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USGS2020

Types and areal distribution of ground failure associated with the 2019 Ridgecrest, California, earthquake sequence

T he July 2019 Ridgecrest, California, earthquake sequence included the largest earthquake ( M 7.1) to strike the conterminous United States in the past 20 yr. To characterize the types, numbers, and areal distributions of different types of ground failure (landslides, liquefaction, and ground cracking), I conducted a field investigation of ground failure triggered by the sequence around the periphery of the epicentral area (which had limited access). The earthquake sequence triggered sparse and widely scattered landslides over an area of ∼ 22 , 000    km 2 "> ∼ 22 , 000 km 2 ∼22,000 km2 and at a maximum epicentral distance of 114 km; these metrics are within the upper bounds as compared with global averages for earthquakes of similar size. Some rock falls blocked primary and secondary roads, but no other landslide damage was reported. Almost all of the landslides in the peripheral area were small rock falls ( ⁠ ∼ 1 – 10    m 3 "> ∼ 1 – 10 m 3 ∼1–10 m3 ⁠ ), but a few larger ( ⁠ ∼ 100    m 3 "> ∼ 100 m 3 ∼100 m3 ⁠ ) rock slides also occurred. Though there are only informal reports about ground failure in the immediate epicentral area and we lack a detailed survey there, the small number (hundreds) and size of the landslides still seems to be far below global averages for M 7.1. This could be a result of the arid landscape and lack of a deeply weathered zone of soil and regolith. Liquefaction occurred along part of the western margin of Searles Valley. One large ( ⁠ ∼ 0.4    km 2 "> ∼ 0.4 km 2 ∼0.4 km2 ⁠ ) lateral spread caused by liquefaction severely damaged parts of Trona. Minor liquefaction also occurred in a ∼ 100 ‐ m ‐ wide "> ∼ 100 ‐ m ‐ wide ∼100‐m‐wide band along the fault‐rupture zone in some places.

California↗

USGS2020

Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence

We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence ( Rekoske et al. , 2019 , 2020 ) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014 ; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and find strong correlation with our event terms, indicating that they are consistent with source processes. We look for physically meaningful trends in the partitioned residuals and test the ability of BSSA14 to capture the behavior we observe in the data. We find that BSSA14 is a good match to the median observations for M > 4 "> M > 4 M>4 ⁠ . However, we find bias for individual events, especially those with small magnitude and hypocentral depth ≥ 7    km "> depth ≥ 7 km depth≥7 km ⁠ , for which peak ground acceleration is underpredicted by a factor of 2.5. Although the site amplification term captures the median site response when all sites are considered together, it does not capture variations at individual stations across a range of site conditions. We find strong basin amplification in the Los Angeles, Ventura, and San Gabriel basins. We find weak amplification in the San Bernardino basin, which is contrary to simulation‐based findings showing a channeling effect from an event with a north–south azimuth. This and an additional set of ground motions from earthquakes southwest of Los Angeles suggest that there is an azimuth‐dependent southern California basin response related to the orientation of regional structures when ground motion from waves traveling south–north are compared with those in the east–west direction. These findings exhibit the power of large, spatially dense ground‐motion datasets and make clear that nonergodic models are a way to reduce bias and uncertainty in ground‐motion estimation for applications like the U.S. Geological Survey National Seismic Hazard Model and the ShakeAlert earthquake early warning System.

California↗

USGS2020

A stress-similarity triggering model for aftershocks of the MW6.4 and MW7.1 Ridgecrest earthquakes

The July 2019 M w "> M w 6.4 and 7.1 Ridgecrest earthquakes triggered numerous aftershocks, including clusters of off‐fault aftershocks in an extensional stepover of the Garlock fault, near the town of Olancha, and near Panamint Valley. The locations of the off‐fault aftershocks are consistent with the stress‐similarity model of triggering, which hypothesizes that aftershocks preferentially occur in areas where the mainshock static stress change tensor is similar in orientation to the background stress tensor. The background stress field is determined from the inversion of earthquake focal mechanisms, with the spatial resolution adapted to the local density of earthquakes. The mainshock static stress change is computed using finite‐source models for the M w "> M w 6.4 foreshock and M w "> M w 7.1 mainshock. I quantify the similarity between these two stress fields using the tensor dot product of the normalized deviatoric stress tensors. The off‐fault aftershocks in the Garlock stepover and the Olancha area fall within lobes of positive stress similarity, whereas the aftershocks near Panamint Valley are partially within a lobe. The cluster in the Garlock fault stepover and the smaller of two clusters near Olancha occur in regions of locally anomalous background stress that results in higher stress similarity. I compute the spatial density of M ≥ 2.0 "> M≥2.0 aftershocks and find that the aftershock density increases as a function of stress similarity, with a factor of ∼ 15 "> ∼15 difference between high stress‐similarity and low stress‐similarity areas. This result is robust with respect to the choice of mainshock model and the uncertainty of the background stress field. The aftershock density varies substantially inside the high stress‐similarity lobes, however, indicating that other variable background conditions, such as material properties, temperature, and fluid pressure, may also be playing a role. Specifically, temperature and fluid pressure conditions might help explain the low rate of aftershocks in the Coso geothermal field.

California↗

USGS2020

Seismicity rate change at the Coso Geothermal Field following the July 2019 Ridgecrest M7.1 earthquake

Many geothermal and volcanic regions experience remote and regional triggering following large earthquakes. The transient or permanent changes in stresses acting on faults and fractures can lead to changes in seismicity rates following either the passage of teleseismic waves or the permanent change in stresses following regional events. One such region of prevalent triggering is the Coso Geothermal Field (CGF) in eastern California, which is located roughly 30km to the north of the 2019 M7.1 Ridgecrest epicenter. Previous regional earthquakes have seemingly caused increases in seismicity rate surrounding the CGF, but also showed an absence of such rate increases in the CGF itself. To test whether seismicity rates in the CGF were dissimilar to the surrounding area following the M7.1 Ridgecrest earthquake, I carry out seismicity rate change calculations using a catalog of seismicity complied using a local seismic network and find that the CGF behaves identical to the surrounding area. Comparisons of seismicity rate changes calculated using a regional network derived catalog and the local network derived catalog show that for a moderate, regional earthquake, the local network catalog reveals a change in seismicity rate while the regionally network catalog shows no significant changes. The differences are likely related to incomplete sampling of seismicity using the regional network due to the existence of a very shallow brittle ductile transition centered on the CGF. The CGF thus is prone to triggering from both teleseismic and regional earthquakes.

California↗

USGS2020

Kinematics of fault slip associated with the July 4-6 2019 Ridgecrest, Californai earthquakes sequence

The 2019 Ridgecrest, California, earthquake sequence produced observable crustal deformation over much of central and southern California, as well as surface rupture over several tens of kilometers. To obtain a detailed picture of the fault slip involved in the 4 July M 6.4 foreshock and 6 July M 7.1 mainshock, we combine strong‐motion seismic waveforms with crustal deformation observations to obtain kinematic and static slip models of both events. We sample the regional seismic wavefield for both the foreshock and mainshock with three‐component records from 31 stations of the California Integrated Seismic Network. The deformation observations include Global Positioning System (GPS), Interferometric Synthetic Aperture Radar (InSAR), and borehole strainmeter recordings of the dynamic strain field. These data collectively constrain the kinematic coseismic slip distributions of the events, with measurements variously observing coseismic slip from one event (e.g., seismic waveforms, kinematic solutions from continuous GPS, and strainmeter time series) or coseismic slip from both events combined (InSAR). We find that the foreshock ruptured two separate faults, one with left‐lateral strike slip on a northeast–southwest‐trending fault and the other with right‐lateral strike slip on an orthogonal fault, with unilateral rupture propagation along both. The mainshock ruptured a series of northwest–southeast‐trending faults with right‐lateral strike slip concentrated in the uppermost 6 km with exceptionally low‐rupture velocity averaging 1.0 – 1.5    km / s ">

California↗

USGS2020

Magnetic field variations in Alaska: Recording space weather events on seismic stations in Alaska

Seismometers are highly sensitive instruments to not only ground motion but also many other nonseismic noise sources (e.g., temperature, pressure, and magnetic field variations). We show that the Alaska component of the Transportable Array is particularly susceptible to recording magnetic storms and other space weather events because the sensors used in this network are unshielded and magnetic flux variations are stronger at higher latitudes. We also show that vertical‐component seismic records across Alaska are directly recording magnetic field variations between 40 and 800 s period as opposed to actual ground motion during geomagnetic events with sensitivities ranging from 0.004 to 0.48    ( m / s 2 ) / T "> 0.48 ( m / s 2 ) / T 0.48 (m/s2)/T ⁠ . These sensitivities were found on a day where the root mean square variation in the magnetic field was 225 nT. Using a method developed by Forbriger (2007 , his section 3.1), we show that improving vertical seismic resolution of an unshielded sensor by as much as 10 dB in the 100–400 s period band using magnetic data from a collocated three‐component magnetometer is possible. However, due to large spatial variations in Earth’s magnetic field, this methodology becomes increasingly ineffective as the distance between the seismometer and magnetometer increases (no more than 200 km separation). A potential solution to this issue may be to incorporate relatively low‐cost magnetometers as an additional environmental data stream at high‐latitude seismic stations. We demonstrate that the Bartington Mag‐690 sensors currently deployed at Global Seismographic Network sites are not only acceptable for performing corrections to seismic data, but are also capable of recording many magnetic field signals with similar signal‐to‐noise ratios, in the 20–1000 s period band, as the observatory grade magnetometers operated by the U.S. Geological Survey Geomagnetism Program. This approach would densify magnetic field observations and could also contribute to space weather monitoring by supplementing highly calibrated magnetometers with additional sensors.

Alaska↗

USGS2020

Real-time performance of the PLUM earthquake early warning method during the 2019 M6.4 and M7.1 Ridgecrest, California, Earthquakes

We evaluate the timeliness and accuracy of ground‐motion‐based earthquake early warning (EEW) during the July 2019 M 6.4 and 7.1 Ridgecrest earthquakes. In 2018, we began retrospective and internal real‐time testing of the propagation of local undamped motion (PLUM) method for earthquake warning in California, Oregon, and Washington, with the potential that PLUM might one day be included in the ShakeAlert EEW system. A real‐time version of PLUM was running on one of the ShakeAlert EEW system’s development servers at the time of the 2019 Ridgecrest sequence, allowing us to evaluate the timeliness and accuracy of PLUM’s warnings for the M 6.4 and 7.1 mainshocks in real time with the actual data availability and latencies of the operational ShakeAlert EEW system. The latter is especially important because high‐data latencies during the M 7.1 earthquake degraded ShakeAlert’s performance. PLUM proved to be largely immune to these latencies. In this article, we present a retrospective analysis of PLUM performance and explore three potential regional alerting strategies ranging from spatially large regions (counties), to moderate‐size regions (National Weather Service public forecast zones), to high‐spatial specificity (50 km regular geographic grid). PLUM generated initial shaking forecasts for the two mainshocks 5 and 6 s after their respective origin times, and faster than the ShakeAlert system’s first alerts. PLUM was also able to accurately forecast shaking across southern California for all three alerting strategies studied. As would be expected, a cost‐benefit analysis of each approach illustrates trade‐offs between increasing warning time and minimizing the area receiving unneeded alerts. Choosing an optimal alerting strategy requires knowledge of users’ false alarm tolerance and minimum required warning time for taking protective action, as well as the time required to distribute alerts to users.

California↗

USGS2020

Optimizing earthquake early warning alert distance strategies using the July 2019 Mw6.4 and Mw7.1 Ridgecrest, California, earthquakes

The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate median‐expected peak ground motions with distance, then using these ground motions to determine median‐expected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 “Did You Feel It?” survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which ground‐motion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the ground‐motion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating ground‐motion uncertainties, we find MMI IV alert regions using median‐expected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find >94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of ground‐motion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the trade‐off between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.

California↗

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.

California↗

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.

California↗

USGS2020

The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence

Operational earthquake forecasting protocols commonly use statistical models for their recognized ease of implementation and robustness in describing the short-term spatiotemporal patterns of triggered seismicity. However, recent advances on physics-based aftershock forecasting reveal comparable performance to the standard statistical counterparts with significantly improved predictive skills when fault and stress field heterogeneities are considered. Here, we perform a pseudo-prospective forecasting experiment during the first month of the 2019 Ridgecrest (California) earthquake sequence. We develop seven Coulomb rate-and-state models that couple static stress change estimates with continuum mechanics expressed by the rate-and-state friction laws. Our model parametrization supports a gradually increasing complexity; we start from a preliminary model implementation with simplified slip distributions and spatially homogeneous receiver faults to reach an enhanced one featuring optimized fault constitutive parameters, finite-fault slip models, secondary triggering effects, and spatially heterogenous planes informed by pre-existing ruptures. The data-rich environment of Southern California allows us to test whether incorporating data collected in near real-time during an unfolding earthquake sequence boosts our predictive power. We assess the absolute and relative performance of the forecasts by means of statistical tests used within the Collaboratory for the Study of Earthquake Predictability (CSEP) and compare their skills against a standard benchmark ETAS model for the short (24 hours after the two Ridgecrest mainshocks) and intermediate-term (one month). Stress-based forecasts expect heightened rates along the whole near-fault region and increased expected seismicity rates in Central Garlock Fault. Our comparative model evaluation supports that faulting heterogeneities coupled with secondary triggering effects are the most critical success components behind physics-based forecasts, but also underlines the importance of model updates incorporating near real-time available aftershock data reaching better performance than ETAS models. We explore the physical basis behind our results by investigating the localized shut down of pre-existing normal faults in the Ridgecrest near-source area.

California↗

USGS2020

Mild displacements of boulders during the 2019 Ridgecrest Earthquakes

Strong seismic waves from the July 2019 Ridgecrest, California, earthquakes displaced rocks in proximity to the M 7.1 mainshock fault trace at several locations. In this report, we document large boulders that were displaced at the Wagon Wheel Staging Area (WWSA), approximately 4.5 km southeast of the southern terminus of the large M 6.4 foreshock rupture (hereafter “the large foreshock”) and 9 km southwest of the nearest approach of the M 7.1 mainshock surface rupture. Some boulders appear to have slid along essentially flat surfaces, which suggest that dynamic stresses overcame the coefficient of friction. Other boulders appear to have rocked within their sockets. In both cases, we use simple mechanical models to estimate total peak dynamic accelerations between 0.5 and 1g⁠, commensurate with modified Mercalli intensity 9. It is unclear if the strongest shaking at this location occurred during the large foreshock or the M 7.1 mainshock. The inferred accelerations are higher than predicted mainshock ground motions at WWSA, although local high accelerations could have been generated by path, site, or source effects. Gaps between boulders and their sockets are easily visible in the immediate aftermath of earthquakes and provide a quick indication of strong shaking. More importantly, the gaps quickly fill with surficial organic debris, including seeds and leaves of the year, that quickly become entombed. Boulders may thus potentially be extracted to examine gap fillings associated with past earthquakes, providing a new datable paleoseismic method.

California↗

USGS2020

Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake

We model the kinematic rupture process of the 2019 M w "> M w Mw 7.1 Ridgecrest, California, earthquake using numerical simulations to reproduce the elastodynamic wave field observed by inertial seismometers, high‐rate Global Navigation Satellite System stations, and borehole strainmeters. This was the largest earthquake in Southern California in 20 yr and was widely felt throughout the region. The M w "> M w Mw 7.1 mainshock was part of a large sequence of ∼ 30 , 000 "> ∼ 30 , 000 ∼30,000 aftershocks and was notably preceded by an M w "> M w Mw 6.4 foreshock by 34 hr on fault structures that were once poorly understood. A large number of seismic and geodetic instruments measured the rupture process for both events, with many stations located in the near field. Hence, this is a rare opportunity to better understand complex earthquake processes that arise in an immature fault zone using advanced computing. Of the kinematic rupture models that we tested, our preferred is the simplest one that reproduces signals recorded by the three different geophysical datasets; it is composed of four distinct ruptures that progressively migrate to the southeast with delayed initiation times, and typical rupture speeds. This type of model does a better job at matching the recorded ground motions and deformations than does one composed of a continuous rupture with very low‐rupture velocity, as proposed in other studies of this earthquake.

California↗

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.

California↗

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.

California↗

USGS2020

Surface displacement distributions for the July 2019 Ridgecrest, California earthquake ruptures

Surface rupture in the 2019 Ridgecrest, California, earthquake sequence occurred along two orthogonal cross faults and includes dominantly left‐lateral and northeast‐striking rupture in the M w "> M w 6.4 foreshock and dominantly right‐lateral and northwest‐striking rupture in the M w "> M w 7.1 mainshock. We present > 650 "> > 650 field‐based, surface‐displacement observations for these ruptures and synthesize our results into cumulative along‐strike displacement distributions. Using these data, we calculate displacement gradients and compare our results with historical strike‐slip ruptures in the eastern California shear zone. For the M w "> M w 6.4 rupture, we report 96 displacements measured along 18 km of northeast‐striking rupture. Cumulative displacement curves for the rupture yield a mean left‐lateral displacement of 0.3–0.5 m and maximum of 0.7–1.6 m. Net mean vertical displacement based on the difference of down‐to‐the‐west (DTW) and down‐to‐the‐east (DTE) displacement curves is close to zero (0.02 m DTW). The M w "> M w 6.4 displacement distribution shows that the majority of displacement occurred southwest of the intersection with the M w "> M w 7.1 rupture. The M w "> M w 7.1 rupture is northwest‐striking and 50 km long based on 576 field measurements. Displacement curves indicate a mean right‐lateral displacement of 1.2–1.7 m and a maximum of 4.3–7.0 m. Net vertical displacement in the rupture averages 0.3 m DTW. The M w "> M w 7.1 displacement distributions demonstrate that maximum displacement occurred along a 12‐km‐long portion of the fault near the M w "> M w 7.1 epicenter, releasing 66% of the geologically based seismic moment along 24% of the total rupture length. Using our displacement distributions, we calculate kilometer‐scale displacement gradients for the M w "> M w 7.1 rupture. The steepest gradients ( ⁠ ∼ 1 – 3    m / km "> ∼ 1 – 3 m / km ⁠ ) flank the 12‐km‐long region of maximum displacement. In contrast, gradients for the 1992 M w "> M w 7.3 Landers and 1999 M w "> M w 7.1 Hector Mine earthquakes are < 0.6    m / km "> < 0.6 m / km . Our displacement distributions are important for understanding the influence of cross‐fault rupture on M w "> M w 6.4 and 7.1 rupture length and displacement and will facilitate comparisons with distributions generated remotely and at broader scales.

California↗

USGS2020

Minimal clustering of injection-induced earthquakes observed with a large-n seismic array

The clustering behavior of injection‐induced earthquakes is examined using one month of data recorded by the LArge‐ n Seismic Survey in Oklahoma (LASSO) array. The 1829‐node seismic array was deployed in a 25 km×32 km area of active saltwater disposal in northern Oklahoma between 14 April and 10 May 2016. Injection rates in the study area are nearly constant around the time of the deployment. We develop a local magnitude ( ⁠ M L ⁠ ) equation for the region and estimate magnitudes for 1104 earthquakes recorded by the deployment. The determined earthquake magnitudes range from M L 0.01 to 3.0. The majority of earthquakes occurred between 1.5 and 5.5 km depth, and the shallowest earthquake depths overlap with the base of injection wells at depths between 1.5 and 2.5 km. We compute focal mechanisms of the largest events ( ⁠ M L > 2.0 ⁠ ), and find a mix of normal‐ and strike‐slip‐faulting types. Earthquakes occur regularly in time during the deployment, but are not evenly distributed in space across the study area, that is, they are spatially clustered. Analysis of the nearest‐neighbor distances in the space–time–magnitude domain shows the seismicity is dominated by single‐event clusters (i.e., independent events). This high proportion of single‐event clusters compared with multievent clusters has been previously noted for induced events at geothermal sites. When clustering occurs, the number of events in a cluster is typically small. We observe only four clusters with 10 or more events. For these larger clusters, we find equivalent numbers of foreshocks and aftershocks; however, the foreshock sequences are significantly longer in duration lasting days to tens of days, while aftershock sequences are observed only on the order of one day. The minimal clustering observed for events in the LASSO array suggests that the majority of events are being directly driven by stress changes due to local saltwater disposal.

Oklahoma↗

USGS2020

Factors influencing the probability of hydraulic fracturing induced seismicity in Oklahoma

Injection‐induced seismicity became an important issue over the past decade, and although much of the rise in seismicity is attributed to wastewater disposal, a growing number of cases have identified hydraulic fracturing (HF) as the cause. A recent study identified regions in Oklahoma where ≥75% of seismicity from 2010 to 2016 correlated with nearly 300 HF wells. To identify factors associated with increased probability of induced seismicity, we gathered publicly available information about the HF operations in these regions including: injected volume, number of wells on a pad, injected fluid (gel vs. slickwater), vertical depth of the well, proximity of the well to basement rock, and the formation into which the injection occurred. To determine the statistical strength of the trends, we applied logistic regression, bootstrapping, and odds ratios. We see no trend with total injected volume in our Oklahoma dataset, in contrast to strong trends observed in Alberta and Texas, but we note those regions have many more multiwell pads leading to larger cumulative volumes within a localized area. We found a > ∼50% lower probability of seismicity with the use of gel compared to slickwater. We found that HF wells targeting older formations had a higher probability of seismicity; however, these wells also tend to be deeper, and we found the trend with well depth to be stronger than the trend with age of formation. When isolated to the Woodford formation, well depth produced the strongest relationship, increasing from ∼5%> to ∼50% probability from 1.5 to 5.5 km. However, no trend was seen in the proximity to basement parameter. Based on previously measured pore pressure gradients, we interpret the strong absolute depth relationship to be a result of the increasing formation overpressure measured in deeper portions of the basin that lower the stress change needed to induce seismicity.

Oklahoma↗