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Research about Ridgecrest, California

Source-linked reports with geographic coverage including Ridgecrest, California.

At least 19 recordsLinked to original sources
USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

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USGS2025

Near-fault amplification and ground motion variability during the 2019 Ridgecrest, California sequence

We estimate ground-motion variability near the 2019 M 7.1 Ridgecrest earthquake sequence. Accurate seismic hazard estimation requires understanding ground-motion spatial correlations, yet many studies lack the dense station coverage needed to resolve small-scale variability. The 2019 M 7.1 Ridgecrest earthquake sequence presents a unique opportunity to examine ground motions and their spatial correlations at a range of interstation distances. The permanent seismic network was augmented with hundreds of temporary stations including several fault-crossing nodal arrays. We compute the event ( δE i ) and within-event ( δW ij ) residuals from the observed peak ground velocity and peak ground acceleration data to isolate potential sources of ground-motion variability. We then compare δW ij between station pairs that record an event to understand the semivariance of the ground motion versus interstation distance. By fitting an exponential model to the semivariances, we determine a correlation range of 25 km for the Ridgecrest region. Although the exponential model fits the broad-scale increase of semivariance with interstation distance, we also observe smaller-scale trends. We find that ground motions are less correlated for station pairs that are near or across faults that ruptured during the 2019 Ridgecrest sequence. We also find large, positive median δW ij with relative values 2–3 times larger than nearby stations for individual stations’ near-fault traces. Near-fault amplification and greater ground-motion variability can delineate fault zones and may locally increase the seismic hazard.

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USGS2025

Mechanics and statistics of postseismic shaking

Analysis of two weeks of continuous post-seismic shaking after the 2019 M7.1 Ridgecrest, CA earthquake sequence using 4 nearby borehole seismometers reveals that continuous ground motions decay as Omori’s law in time and follow the Gutenberg-Richter distribution in logarithmic amplitude. The measured temporal decay in amplitudes agrees with predictions of the rate-and-state framework and indicates shaking amplitudes are proportional to the velocity of afterslip. Our ground motion-based statistical framework provides a basis to forecast shaking intensity in the minutes to hours after a large earthquake.

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USGS2025

Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study

We illustrate the systematic difference between moment magnitude and local magnitude caused by underlying earthquake source physics, using seismic moments submitted to the Statewide California Earthquake Center/United States Geological Survey Community Stress Drop Validation Study 2019 Ridgecrest data set. While the relationship between seismic moment and moment magnitude ( M or M w ) of log 10 ( M 0 ) ~ 1.5* M is uniformly valid for all earthquake sizes by definition (Hanks and Kanamori, 1979), the relationship between local magnitude M L and moment is itself magnitude dependent. For moderate events, ~3< M < ~6, M and M L are coincident; for earthquakes smaller than ~3, M L ~ 1.0 log 10 M 0 (Hanks and Boore, 1984). This is a physical consequence of the corner frequency fc becoming larger than the upper frequency of observation and implies that M L and M differ systematically by a factor of 1.5 for these small events. While this idea is not new, we propose a new, continuous relationship between local magnitude and moment, for magnitudes 2 to 6 which extrapolates to smaller and larger magnitudes, applicable to southern California specific to the Ridgecrest region. We make use of the plethora of seismic moments as submitted by many participants of the Community Stress Drop study, compared to the Southern California Seismic Network (SCSN) catalog magnitudes. Overall, the seismic moments in the Community Study recover moment magnitude well, so we use our new M L - M 0 to convert M L to M , refining the SCSN operational M Lr scale. This systematic difference of 50% in slope between local and moment magnitude at small magnitudes has implications for spectral stress drop estimates, earthquake ground motion modeling, as well as other magnitude scales and earthquake occurrence statistics.

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USGS2022

Ready for real time: Performance of Global Navigation Satellite System in 2019 Mw 7.1 Ridgecrest, California, rapid response products

Global Navigation Satellite Systems (GNSSs) have undergone notable advancement in the last few decades, leading to the availability of a dataset with capabilities well beyond its original intended purpose. The proliferation of high‐rate (1 Hz or greater) GNSS receivers in areas of seismological interest now allows for routine consideration of dynamic earthquake ground motions, with centimeter‐level displacement accuracy via precise point positioning methods. Real‐time (RT) GNSS observations, from stations that are both telemetered and processed to displacement with minimal latency, have lower accuracy compared to post‐processed (PP) GNSS displacements due to imprecise knowledge of atmospheric conditions, satellite clocks, and satellite orbits in RT. Whether the quality of RT high‐rate GNSS is sufficient for use in rapid response products remains to be thoroughly examined. Here, we highlight RT GNSS displacement time series processed during the 2019 M w "> M w Mw 7.1 Ridgecrest, California, earthquake in the context of common rapid‐response products, magnitude estimation, and kinematic fault‐slip models. We discuss how these data can be used to supplement RT seismic data for rapid characterization of significant earthquakes. We find that kinematic fault‐slip models using RT GNSS data retain the general spatiotemporal characteristics of those with PP data, with subtle differences in size and amplitude of modeled slip asperities. We demonstrate the effect of these rapid seismic source models using RT GNSS data on the U.S. Geological Survey product ShakeMap—a downstream ground‐motion prediction algorithm informed by the rupture dimensions estimated in the slip model. Discrepancies in the ShakeMap estimate are minor, within ±12% change, with the most severe variation at the fault edges. Our analysis suggests that, when used in conjunction with available seismic data sources, RT GNSS is sufficient and valuable for rapid earthquake characterization.

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USGS2021

Post-seismic relaxation following the 2019 Ridgecrest, California, earthquake sequence

The 2019 Ridgecrest, California, earthquake sequence involved predominantly right‐lateral strike slip on a northwest–southeast‐trending subvertical fault in the 6 July M 7.1 mainshock, preceded by left‐lateral strike slip on a northeast–southwest‐trending subvertical fault in the 4 July M 6.4 foreshock. To characterize the postseismic deformation, we assemble displacements measured by Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar. The geodetic measurements illuminate vigorous postseismic deformation for at least 21 months following the earthquake sequence. The postseismic transient deformation is particularly well constrained from survey‐mode GPS (sGPS) in the epicentral region carried out during the weeks after the mainshock. We interpret these observations with mechanical models including afterslip and viscoelastic relaxation of the lower crust and mantle asthenosphere. During the first 21 months, up to several centimeters of horizontal motions are measured at continuous GPS and sGPS sites, with amplitude that diminishes slowly with distance from the mainshock rupture, suggestive of deeper afterslip or viscoelastic relaxation. We find that although afterslip involving right‐lateral strike slip along the mainshock fault traces and their deeper extensions reach a few decimeters, most postseismic deformation is attributable to viscoelastic relaxation of the lower crust and mantle. Within the Basin and Range crust and mantle, we infer a transient lower crust viscosity several times that of the mantle asthenosphere. The transient mantle asthenosphere viscosity is &#x223C; 1.3 &#xD7; 10 17 &#x2009;&#x2009; Pa &#x2009; s ">

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USGS2021

Refinements to the Graves–Pitarka kinematic rupture generator, including a dynamically consistent slip‐rate function, applied to the 2019 Mw 7.1 Ridgecrest earthquake

The main objective of this study is to develop physics‐based constraints on the spatiotemporal variation of the slip‐rate function using a simplified dynamic rupture model. First, we performed dynamic rupture modeling of the 2019 M w 7.1 Ridgecrest, California, earthquake, to analyze the effects of depth‐dependent stress and material friction on slip rate. Then, we used our modeling results to guide refinements to the slip‐rate function that were implemented in the Graves–Pitarka kinematic rupture generation technique. The dynamic ruptures were computed on a surface‐rupturing, planar strike‐slip fault that includes a weak (negative to low‐stress‐drop) zone in the upper 4 km of the crust. Below the weak zone, we placed high‐stress‐drop patches designed to mirror the large‐slip areas seen in various rupture model inversions of the event. The locations of the high‐stress‐drop patches and the hypocenter were varied in multiple realizations to investigate how changing the dynamic conditions affected the resulting rupture kinematics, in particular, the slip rate. From these simulations, we observed a systematic change in the shape of the slip‐rate function from Kostrov type below the weak zone to a predominantly symmetric shape within the weak zone, along with a depth‐dependent reduction of peak slip rate. We generalized these shallow rupture features into a depth‐dependent parametric variation of the slip‐rate function and implemented it in the Graves–Pitarka kinematic rupture model generator. The performance of the updated kinematic approach was then verified in 0–4 Hz simulations of the M w 7.1 Ridgecrest earthquake, which showed that incorporating the depth‐dependent variation in the shape of the slip‐rate function improves the fit to the observed near‐fault ground motions in the 0.5–3 s period range.

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USGS2021

Diffuse deformation and surface faulting distribution from sub-metric image correlation along the 2019 Ridgecrest ruptures (California, USA)

The 2019 M w "> M w 6.4 and 7.1 Ridgecrest, California, earthquake sequence (July 2019) ruptured consecutively a system of high‐angle strike‐slip cross faults (northeast‐ and northwest‐trending) within 34 hr. The complex rupture mechanism was illuminated by seismological and geodetic data, bringing forward the issue of the interdependency of the two fault systems both at depth and at the surface, and of its effect on the final surface displacement pattern. Here, we use high‐resolution (WorldView and Pleiades) optical satellite image correlation to measure the near‐fault horizontal and vertical surface displacement fields at 0.5 m ground resolution for the two earthquakes. We point out significant differences with previous geodetic‐ and geologic‐based measurements, and document the essential role of distributed faulting and diffuse deformation in producing the observed surface displacement patterns. We derive strain fields from the horizontal displacement maps, and highlight the predominant role of rotation and shear strain in the surface rupture process. We discuss the segmentation of the rupture based on the fault geometry and along‐strike slip variations. We also image several northeast‐trending faults with similar orientation to the deeply embedded shear fabric identified in aftershock studies, and show that these cross faults are present all along the rupture, including at a scale <100 m. Finally, we compare our results to kinematic slip inversions, and show that the surface diffuse deformation is primarily associated with areas of shallow slip deficit; however, this diffuse deformation cannot be explained using elastic modeling. We conclude that inelastic processes play an important role in contributing to the total surface deformation associated with the 2019 Ridgecrest sequence.

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USGS2021

Detailed traveltime tomography and seismic catalog around the 2019 Mw7.1 Ridgecrest, California, earthquake using dense rapid-response seismic data

We derive a detailed earthquake catalogue and V p , V s and V p / V s models for the region around the 2019 M w 6.4 and M w 7.1 Ridgecrest, California, earthquake sequence using data recorded by rapid-response, densely deployed sensors following the Ridgecrest main shock and the regional network. The new catalogue spans a 4-month period, starting on 1 June 2019, and it includes nearly 95 000 events detected and located with iterative updates to our velocity models. The final V p and V s models correlate well with surface geology in the top 4 km of the crust and spatial seismicity patterns at depth. Joint interpretation of the derived catalogue, velocity models, and surface geology suggests that (i) a compliant low-velocity zone near the Garlock Fault arrested the M w 7.1 rupture at the southeast end; (ii) a stiff high-velocity zone beneath the Coso Mountains acted as a strong barrier that arrested the rupture at the northwest end and (iii) isolated seismicity on the Garlock Fault accommodated transtensional-stepover strain triggered by the main events. The derived catalogue and velocity models can be useful for multiple future studies, including further analysis of seismicity patterns, derivations of accurate source properties (e.g. focal mechanisms) and simulations of earthquake processes and radiated seismic wavefields.

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USGS2021

Temporal seismic velocity variations: Recovery following from the 2019 Mw 7.1 Ridgecrest, California earthquake

We investigated seismic velocity changes ( dv / v ) associated with the 2019 Ridgecrest earthquake sequence with high‐frequency autocorrelations of ambient seismic noise data. Daily autocorrelation functions were computed for the entirety of 2019 and the first quarter of 2020 for broadband stations within the region, including the temporary broadband stations installed during the aftershock deployment. Travel time shifts in the daily autocorrelation functions, relative to the mean autocorrelation waveform, were computed to produce dv / v time series, which are sensitive to the evolving material properties of the shallow crust surrounding the Ridgecrest fault zone (RFZ). A short‐term velocity drop follows the M w 7.1 earthquake at stations in the vicinity of the rupture surface, while those greater than 50 km away showed no such drop. The maximum, absolute changes in seismic velocity are proportional to the logarithm of distance from the fault rupture and to the peak dynamic strain experienced during the earthquake. Near the areas of the highest coseismic slip within the RFZ, seismic velocities recovered over 3 months. However, in the vicinity of the nearby Garlock fault, where triggered slip manifested, and north of the RFZ, seismic velocities recovered within a month. We interpret the seismic velocity changes and their recovery to be largely due to changes in the physical properties of the shallow crust, such as fault zone damage recovery caused by the earthquake rupture process and in response to the large dynamic stresses of passing seismic waves from the mainshock.

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USGS2021

Coseismic surface displacement in the 2019 ridgecrest earthquakes: Comparison of field measurements and optical image correlation results

A fundamental topic in earthquake studies is understanding the extent to which fault rupture at the surface is localized on primary fault strands as opposed to distributed tens to hundreds of meters away from primary ruptures through off‐fault deformation (OFD) via a combination of discrete secondary faulting and bulk deformation. The 2019 Ridgecrest, CA M w 6.4 and M w 7.1 earthquakes provide an opportunity to explore this problem via comparison of published field‐based and mostly on‐fault offset measurements, with new lateral displacement measurements made over length scales of hundreds of meters across the primary rupture using WorldView satellite images collected before and after the earthquakes. A mean fit to the field observations underestimates net slip (pixel‐correlation results) by an average of 0.4 m along the M w 6.4 rupture (∼41% of net left‐lateral displacement) and 0.7 m along the M w 7.1 rupture (∼65% of net right‐lateral displacement). We attribute these differences to substantial OFD along the lengths of the M w 6.4 (∼59%) and M w 7.1 (∼35%) ruptures. A maximum fit to the field observations provides an improved match to the pixel correlation results (difference of 0.1–0.2 m or ∼76%–98% of net displacement). OFD may in part depend on displacement magnitude, where locations along the rupture with smaller displacements (<2 m) are characterized by a greater percentage of OFD. Furthermore, OFD increases with rupture zone width, especially at the scale of individual rupture strands (hundreds of meters). These findings contribute to a growing understanding of the important role of OFD in accommodating deformation near the surface.

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USGS2020

Nodal seismograph recordings of the 2019 Ridgecrest Earthquake Sequence

The 2019 Ridgecrest, California earthquake sequence included M w 6.4 and Mw 7.1 earthquakes that occurred on successive days beginning on 4 July 2019. These two largest earthquakes of the sequence occurred on orthogonal faults that ruptured the Earth’s surface. To better evaluate the 3D subsurface fault structure, ( P - and S -wave) velocity, 3D and temporal variations in seismicity, and other important aspects of the earthquake sequence, we recorded aftershocks and ambient noise using up to 461 three-component nodal seismographs for about two months, beginning about one day after the M w 7.1 mainshock. The ~ 30,000 M w ≥1 earthquakes that were recorded on the dense arrays provide an unusually large volume of data with which to evaluate the earthquake sequence. This report describes the recording arrays and is intended to provide metadata for researchers interested in evaluating various aspects of the 2019 Ridgecrest earthquake sequence using the nodal data set.

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USGS2020

Documentation of Surface Fault Rupture and Ground‐Deformation Features Produced by the 4 and 5 July 2019 Mw 6.4 and Mw 7.1 Ridgecrest Earthquake Sequence

The M w "> M w Mw 6.4 and M w "> M w Mw 7.1 Ridgecrest earthquake sequence occurred on 4 and 5 July 2019 within the eastern California shear zone of southern California. Both events produced extensive surface faulting and ground deformation within Indian Wells Valley and Searles Valley. In the weeks following the earthquakes, more than six dozen scientists from government, academia, and the private sector carefully documented the surface faulting and ground‐deformation features. As of December 2019, we have compiled a total of more than 6000 ground observations; approximately 1500 of these simply note the presence or absence of fault rupture or ground failure, but the remainder include detailed descriptions and other documentation, including tens of thousands of photographs. More than 1100 of these observations also include quantitative field measurements of displacement sense and magnitude. These field observations were supplemented by mapping of fault rupture and ground‐deformation features directly in the field as well as by interpreting the location and extent of surface faulting and ground deformation from optical imagery and geodetic image products. We identified greater than 68 km of fault rupture produced by both earthquakes as well as numerous sites of ground deformation resulting from liquefaction or slope failure. These observations comprise a dataset that is fundamental to understanding the processes that controlled this earthquake sequence and for improving earthquake hazard estimates in the region. This article documents the types of data collected during postearthquake field investigations, the compilation effort, and the digital data products resulting from these efforts.

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USGS2020

Liquefaction and related ground failure from July 2019 Ridgecrest earthquake sequence

The 2019 Ridgecrest earthquake sequence produced a 4 July M 6.5 foreshock and a 5 July M 7.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24 hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non‐ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar‐based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre‐ and postevent mapping of liquefaction hazards. We describe liquefaction and ground‐failure features in Trona and Argus, which produced lateral deformations and impacts on several single‐story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction‐related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.

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USGS2020

Pseudo-prospective evaluation of UCERF3-ETAS forecasts during the 2019 Ridgecrest sequence

The 2019 Ridgecrest sequence provides the first opportunity to evaluate Uniform California Earthquake Rupture Forecast v.3 with epidemic‐type aftershock sequences (UCERF3‐ETAS) in a pseudoprospective sense. For comparison, we include a version of the model without explicit faults more closely mimicking traditional ETAS models (UCERF3‐NoFaults). We evaluate the forecasts with new metrics developed within the Collaboratory for the Study of Earthquake Predictability (CSEP). The metrics consider synthetic catalogs simulated by the models rather than synoptic probability maps, thereby relaxing the Poisson assumption of previous CSEP tests. Our approach compares statistics from the synthetic catalogs directly against observations, providing a flexible approach that can account for dependencies and uncertainties encoded in the models. We find that, to the first order, both UCERF3‐ETAS and UCERF3‐NoFaults approximately capture the spatiotemporal evolution of the Ridgecrest sequence, adding to the growing body of evidence that ETAS models can be informative forecasting tools. However, we also find that both models mildly overpredict the seismicity rate, on average, aggregated over the evaluation period. More severe testing indicates the overpredictions occur too often for observations to be statistically indistinguishable from the model. Magnitude tests indicate that the models do not include enough variability in forecasted magnitude‐number distributions to match the data. Spatial tests highlight discrepancies between the forecasts and observations, but the greatest differences between the two models appear when aftershocks occur on modeled UCERF3‐ETAS faults. Therefore, any predictability associated with embedding earthquake triggering on the (modeled) fault network may only crystalize during the presumably rare sequences with aftershocks on these faults. Accounting for uncertainty in the model parameters could improve test results during future experiments.

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USGS2020

ShakeAlert Earthquake Early Warning System Performance During the 2019 Ridgecrest Earthquake Sequence

During July 2019, a sequence of earthquakes including a Mw6.4 foreshock and a Mw7.1 mainshock occurred near Ridgecrest, California. ShakeAlert, the U.S. Geological Survey (USGS) ShakeAlert public Earthquake Early Warning (EEW) system being developed for the U.S. West Coast, was operational during this time, though public alerting was only available within LA County. ShakeAlert created alert messages for many of the earthquakes, including the two largest events, and for many of the larger aftershocks. In this study, we dissect log files and replay data through the system to reconstruct the sequence of events and analyze the performance of the system during that time period. While the system performed reasonably well overall, the sequence also revealed various issues and short comings that will be addressed in impending and future system upgrades, with most parts of the system working as they should. ShakeAlert correctly detected and rapidly characterized both the Mw6.4 and Mw7.1 earthquakes within 6.9 s of their origin times and created alert messages that were available to ShakeAlert’s pilot users. No public alerts were sent out by the ShakeAlertLA cellphone app (the only publicly available alerting method at the time) because the predicted shaking for LA County was below the app’s alerting threshold of MMI 4.0. For the Mw6.4 event this was accurate. For the Mw7.1 event, public alerts for LA County were warranted, but ShakeAlert underpredicted the shaking levels because both the point-source and the finite-fault algorithms underestimated the magnitude of the earthquake by 0.8 units. A number of software and hardware issues that were responsible for the magnitude underestimation of the mainshock have been identified and will be addressed in future ShakeAlert releases. We also analyze the hypothetical alerting performance of ShakeAlert had public alerting been available throughout southern California with a lower alerting threshold of 〖MMI〗_alert=2.5MMI 2.5. We find that, despite the magnitude underestimation, ShakeAlert could have provided timely warnings to a large fraction of affected sites, including some of the near-epicentral sites with high ground motion intensities.

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USGS2020

Evidence of previous faulting along the 2019 Ridgecrest, California earthquake ruptures

The July 2019 Ridgecrest earthquake sequence in southeastern California was characterized as surprising because only ~35% of the rupture occurred on previously mapped faults. Employing more detailed inspection of pre-event high-resolution topography and imagery in combination with field observations, we document evidence of active faulting in the landscape along the entire fault system. Scarps, deflected drainages, and lineaments and contrasts in topography, vegetation, and ground color demonstrate previous slip on a dense network of orthogonal faults, consistent with patterns of surface rupture observed in 2019. Not all of these newly mapped fault strands ruptured in 2019. Outcrop-scale field observations additionally reveal tufa lineaments and sheared Quaternary deposits. Neotectonic features are commonly short (<2 km), discontinuous, and display en echelon patterns along both the M 6.4 and M 7.1 ruptures. These features are generally more prominent and better preserved outside the late Pleistocene lake basins. Fault expression may also be related to deformation style: scarps and topographic lineaments are more prevalent in areas where substantial vertical motion occurred in 2019. Where strike-slip displacement dominated in 2019, the faults are mainly expressed by less prominent tonal and vegetation features. Both the NE- and NW-trending active fault systems are subparallel to regional bedrock fabrics that were established as early as ~150 Ma, and may be reactivating these older structures. Overall, we estimate that 50-70% (i.e., an additional 15-35%) of the 2019 surface ruptures could have been recognized as active faults with detailed inspection of pre-event data. Similar detailed mapping of potential neotectonic features could help improve seismic hazard analyses in other regions of eastern California and elsewhere that have distributed faulting or incompletely mapped faults. In areas where faults cannot be resolved as single thoroughgoing structures, a zone of potential faulting should be used as a hazard model input.

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