Abstract Accurate determination of focal mechanisms for induced earthquakes and microseismic events is essential for characterizing rupture behavior, in situ stress states, and fracture network activation. Small-magnitude events are often recorded with low signal-to-noise ratios, making first-motion polarity measurement challenging. A combined Convolutional Neural Network–Transformer model is trained on 290,730 Southern California waveforms using noise augmentation and random arrival-time perturbation, achieving 96% polarity accuracy. Applied to the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) stage 3 stimulation (April 2022), the model predicts P-wave polarities and is tested on rotated transverse components to estimate SH polarity as a site-specific extension for the FORGE downhole array. Using 166 manually picked P polarities and 158 manually picked SH polarities from high-signal-quality FORGE events, the P and SH pick accuracies after quality-control thresholding are 98.0% (148/151) and 96.2% (125/130), respectively. A double-couple-constrained Bayesian inversion combines the retained polarities with amplitude ratios to obtain focal mechanism solutions. The workflow yields 717 high-quality focal mechanisms, dominated by strike-slip and normal faulting, showing complex faulting behavior across the stimulated volume.

Mapping fracture networks in Enhanced Geothermal Systems (EGS) is essential for optimizing reservoir performance, yet complex fracture evolution during stimulation remains difficult to resolve. This study examines the evolution of microseismicity and fracture networks during stage 3 of the 2022 EGS stimulation at the Utah Frontier Observatory for Research in Geothermal Energy site. We map the fracture network represented by 20 clusters of seismic events identified by waveform similarities with cross-correlation. We characterize their geometric properties such as strike, dip, length, and width, and analyze the time evolution of activated fractures. The results reveal a systematic fracture evolution: early activation of pre-existing natural fractures, complex network development during peak injection, and continued activation of less favorably oriented fractures post-injection. Magnitude calibration using the Principal Component Analysis of cross-correlated waveforms improves relative amplitude measurements, refining estimations of the Gutenberg-Richter b-values with spatial variations in b-values suggesting stress re-distribution across the stimulated area. Analysis of the stress state of selected fractures further shows that fractures requiring higher excess pore pressure primarily activate at the end of injection and post-injection, highlighting stress transfer due to pore pressure as a dominant triggering mechanism. These findings provide insights into fracture propagation, stress evolution, and seismic hazard assessment in EGS reservoirs.

Monitoring human responses to extreme weather events is essential for effective emergency response and urban resilience planning. Existing approaches that leverage social sensing data, such as mobile phone location records and social media activity, have enabled valuable insights into population dynamics during disasters but are often limited by sampling biases, privacy constraints, and reliance on communication infrastructure that may degrade or fail under extreme conditions. In contrast, human activities in urban environments generate ground vibrations that are continuously captured by existing seismic networks as ambient seismic noise. These high-frequency ambient seismic signals provide a continuous, passive record of collective human activity with near-real-time availability and long-term historical coverage, making it a resilient and complementary data source for monitoring urban dynamics during disruptive events. This study evaluates the effectiveness of high-frequency seismic noise as an urban sensing modality using Winter Storm Uri in the Dallas-Fort Worth metropolitan area (Texas, USA) as a case study. By analyzing seismic signals from 13 stations across the DFW region, we quantified human-induced noise using spectral and displacement measures and validated the results with multi-source mobility and environmental datasets. The results reveal that seismic signals effectively capture the spatiotemporal patterns of human mobility during the storm event, including diurnal activity cycles, abrupt activity disruption during disaster, and gradual post-event recovery. The method's validity is supported by strong correlations with independent data sources, including mobile phone mobility data (r > 0.8), air traffic records, and meteorological observations. We also identify optimal frequency bands and spatial parameters for isolating human-related seismic signatures. In contrast, rural seismic signals were found to be more sensitive to environmental noise, particularly wind-induced vegetation movement. These findings underscore the emerging potential of leveraging existing seismic networks as a continuous, passive, and rapid urban sensing technique.

Receiver-side converted earthquake waves, commonly known as receiver functions (RFs), have long been employed for deep earth structure investigations. However, challenges persist when attempting to achieve highresolution imaging of shallow (e.g., basin-scale) subsurface features using RFs. In this study, we present a novel multichannel blind deconvolution approach for computing RFs from regional earthquake data recorded at a dense receiver network. The proposed multichannel deconvolution model considers both the RFs and source wavelets of the earthquakes as unknown and derives them by solving a linearized and overdetermined equation system in the frequency domain. Compared to conventional single-station RF, a main advantage of this approach is its reduced sensitivity to noise. We apply the multichannel blind deconvolution method to a regional earthquake dataset from a nodal array deployed above the 2016 M5 Cushing (Oklahoma, US) earthquake sequence. The resulting RF profiles clearly depicts a conversion at ca. 1.1 km depth, showing a good fit with the expected basement depths in that region. Furthermore, the RF profiles do not show vertical displacement across the fault, confirming the Cushing fault as a sharply delineated strike-slip fault.

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green's function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. We focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multi-taper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

Differences in stress-drop estimates among groups of scientists for the same earthquakes suggest disagreement in the shape of the source spectra that are used to measure corner frequency. A critical step in characterizing source spectra involves applying empirical corrections for site effects and the loss of high-frequency energy that occurs along the source-receiver path. As part of the Ridgecrest stress-drop validation study, we compare path-corrected source spectra among different methods for two nearly collocated M 3 earthquakes and investigate whether systematic differences in the applied path corrections are affecting corner-frequency estimates. We find substantial disagreements in the path corrections, which are well approximated with a simple exponential function related to the strong ground motion parameter kappa. These kappa differences are strongly correlated with corner-frequency estimates for path-corrected spectra, suggesting they are a large source of systematic differences in corner frequency (and inferred stress drop) among the methods, reflecting varying trade-offs between the source and path contributions to observed spectra. Because each method presumably fits the data it uses sufficiently well, these results indicate the limitations of existing purely empirical techniques to estimating path corrections and the need for new approaches.

Spectral source parameters used to estimate an earthquake's stress drop (A6) can vary significantly across measurement approaches. The Statewide California Earthquake Center/ U.S. Geological Survey Community Stress-Drop Validation Study was initiated to compare source parameter estimates, focusing initially on a dataset from the 2019 Ridgecrest earthquake sequence. As part of that validation effort, here we focus on one potential source of uncertainty: whether spectral fitting approaches alone, applied to a common set of spectra from the 2019 Ridgecrest sequence result in different source parameter estimates. By using a common set of benchmark spectra analyzed across a consistent frequency band of 1- 40 Hz, we eliminate many sources of variability. A subgroup of validation study participants volunteered to estimate the low-frequency displacement (40) and corner frequency (fc) by fitting a smooth function to benchmark displacement spectra. Participants used linear- or log-sampled spectra, assumed a Brune or Boatwright spectral model, and applied different misfit criteria. We compare 17 approaches used to estimate 40, fc, and A6 for 54 earthquake spectra. Our results reveal that 35% of events have A6 estimates within a factor of two, whereas others exhibit variations exceeding an order of magnitude. The variability in 40 and fc can largely be attributed to whether a spectrum is consistent with the smooth function of an idealized simple crack model. The trade-off between 40 and fc may be more pronounced when using linearly sampled spectra, as higher frequency spectral bumps control the fits. As expected, methods that assumed a Boatwright model tended to have lower 40 and somewhat higher fc compared to those assuming a Brune model, although resulting A6 estimates are similar. When compared to the overall validation study results, the fitting approach alone may account for between 5% and 90% (25% on average) of the total variability in spectral A6.

Stress drop is a fundamental parameter related to earthquake source physics, but is hard to measure accurately. To better understand how different factors influence stress-drop measurements, we compare two different methods using the Ridgecrest stress-drop validation data set: spectral decomposition (SD) and spectral ratio (SR), each with different processing options. We also examine the influence of spectral complexity on source parameter measurement. Applying the SD method, we find that frequency bandwidth and time-window length could influence spectral magnitude calibration, while depth-dependent attenuation is important to correctly map stress-drop variations. For the SR method, we find that the selected source model has limited influence on the measurements; however, the Boatwright model tends to produce smaller standard deviation and larger magnitude dependence than the Brune model. Variance reduction threshold, frequency bandwidth, and time-window length, if chosen within an appropriate parameter range, have limited influence on source parameter measurement. For both methods, wave type, attenuation correction, and spectral complexity strongly influence the result. The scale factor that quantifies the magnitude dependence of stress drop show large variations with different processing options, and earthquakes with complex source spectra deviating from the Brune-type source models tend to have larger scale factor than earthquakes without complexity. Based on these detailed comparisons, we make a few specific suggestions for data processing workflows that could help future studies of source parameters and interpretations.

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1 degrees radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.

Between June 2019 and June 2022, 1192 earthquakes of magnitude ML 0.2–3.7 were recorded by the Oklahoma state seismic network near the town of Quinton in eastern Oklahoma. In our analysis of the area, we find that the earthquake sequence was plausibly enhanced due to adjacent hydraulic fracturing (HF) activity that broadened the influence of wastewater injection at a single disposal well. The initial cataloged seismicity indicated earthquake epicenters aligning as a northeast-striking fault south of northeast-striking mapped faults. However, careful relocation suggests the seismicity epicenters overlay a series of east–west-striking faults that are subparallel to the maximum-horizontal stress direction. We reanalyze the seismicity to establish the interaction between HF and wastewater disposal (WD) and their role in inducing seismicity in the area. We relocated earthquakes using a double-difference scheme and a local 1D velocity model to reveal three fault segments oriented in a northwest direction. The spatiotemporal occurrence of seismicity suggests increased pore pressure from the point of injection as the primary driver of seismicity. HF is found to “prime” the faults for the WD to induce and propagate seismicity. Furthermore, the preceding HF may have increased the vertical hydraulic connectivity, allowing a direct vertical propagation of fault permeability. The frequency-index method, which utilizes earthquake waveform frequency content, indicates fluid inclusion into the seismogenic faults during the fault reactivation by wastewater injection.

The spectral stress drop is a popular parameter for the simple quantification and characterization of an earthquake source and its expected seismic radiation, enabling investigation of earthquake spatial and temporal variability for larger numbers of events. In addition, spectral measurements are one of the few possible for earthquake characterization and hazard prediction in regions of low seismicity. However, spectral stress-drop estimates are uncertain, especially as recorded earthquakes may be too complex to characterize ideally with a single parameter. Empirical Green's function (EGF) approaches to isolate the earthquake source are widely regarded as one of the best for individual analysis of well-recorded earthquakes. However, analysis decisions related to the selection of stations, EGFs, time windows, frequency bandwidth, and source models can cause discrepancies in resulting estimates of the source spectrum, source time function, and source parameters. We present results following one well-developed EGF approach, and compare it with those from three other independent methods applied to earthquakes in the 2019 Ridgecrest, California, earthquake, sequence selected for the Southern California Earthquake Center /U.S. Geological Survey Community Stress Drop Validation Study. The common data set consists of two weeks of earthquakes from the 2019 Ridgecrest earthquake sequence, including nearly 13,000 events of M 1 and greater, recorded on stations within 100 km. We obtain estimates of corner frequency and spectral stress drop for 75 earthquakes (M 2.2-4.6) and find varying degrees of similarity among studies. We investigate four events in detail (M 2.7-4.1) and find that we obtain consistent results when the sources are relatively simple. Multiple EGFs produce good ratios and source time functions at stations with good azimuthal distribution. This suggests that there is a role for such approaches to resolve the inherent ambiguity in larger scale inversions between source scaling and attenuation and site effects.

SUMMARY Microseismic monitoring is an important technique to obtain detailed knowledge of in-situ fracture size and orientation during stimulation to maximize fluid flow throughout the rock volume and optimize production. Furthermore, considering that the frequency of earthquake magnitudes empirically follows a power law (i.e. Gutenberg–Richter), the accuracy of microseismic event magnitude distributions is potentially crucial for seismic risk management. In this study, we analyse microseismicity observed during four hydraulic fracture treatments of the legacy Cotton Valley experiment in 1997 at the Carthage gas field of East Texas, where fractures were activated at the base of the sand-shale Upper Cotton Valley formation. We perform waveform cross-correlation to detect similar event clusters, measure relative amplitude from aligned waveform pairs with a principal component analysis, then measure precise relative magnitudes. The new magnitudes significantly reduce the deviations between magnitude differences and relative amplitudes of event pairs. This subsequently reduces the magnitude differences between clusters located at different depths. Reduction in magnitude differences between clusters suggests that some attenuation-related biases could be effectively mitigated with relative magnitude measurements. The maximum likelihood method is applied to understand the magnitude frequency distributions and quantify the seismogenic index of the clusters. Statistical analyses with new magnitudes suggest that fractures that are more favourably oriented for shear failure have lower b-value and higher seismogenic index, suggesting higher potential for relatively larger earthquakes, rather than fractures subparallel to maximum horizontal principal stress orientation.

Knowledge of the midcontinent crustal structure of North America is crucial for understanding the evolutionary history of the ancient North American craton as Laurentia grew through accretion similar to 1.5 Ga to 1 Ga. Although Oklahoma has been recognized as a tectonically stable region since the Phanerozoic, its crustal structure records the earlier formation of the Mazatzal and southern Granite-Rhyolite provinces 1.6 Ga to 1.4 Ga. We present results from teleseismic receiver function analysis applied to 221 events recorded on 169 broadband stations in central Oklahoma. Our findings include a Moho depth map of central Oklahoma based on stacked and depth-converted teleseismic P receiver functions. The results are interpreted together with gravimetric and magnetic datasets and a recently established seismic velocity model of the crust. The Moho map shows a generally flat crust-mantle boundary in central Oklahoma with an average depth of 43.5 km while we observe a sudden thickening on the crust of the northwestern part of Oklahoma where the Moho deepens to over 50 km depth. We also find a Mid-lithosphere discontinuity at the upper-most mantle in north-central Oklahoma, presented as a negative phase deepening southeastward from 60 km to 80 km. We further observe an intracrustal discontinuity at the Nemaha uplift and Anadarko shelf regions in a depth range of 17-30 km. The hypothesis of the Mid-continent Rift (MCR) extending into Oklahoma is examined in terms of the crustal structure and Moho depth variation revealed by receiver functions. We do not find evidence of Moho structure or lower-crustal underplay characteristics similar to what has been discovered in the northern part of MCR, but the intracrustal discontinuity that deepens towards the hypothesized MCR region suggests upper-crustal volcanics potentially caused by the extended expansion regime of the failed rift near the south-most termination.

Taking advantage of easy-to-deploy, dense-spacing, and multi-physical measurements (temperature and strain), distributed optical fiber sensing (DOFS) has drawn a lot of attention in the geoscience community. In contrast with traditional sensors, the optical fiber itself is used as both a sensing element and a means of data transfer. Sensing fibers can be easily deployed in harsh environments such as high-pressure and hightemperature downhole.Previous studies have demonstrated the capability of DOFS in multi-scale geoscience tasks related to resource exploration and environmental hazard monitoring. While DOFS shows promise to enhance our capabilities of tackling geoscience problems, there are still challenges and problems associated with its development and applications. This Research Topic reviews many of the recent advances and applications of DOFS and shows how the technology can be improved. We hope this collection may motivate more DOFS developments regards solving geoscience problems.Below presents a short review of the accepted papers on this Research Topic:

High-resolution passive seismic imaging of shallow subsurface structures is often challenged by the scarcity of coherent body-wave energy in ambient noise recorded at surface stations. We show that the autocorrelation (AC) of teleseismic P-wave coda extracted from just one month of continuous recording at 5 Hz geophones can overcome this limitation. We apply this method to investigate the longitudinal subsurface bedrock structure evolution. Both fluvial and glacial processes have been proposed to explain the canyon's genesis and morphology. The teleseismic P-wave coda AC retrieves zero-offset reflections from the shallow (200-500 m depth) basement interface at 120 stations along a 5 km long profile. In addition, we invert interferometrically retrieved surface-wave dispersion for the shear-wave structure of the sedimentary fill. Combined interpretation of these results and other geophysical and well data suggests an overdeepened basement geometry most consistent with glacial processes.

SUMMARY It is well known that large earthquakes often exhibit significant rupture complexity such as well separated subevents. With improved recording and data processing techniques, small earthquakes have been found to exhibit rupture complexity as well. Studying these small earthquakes offers the opportunity to better understand the possible causes of rupture complexities. Specifically, if they are random or are related to fault properties. We examine microearthquakes (M < 3) in the Parkfield, California, area that are recorded by a high-resolution borehole network. We quantify earthquake complexity by the deviation of source time functions and source spectra from simple circular (omega-square) source models. We establish thresholds to declare complexity, and find that it can be detected in earthquakes larger than magnitude 2, with the best resolution above M2.5. Comparison between the two approaches reveals good agreement (>90 per cent), implying both methods are characterizing the same source complexity. For the two methods, 60–80 per cent (M 2.6–3) of the resolved events are complex depending on the method. The complex events we observe tend to cluster in areas of previously identified structural complexity; a larger fraction of the earthquakes exhibit complexity in the days following the Mw 6 2004 Parkfield earthquake. Ignoring the complexity of these small events can introduce artefacts or add uncertainty to stress drop measurements. Focusing only on simple events however could lead to systematic bias, scaling artefacts and the lack of measurements of stress in structurally complex regions.

During routine operations monitoring Oklahoma earthquakes, we found that certain earthquakes occurred closely both in space and time and had overlapping phases at the recording stations. Through further scrutiny and analysis, we determined that rather than being distinctly different earthquakes, some of the earthquakes exhibited multiphase arrivals and longer than expected coda due to unique ray paths that encounter impedance contrasts such as at the sedimentary rock-basement. Of course, some of these events truly were distinct events, which we term overlapping earthquakes, for which perceived coda duration overlaps and obscures the phase arrivals of the second event due to the source proximity in both time and space. We detail our classification scheme to separate the local earthquakes in Oklahoma as single, overlapping earthquakes, or those associated with multiphase arrivals. We forward model seismic wave propagation in a 2D crustal model and develop a methodology that utilizes waveform correlation to distinguish phases from overlapping earthquakes to those from crustal reverberations. Duration analysis shows a more elongated duration, qualitatively similar to the duration produced by overlapping earthquakes, at the sites where multiphase arrivals are observed.

To better quantify how injection, prior seismicity, and fault properties control rupture growth and propagation of induced earthquakes, we perform a finite‐fault slip inversion on a M w 4.0 earthquake that occurred in April 2015, the largest earthquake in an induced sequence near Guthrie, Oklahoma. The slip inversion reveals a rupture with slip patches that are anti‐correlated to the locations of prior seismicity. The prior seismicity driven by low pore pressure changes and static stress changes occurred on weaker portions of the fault, while the M w 4.0 earthquake likely ruptured relatively stronger portions of the fault. To resolve if pore pressure changes or the initial underlying stress distribution and fault strength controlled the final slip distribution of the Guthrie M w 4.0 earthquake, we compare strike‐slip events of similar magnitude from tectonically active regions and previously inactive regions. Earthquakes on reactivated faults exhibit different slip distributions than active regions, they have more prominent and well separated slip patches, a behavior often associated with faults of lower fault maturity. Pore pressure shows little effect on the distributions. These observations suggest that the initial underlying stress distribution and fault strength of reactivated faults in low deformation regions is the primary controlling factor of the slip distribution with pore pressure perturbations and earthquake interactions being secondary. Therefore, Guthrie M w 4.0 earthquakes slip distribution was enhanced by pore‐pressure perturbations and earthquake interactions by creating an optimal stress state for its failure, but the slip distribution itself is controlled by its fault's initial stress and strength state.