresses that affected the striation formation. Consequently, stresses similar to acting faults in this area were detected in minor faults around the known concealed active fault. This suggests that the minor faults might be part of the damage zone that has been developed around the concealed active fault. Some minor faults were recognized in Quaternary volcanic rocks, confirming that they experienced displacements recently. This study indicates the possibility of detecting concealed active faults in the bedrock by geological survey.

Site-specific probabilistic seismic hazard analysis in Nuweiba, Gulf of Aqaba, Egypt: Combining area source model and active faults

Gulf of Aqaba in Eastern Egypt is characterized by significant seismic activity and high deformation rate, making it a major source of destructive earthquakes in the region. While previous studies have produced probabilistic seismic hazard analysis (PSHA) maps for the region, they have not adequately accounted for active faults or site response in their calculations. This study introduces a newly developed seismic source model for the Gulf of Aqaba, considering area source, active fault source, and a combination of both models to improve seismic hazard assessment for the region. The study incorporates the previously measured average shear-wave velocity for the upper 30 m (Vs30), fundamental frequency (F0), and amplification factor (A0) at some selected sites in Nuweiba city. Accordingly, site-specific PSHA is carried out for the bedrock condition (Vs30 = 800 m/s, according to the Egyptian Building Code) and for the surface of soft sediments using F0, A0, and Vs30. Uncertainties in slip rate, maximum magnitude, and recurrence model are accounted during PSHA computation. Ground motion parameters such as peak ground acceleration (PGA) and spectral acceleration (SA) for various probabilities of exceedance in 50-year lifetime are calculated for both rock site conditions and soft sediments. The maximum calculated PGA and SA are 0.65 g and 1.25 g, respectively, for a 10% probability of exceedance over 50 years at bedrock conditions.

Earthquake source impacts on the generation and propagation of seismic infrasound to the upper atmosphere

SUMMARY Earthquakes with moment magnitude (Mw) ranging from 6.5 to 7.0 have been observed to generate sufficiently strong acoustic waves (AWs) in the upper atmosphere. These AWs are detectable in Global Navigation Satellite System satellite signals-based total electron content (TEC) observations in the ionosphere at altitudes ∼250–300 km. However, the specific earthquake source parameters that influence the detectability and characteristics of AWs are not comprehensively understood. Here, we extend our approach of coupled earthquake-atmosphere dynamics modelling by combing dynamic rupture and seismic wave propagation simulations with 2-D and 3-D atmospheric numerical models, to investigate how the characteristics of earthquakes impact the generation and propagation of AWs. We developed a set of idealized dynamic rupture models varying faulting types and fault sizes, hypocentral depths and stress drops. We focus on earthquakes of Mw 6.0–6.5, which are considered the smallest detectable with TEC, and find that the resulting AWs undergo non-linear evolution and form acoustic shock N waves reaching thermosphere at ∼90–140 km. The results reveal that the magnitude of the earthquakes is not the sole or primary factor determining the amplitudes of AWs in the upper atmosphere. Instead, various earthquake source characteristics, including the direction of rupture propagation, the polarity of seismic wave imprints on the surface, earthquake mechanism, stress drop and radiated energy, significantly influence the amplitudes and periods of AWs. The simulation results are also compared with observed TEC fluctuations from AWs generated by the 2023 Mw 6.2 Suzu (Japan) earthquake, finding preliminary agreement in terms of model-predicted signal periods and amplitudes. Understanding these nuanced relationships between earthquake source parameters and AW characteristics is essential for refining our ability to detect and interpret AW signals in the ionosphere.

The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS

Accurately modeling time-dependent coseismic crustal deformation as observed on high-rate Global Navigation Satellite System (HR-GNSS) lends insight into earthquake source processes and improves local earthquake and tsunami early warning algorithms. Currently, time-dependent crustal deformation modeling relies most frequently on simplified 1D radially symmetric Earth models. However, for shallow subduction zone earthquakes, even low-frequency shaking is likely affected by the many strongly heterogeneous structures such as the subducting slab, mantle wedge, and the overlying crustal structure. We demonstrate that including 3D structure improves the estimation of key features of coseismic HR-GNSS time series, such as the peak ground displacement (PGD), the time to PGD (tPGD), static displacements (SD), and waveform cross-correlation values. We computed synthetic 1D and 3D, 0.25 Hz and 0.5 Hz waveforms at HR-GNSS stations for four M7.3+ earthquakes in Japan using MudPy and SW4, respectively. From these synthetics, we computed intensity-measure residuals between the synthetic and observed GNSS waveforms. Comparing 1D and 3D residuals, we observed that the 3D simulations show better fits to the PGD and SD in the observed waveforms than the 1D simulations for both 0.25 Hz and 0.5 Hz simulations. We find that the reduction in PGD residuals in the 3D simulations is a combined effect of both shallow and deep 3D structures; hence incorporating only the upper 30 km of 3D structure will still improve the fit to the observed PGD values. Our results demonstrate that 3D simulations significantly improve models of GNSS waveform characteristics and will not only help understand the underlying processes, but also improve local tsunami warning.

Numerical Simulation of Electromagnetic Responses to an Earthquake Source Due To the Piezoelectric Effect of ‘∞m’ Symmetry

AbstractIt is reported that earthquakes are accompanied by electromagnetic (EM) anomalies. These anomalies are thought to be caused by earthquakes but their generation mechanism is still unclear. The piezoelectric effect has been proposed as a possible mechanism, particularly in quartz‐rich rocks. However, the EM responses to earthquakes due to such an effect have not been well understood. In this article, we study the EM signals generated by an earthquake source due to the piezoelectric effect. We develop a semi‐analytical method to simulate the EM responses to earthquake sources in a 3‐D layered model and conduct numerical simulations to investigate the characteristics of the EM fields. The results show that the piezoelectric effect in quartz‐rich rocks could be the responsible mechanism for the EM signals recorded during the earthquake. Two kinds of EM signals can be generated by the earthquake, namely, the early EM wave arriving before the seismic waves and the coseismic EM field accompanying the seismic waves. For an Mw 6.1 earthquake, the coseismic electric field is ∼0.1 μV/m and the magnetic field can only reach ∼10−5 nT. We also study the sensitivity of the coseismic EM fields to the rock conductivity. The results indicate that the coseismic EM fields are mainly affected by the conductivity of the shallow layer where the receiver is located, and if the top layer is thin, they are also affected by the conductivity of the deeper layer due to the contribution of the evanescent EM wave created at the interface.

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.

Probabilistic estimation of the source component of seismic hazard in North-Eastern Brazil

Stable continental regions pose unique challenges for conducting Probabilistic Seismic Hazard Analysis because the earthquake activity driving mechanisms are poorly understood. For instance, the lower seismicity (hence the paucity of data) and the absence of well-defined active fault systems complicate accurately determining seismic source parameters. Northeastern Brazil is a stable continental region exhibiting moderate-size events recorded with significant seismic intensities and provoking the collapse of poorly constructed buildings in the last century. Thus, assessing the seismic hazard is critical for seismic risk mitigation. The seismic hazard depends on three components: source, path, and site, and here, we present the probabilistic seismic hazard analysis of the source component for NE Brazil. Spatial aggregation of earthquake sources outlined four areal seismic zones. A goodness-of-fit test rejected the Gutenberg-Richter model of magnitude frequency distribution in one of the studied seismic zones. For this reason, we estimated the magnitude probability distribution function in that zone using a nonparametric adaptive kernel estimator. In other zones the Gutenberg-Richter magnitude frequency model was applied. In either way of the magnitude probability distribution modelling we considered the upper bound for magnitude equal to 6.6 mR, based on the upper bound of a 95 % confidence interval for the standard normal distribution of palaeoearthquake sizes. Our findings suggests that potentially damaging events are likely to occur, and we cannot neglect chances for the occurrence of earthquakes exceeding 5.2 mR. The calculated mean return periods indicate significantly shorter intervals between consecutive large events than palaeoseismic records.

Numerical Multifield Coupling Model of Stress Evolution and Gas Migration: Application of Disaster Prediction and Mining Sustainability Development

At present, coal mining is gradually shifting towards deep areas, and coal mines under deep mining conditions are more prone to coal and gas outburst accidents. In this research, we aim to explain the causes and mechanisms of dynamic disasters, which are caused by the combined action of static load, gas, and dynamic load on tectonic regions in complex stress field environments. Through numerical simulation using COMSOL Multiphysics software, based on the geological conditions of a mine in Jilin Province, it was found that faults lead to abnormal stress in tectonic regions. The combined action of dynamic and static loads results in excessive stress, causing the fragmentation and displacement of the coal body, leading to coal mine disasters, thus disrupting sustainability. Additionally, the coal matrix gas entering fractures raises the gas pressure and leads to the accumulation of methane near earthquake sources. Dynamic loads accelerate gas desorption in coal and increase porosity and permeability, facilitating rapid gas migration. This influx of gas into the roadways exceeds safety limits. Then, based on these findings and on-site conditions, a set of sustainable measures for coal mines has been proposed. This research offers theoretical guidance for enhancing safety, stability, and sustainability in coal mining processes.

Possible Interrelations of Space Weather and Seismic Activity: An Implication for Earthquake Forecast

The statistical analysis of the impact of the top 50 X-class solar flares (1997–2024) on global seismic activity as well as on the earthquake preparation zones located in the illuminated part of the globe and in an area of 5000 km around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases, an increase in seismicity is observed, especially in the region around the subsolar point (up to 33%) during the 10 days after the solar flare in comparison with the preceding 10 days. The case study of the aftershock sequence of a strong Mw = 9.1 earthquake (Sumatra–Andaman Islands, 26 December 2004) after the solar flare of X10.16 class (20 January 2005) demonstrated that the number of aftershocks with a magnitude of Mw ≥ 2.5 increases more than 17 times after the solar flare with a delay of 7–8 days. For the case of the Darfield earthquake (Mw = 7.1, 3 September 2010, New Zealand), it was shown that X-class solar flares and M probably triggered two strong aftershocks (Mw = 6.1 and Mw = 5.9) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from the point of view of the fault electrical conductivity and orientation. Based on the obtained results, the possible application of natural electromagnetic triggering of earthquakes is discussed for the earthquake forecast using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

Machine learning emulation of high resolution inundation maps

SUMMARY Estimating coastal tsunami impact for early-warning or long-term hazard analysis requires the calculation of inundation metrics such as flow-depth or momentum flux. Both applications require the simulation of large numbers of scenarios to capture both the aleatory variability and the epistemic tsunami uncertainty. A computationally demanding step in simulating inundation is solving the non-linear shallow water (NLSW) equations on meshes with sufficiently high resolution to represent the local elevation accurately enough to capture the physics governing the flow. This computational expense is particularly challenging in the context of Tsunami Early Warning where strict time constraints apply. A machine learning (ML) model that predicts inundation maps from offshore simulation results with acceptable accuracy, trained on an acceptably small training set of full simulations, could replace the computationally expensive NLSW part of the simulations for vast numbers of scenarios and predict inundation rapidly and with reduced computational demands. We consider the application of an encoder–decoder based neural network to predict high-resolution inundation maps based only on more cheaply calculated simulated time-series at a limited number of offshore locations. The network needs to be trained using input offshore time-series and the corresponding inundation maps from previously calculated full simulations. We develop and evaluate the ML model on a comprehensive set of inundation simulations for the coast of eastern Sicily for tens of thousands of subduction earthquake sources in the Mediterranean Sea. We find good performance for this case study even using relatively small training sets (order of hundreds) provided that appropriate choices are made in the specification of model parameters, the specification of the loss function and the selection of training events. The uncertainty in the prediction for any given location decreases with the number of training events that inundate that location, with a good range of flow depths needed for accurate predictions. This means that care is needed to ensure that rarer high-inundation scenarios are well-represented in the training sets. The importance of applying regularization techniques increases as the size of the training sets decreases. The computational gain of the proposed methodology depends on the number of complete simulations needed to train the neural network, ranging between 164 and 4196 scenarios in this study. The cost of training the network is small in comparison with the cost of the numerical simulations and, for an ensemble of around 28 000 scenarios, this represents a 6- to 170-fold reduction in computing costs.

Typical of Tsunami Hazard Potential from Earthquake and Landslide Sources in Palabuhanratu Bay, Indonesia

Typical of Tsunami Hazard Potential from Earthquake and Landslide Sources in Palabuhanratu Bay, Indonesia

Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events

SUMMARY Rapid and accurate characterization of earthquake sources is crucial for mitigating seismic hazards. In this study, based on 18 000 scenario ruptures ranging from Mw 6.4 to Mw 8.3 and corresponding synthetic high-rate Global Navigation Satellite System (HR-GNSS) waveforms, we developed a multibranch neural network framework, the continental large earthquake agile response (CLEAR), to simultaneously determine the magnitude and slip distributions. We apply CLEAR to recent large strike-slip events, including the 2021 Mw 7.4 Maduo earthquake and the 2023 Mw 7.8 and Mw 7.6 Turkey doublet. The model generally estimates the magnitudes successfully at 32 s with errors of less than 0.15, and predicts the slip distributions acceptably at 64 s, requiring only approximately 30 ms on a single CPU (Central Processing Unit). With optimal azimuthal coverage of stations, the system is relatively robust to the number of stations and the time length of the received data.

Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests

Earthquakes pose significant risks globally, necessitating effective seismic risk mitigation strategies like earthquake early warning (EEW) systems. However, developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations. This study examines a deep-learning-based on-site EEW framework known as ROSERS (Real-time On-Site Estimation of Response Spectra) proposed by the authors, which constructs response spectra from early recorded ground motion waveforms at a target site. This study has three primary goals: (1) evaluating the effectiveness and applicability of ROSERS to subduction seismic sources; (2) providing a detailed interpretation of the trained deep neural network (DNN) and surrogate latent variables (LVs) implemented in ROSERS; and (3) analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations. ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions. Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database, especially given the peculiarities of the subduction seismic environment. The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships. Finally, the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity. The results indicate that on-site predictions can be considered reliable within a 2–9 km radius, varying based on the magnitude and distance from the earthquake source. This information can assist end-users in strategically placing sensors, minimizing blind spots, and reducing errors from regional extrapolation.

Teotihuacan ancient culture affected by megathrust earthquakes during the early Epiclassic Period (Mexico)

Teotihuacan was one of the thriving cultures in the Mesoamerica pre-Hispanic times, located in the Central Valley of Mexico. The city-state was a dominant centre point during the Classic period and its influence affected other contemporaneous cultures. Around the year 550 CE, a continuous decrease in urban population and selective building destruction was noted, accompanied by widespread fire. The layout of the city is identified by an avenue that articulates the political-administrative and religious centres, with such significant and impressive buildings as the Pyramid of the Sun, the Pyramid of the Moon and the Temple of Feathered Serpent. A systematic analysis of building damage in the pyramids reveals several Earthquake Archaeological Effects, (EAEs) potentially related to seismic loading. A damage pattern compatible with a strong ground shaking was also identified in the west staircase of the Old Temple of the Feathered Serpent, in the first rows of the west staircase of the Adosada platform (New Temple), and in the Pyramid of the Sun. In total, five ancient earthquakes have been determined from the damage, dated from the Tzacualli cultural period (1–100 CE), to the Xolalpan – Metepec period (450–550 CE). Unfortunately, this methodology does not determine the earthquake source. Therefore, we consider the possibility that repetitive megathrust earthquakes (Mw > 8.5) from the Middle American Trench (Pacific coast) could be responsible for the spatial pattern of the building damage. This proposal does not conflict with other existing theories for the Teotihuacan abrupt collapse, considering that the sudden overlapping of natural disasters like earthquakes could increase internal warfare (uprising), and civil unrest.

A fully scalable homogenization method to upscale 3-D elastic media

SUMMARY Modelling seismic wavefields in complex 3-D elastic media is the key in many fields of Earth Science: seismology, seismic imaging, seismic hazard assessment and earthquake source mechanism reconstruction. This modelling operation can incur significant computational cost, and its accuracy depends on the ability to take into account the scales of the subsurface heterogeneities varying. The theory of homogenization describes how the small-scale heterogeneities interact with the seismic waves and allows to upscale elastic media consistently with the wave equation. In this study, an efficient and scalable numerical homogenization tool is developed, relying on the similarity between the equations describing the propagation of elastic waves and the homogenization process. By exploiting the optimized implementation of an elastic modelling kernel based on a spectral-element discretization and domain decomposition, a fully scalable homogenization process, working directly on the spectral-element mesh, is presented. Numerical experiments on the entire SEAM II foothill model and a 3-D version of the Marmousi II model illustrate the efficiency and flexibility of this approach. A reduction of two orders of magnitude in terms of absolute computational cost is observed on the elastic wave modelling of the entire SEAM II model at a controlled accuracy.

Uncertainty in Ground-Motion-to-Intensity Conversions Significantly Affects Earthquake Early Warning Alert Regions

Abstract We examine how the choice of ground-motion-to-intensity conversion equations (GMICEs) in earthquake early warning (EEW) systems affects resulting alert regions. We find that existing GMICEs can underestimate observed shaking at short rupture distances or overestimate the extent of low-intensity shaking. Updated GMICEs that remove these biases would improve the accuracy of alert regions for the ShakeAlert EEW system for the West Coast of the United States. ShakeAlert uses ground-motion prediction equations (GMPEs), which calculate spatial distributions of peak ground acceleration (PGA) and peak ground velocity (PGV) from earthquake source estimates, combined with GMICEs to translate GMPE output into modified Mercalli intensity (MMI). We find significant epistemic uncertainty in alert distances; near-source MMI estimates from different GMICEs can differ by over 1 MMI unit, and MMI extents used for public EEW alerts can differ by hundreds of kilometers for larger magnitude earthquakes (M ∼6.5+). We use a catalog of “Did You Feel It?” shaking reports to evaluate how well GMICEs predict observed shaking. Our preferred GMICE is the one that computes MMI using PGV for high intensities and transitions to using PGA for nondamaging intensities. These results motivate updating GMICE relationships more generally, including in ShakeMap applications.

SSA2py: A High-Performance Python Implementation of the Source-Scanning Algorithm for Spatiotemporal Seismic Source Imaging

Abstract This article introduces the first version of SSA2py (v.1.0)—an open-source package designed to implement the source-scanning algorithm (SSA). SSA2py is a Python-based, high-performance-oriented package that incorporates the SSA method, which has been effectively applied to numerous earthquakes for imaging the spatiotemporal behavior of the seismic source. The software supports a wide range of data and metadata resources. These include the International Federation of Digital Seismograph Networks Web Services, the SeedLink protocol, and others, ensuring optimal access to waveforms and station metadata. Furthermore, the code may evaluate the quality of accessible waveforms using signal analysis methods, allowing for the most appropriate data selection. The SSA method has been computationally optimized using multiprocessing techniques for efficient central processing unit and graphic processing units executions, enabling considerably accelerated computational processes even for large-scale grid searches. The program is also designed to provide statistical and methodological uncertainties for the executed cases through jackknife, bootstrap, and backprojection array response function tests. After appropriate tuning by the user, SSA2py can be used for detailed earthquake source studies that backprojection technique typically serves as a complementary output to the source inversion result or as a near-real-time tool for successful and quick identification of the style and complexity of the earthquake rupture. With a wide and flexible configuration, the user has complete control over all calculating aspects of SSA2py. This article provides a detailed description of the structure and capabilities of this new package, and its reliability is demonstrated through targeted applications to the 2004 Mw 6.0 Parkfield and 2019 Mw 7.1 Ridgecrest earthquakes. Furthermore, the computational efficiency of SSA2py is validated through rigorous performance tests.

Modeling of kappa factor using multivariate adaptive regression splines: application to the western Türkiye ground motion dataset

The recent seismic activity on Türkiye’s west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North–South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region–specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three–component strong–ground motion data from accelerometer sites with known VS30 values throughout western Türkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well–known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.

Reliable Earthquake Source Parameters Using Distributed Acoustic Sensing Data Derived from Coda Envelopes

Abstract A challenge in fully using distributed acoustic sensing (DAS) data collected from fiber-optic sensors is correcting the signals to provide quantitative true ground motion. Such corrections require considering coupling and instrument response issues. In this study, we show via comparison with geophone and broadband seismometer data that we can use coda envelope calibration techniques to obtain absolute moment magnitudes and source spectra from DAS data. Here, we use DAS and nodal geophones deployed as part of a geothermal monitoring experiment at Brady Hot Springs, Nevada, and on a 20 km long dark fiber of the ESnet’s Dark Fiber Testbed–a U.S. Department of Energy user facility, in Sacramento, California. Several DAS line segments with colocated geophone stations were used to compare the amplitude variation using narrowband S-wave coda envelopes. The DAS coda envelope decay at each point showed remarkable similarity with coda envelopes from different events in each narrow frequency range examined. The coda envelopes are used to determine Mw magnitudes and source spectra from regional stations without any major scatter. Because coda waves arrive from a range of directions, the azimuthal sensitivity of DAS is somewhat ameliorated. We show that the openly available seismic coda calibration software toolkit can be used for straightforward and faster processing of large DAS datasets for source parameters and subsurface imaging.

The influence of earthquake source complexity on frequency-dependent radiation patterns by modifying distance-dependent properties

The influence of earthquake source complexity on frequency-dependent radiation patterns by modifying distance-dependent properties