Geology topics

Eric M. Thompson

Publications and source records attributed to Eric M. Thompson.

At least 55 records · Page 3Linked to original sources

USGS2021

Automated detection of clipping in broadband earthquake records

Because the amount of available ground‐motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground‐motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near‐real‐time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.

Seismological Research Letters↗

USGS2021

The impact of 3D finite‐fault information on ground‐motion forecasting for earthquake early warning

We identify aspects of finite‐source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite‐fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D‐source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line‐source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D‐source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground‐motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.

Bulletin of the Seismological Society of America↗

USGS2021

Basin and site effects in the U.S. Pacific Northwest estimated from small‐magnitude earthquakes

Earthquake hazards in the U.S. Pacific Northwest (PNW) are increased by the presence of deep sedimentary basins that amplify and prolong ground shaking. To better understand basin and site effects on ground motions, we compile a database of recordings from crustal and intraslab earthquakes. We process 8028 records with magnitudes from 3.5 to 6.8 and hypocentral depths up to 62 km to compute Fourier amplitude spectra of ground acceleration for frequencies of 0–20 Hz. We compute residuals relative to the Bayless and Abrahamson (2019; hereafter, BA18) ground‐motion model and perform a series of linear, crossed, mixed‐effects regressions. In addition to estimating the bias, event, and site terms, we incorporate groupings for broad regionalized site response in three different regions (Seattle basin, Puget Lowland, non‐Puget Lowland), for effects from seismotectonic regime (crustal and intraslab sources), and for interactions between the regions and seismotectonic regimes. We find that the scaling of site response with respect to V S 30 (time‐averaged shear‐wave velocity from the surface to a depth of 30 m) and to basin depth indicators Z 1.0 and Z 2.5 (depths to the 1.0 and 2.5 km/s shear‐wave velocity horizons) is generally consistent with BA18; however, the region terms display strong spatial amplification patterns. For frequencies less than 5 Hz, the Seattle basin amplifies ground motions up to a factor of four, relative to the non‐Puget Lowland, with a maximum amplification around near 0.5 Hz. Sites in the Puget Lowland amplify low frequencies up to a factor of 2.5. At higher frequencies ( ⁠ f > 5 Hz ⁠ ), the Puget Lowland and Seattle basin show regional deamplification of ground motions, with the smallest average amplification factor of 0.65 occurring at 10.0 Hz. Although we observe slight differences in the seismotectonic regime terms, we find that the region terms are significantly more important for modeling earthquake hazard in the PNW.

Oregon, Washington↗

USGS2021

The US Geological Survey ground failure product: Near-real-time estimates of earthquake-triggered landslides and liquefaction

Since late 2018, the US Geological Survey (USGS) ground failure (GF) earthquake product has provided publicly available spatial estimates of earthquake-triggered landslide and liquefaction hazards, along with the qualitative hazard and population exposure-based alerts for M > 6 earthquakes worldwide and in near real time (within ∼30 min). Earthquake losses are oftentimes greatly aggravated by the impacts due to ground failure, yet those particular events with dramatic additional losses have not, heretofore, been rapidly identifiable. The GF product now provides situational awareness about the potential extent and severity of ground failure in the crucial time period before direct observations are available. We describe our implementation of the GF product and the lessons learned from the earthquakes that have occurred since the GF product was released. We describe the product design process, the underlying GF models, the methods we have developed for modeling uncertainty, and the development of the alert levels. The GF product has been produced in near real time for 320 events over the 2-year period since its public implementation in late 2018 through early 2021. The majority of these events yielded the lowest level (green) alerts for all ground-failure types, with 25 resulting in elevated hazard or exposure to landslides and 47 for liquefaction. In a qualitative comparison between the GF product alerts and GF occurrence information, we found that the product succeeds at assigning appropriate alert levels in the majority of cases. Based on our experience with the product, we have identified the following priorities for future improvements: (1) refinements of the underlying probabilistic models to incorporate severity and explicitly model the type of landslide/liquefaction; (2) development of models for fatalities and economic losses due to ground failure; and (3) estimation of the impacts of ground failure on infrastructure.

Earthquake Spectra↗

USGS2021

ShakeMap operations, policies, and procedures

The US Geological Survey’s ShakeMap is used domestically and globally for post-earthquake emergency management and response, engineering analyses, financial instruments, and other decision-making activities. Recent developments in the insurance, reinsurance, and catastrophe bond sectors link payouts of potentially hundreds of millions of dollars to ShakeMap products. Similarly, building codes, post-earthquake building damage forensic evaluations, and geotechnical evaluations often rely on estimated peak response-spectral values for site-specific evaluations that may lead to costly analyses, retrofits, or other expenditures. Given such activities, financial, engineering, and other technical users demand processing specifications and a metadata trail for actuarial, escrow, and forensic purposes for each significant earthquake. Recent inquiries include how and why maps change with time, how to interpret metadata, and how to obtain the creation and update history of various map layers. Similarly, the collection of ShakeMap scenarios and historical ShakeMaps—either created in earlier versions or rerun as part of the latest version of the ShakeMap Atlas—warrant a full explanation of the inputs, processing, and archiving given their contribution to fragility curve development and loss model calibration. For these reasons, in addition to event-specific ShakeMap metadata and a comprehensive online ShakeMap Manual, we have crafted this practice paper to answer several of the most frequently asked technical questions. We also describe an application programming interface (API) for accessing site-specific shaking metrics and their uncertainties for earthquake forensic purposes in a consistent fashion. In all, we describe the advantages of employing ShakeMaps for these critical purposes as well as describe their limitations and uncertainties, offering an extensive set of instructions and disclaimers that can be referenced by ShakeMap users.

California↗

USGS2021

Selection of random vibration theory procedures for the NGA-East project and ground-motion modeling

Traditional ground-motion models (GMMs) are used to compute pseudo-spectral acceleration (PSA) from future earthquakes and are generally developed by regression of PSA using a physics-based functional form. PSA is a relatively simple metric that correlates well with the response of several engineering systems and is a metric commonly used in engineering evaluations; however, characteristics of the PSA calculation make application of scaling factors dependent on the frequency content of the input motion, complicating the development and adaptability of GMMs. By comparison, Fourier amplitude spectrum (FAS) represents ground-motion amplitudes that are completely independent from the amplitudes at other frequencies, making them an attractive alternative for GMM development. Random vibration theory (RVT) predicts the peak response of motion in the time domain based on the FAS and a duration, and thus can be used to relate FAS to PSA. Using RVT to compute the expected peak response in the time domain for given FAS therefore presents a significant advantage that is gaining traction in the GMM field. This article provides recommended RVT procedures relevant to GMM development, which were developed for the Next Generation Attenuation (NGA)-East project. In addition, an orientation-independent FAS metric—called the effective amplitude spectrum (EAS)—is developed for use in conjunction with RVT to preserve the mean power of the corresponding two horizontal components considered in traditional PSA-based modeling (i.e., RotD50). The EAS uses a standardized smoothing approach to provide a practical representation of the FAS for ground-motion modeling, while minimizing the impact on the four RVT properties ( zeroth moment, m0 ; bandwidth parameter, δ ; frequency of zero crossings, fz ; and frequency of extrema, fe ). Although the recommendations were originally developed for NGA-East, they and the methodology they are based on can be adapted to become portable to other GMM and engineering problems requiring the computation of PSA from FAS.

Earthquake Spectra↗

USGS2021

A near-real-time model for estimating probability of road obstruction due to earthquake-triggered landslides

Coseismic landslides are a major source of transportation disruption in mountainous areas, but few approaches exist for rapidly estimating impacts to road networks. We develop a model that links the U.S. Geological Survey (USGS) near-real-time earthquake-triggered landslide hazard model with Open Street Map (OSM) road network data to rapidly estimate segment-level obstruction risk following major earthquake activity worldwide. To train and validate the model, we process OSM data for 15 historical earthquakes and calculate the average segment-level landslide hazard from the USGS model for each event. We then fit a multivariate adaptive regression spline model for the probability of road obstruction as a function of road segment length and landslide hazard, using a training and validation dataset derived from the intersections of road networks with earthquake-triggered landslide inventories. The resulting probabilistic model is well calibrated across a range of earthquake events, with estimated obstruction probabilities matching the relative frequency of potential road obstructions. The model runs quickly and is capable of producing road segment-level obstruction estimates within minutes to hours of a major earthquake. However, in near-real-time application, the accuracy of the obstruction estimates will be dependent on the quality of the ShakeMap shaking estimates, which often improves with time as more information becomes available after the earthquake. By providing a rapid first-order translation of landslide hazard into potential infrastructure impacts, this model helps provide emergency responders with tangible information on initial areas of concern.

Earthquake Spectra↗

USGS2021

Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (M w ) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

Alaska↗

USGS2021

Seismic wave propagation and basin amplification in the Wasatch Front, Utah

Ground‐motion analysis of more than 3000 records from 59 earthquakes, including records from the March 2020 M w "> M w 5.7 Magna earthquake sequence, was carried out to investigate site response and basin amplification in the Wasatch Front, Utah. We compare ground motions with the Bayless and Abrahamson (2019 ; hereafter, BA18) ground‐motion model (GMM) for Fourier amplitude spectra, which was developed on crustal earthquake records from California and other tectonically active regions. The Wasatch Front records show a significantly different near‐source rate of distance attenuation than the BA18 model, which we attribute to differences in (apparent) geometric attenuation. Near‐source residuals show a period dependence of this effect, with greater attenuation at shorter periods ( ⁠ T < 0.5    s "> T < 0.5 s ) and a correlation between period and the distance over which the discrepancy manifests ( ⁠ ∼ 20 – 50    km "> ∼ 20 – 50 km ⁠ ). We adjusted the recorded ground motions for these regional path effects and solved for station site terms using linear mixed‐effects regressions, with groupings for events and stations. We analyzed basin amplification by comparing the site terms with the basin geometry and basin depths from two seismic‐velocity models for the region. Sites over the deeper parts of the sedimentary basins are amplified by factors of 3–10, relative to sites with thin sedimentary cover, with greater amplification at longer periods ( ⁠ T ≳ 1    s "> T ≳ 1 s ). Average ground‐motion variability increases with period, and long‐period variability exhibits a slight increase at the basin edges. These results indicate regional seismic wave propagation effects requiring further study, and potentially a regionalized GMM, as well as highlight basin amplification complexities that may be incorporated into seismic hazard assessments.

Utah↗

USGS2021

The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US

The U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous U.S. NSHM includes significant changes to the underlying ground motion models (GMMs), most of which are necessary to enable the new multi-period response spectra (MPRS) requirements of seismic design regulations that use hazard results for 22 spectral periods and eight site classes. This article focuses on the GMMs used in the western United States (WUS) and is a companion to a recent article on the GMMs used in the central and eastern United States (CEUS). In the WUS, for crustal and subduction earthquakes, two models used in previous versions of the NSHM are excluded to provide consistency over all considered periods and site classes. To more accurately estimate ground motions at long periods in the vicinity of Los Angeles, San Francisco, Salt Lake City, and Seattle, the 2018 NSHM incorporates deep sedimentary basin depth from local seismic velocity models. The subduction GMMs considered lack basin depth terms and are modified to include an additional scale factor to account for this. This article documents the WUS GMMs used in the 2018 NSHM update and provides detail on the changes to GMM medians, aleatory variability, epistemic uncertainty, and site-effect models. It compares each of these components with those considered in prior NSHMs and discusses their total effect on hazard.

Arizona, California, Idaho, Montana, New Mexico, N↗

USGS2021

The 2018 update of the US National Seismic Hazard Model: Ground motion models in the central and eastern US

The United States Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous US NSHM includes major changes to the underlying ground motion models (GMMs). Most of the changes are motivated by the new multi-period response spectra requirements of seismic design regulations that use hazard results for 22 spectral periods and 8 site classes. In the central and eastern United States (CEUS), the 2018 NSHM incorporates 31 new GMMs for hard-rock site conditions ( V S 30 = 3000 m / s ) "> ( V S 30 = 3000 m / s ) (VS30=3000m/s) , including the Next Generation Attenuation (NGA)-East GMMs. New aleatory variability and site-effect models, both specific to the CEUS, are applied to all median hard-rock GMMs. This article documents the changes to the USGS GMM selection criteria and provides details on the new CEUS GMMs used in the 2018 NSHM update. The median GMMs, their weights, epistemic uncertainty, and aleatory variability are compared with those considered in prior NSHMs. This article further provides implementation details on the CEUS site-effect model, which allows conversion of hard-rock ground motions to other site conditions in the CEUS for the first time in NSHMs. Compared with the 2014 NSHM hard-rock ground motions, the weighted average of median GMMs increases for large magnitude events at middle to large distance range, epistemic uncertainty increases in almost all situations, but aleatory variability is not significantly different. Finally, the total effect on hazard is demonstrated for an assumed earthquake source model in the CEUS, which shows an increased ring of ground motions in the vicinity of the New Madrid seismic zone and decreased ground motions near the East Tennessee seismic zone.

Earthquake Spectra↗

USGS2021

The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed

The 2018 US Geological Survey National Seismic Hazard Model (NSHM) incorporates new data and updated science to improve the underlying earthquake and ground motion forecasts for the conterminous United States. The NSHM considers many new data and component input models: (1) new earthquakes between 2013 and 2017 and updated earthquake magnitudes for some earlier earthquakes; (2) two updated smoothed seismicity models to forecast earthquake rates; (3) two suites of new central and eastern US (CEUS) ground motion models (GMMs) to translate ground shaking for various earthquake sizes and source-to-site distances considered in the model; (4) two CEUS GMMs for aleatory variability; (5) two CEUS site-effect models that modify ground shaking based on alternative shallow site conditions; (6) more advanced western US (WUS) lithologic and structural information to assess basin site effects for selected urban regions; and (7) a more comprehensive range of outputs (22 periods and 8 site classes) than in previous versions of the NSHMs. Each of these new datasets and models produces changes in the probabilistic ground shaking levels that are spatially and statistically analyzed. Recent earthquakes or changes to some older earthquake magnitudes and locations mostly result in probabilistic ground shaking levels that are similar to previous models, but local changes can reach up to +80% and −60% compared to the 2014 model. Newly developed CEUS models for GMMs, aleatory variability, and site effects cause overall changes up to ±64%. The addition of the WUS basin amplifications causes changes of up to +60% at longer periods for sites overlying deep soft soils. Across the conterminous United States, the hazard changes in the model are mainly caused by new GMMs in the CEUS, by sedimentary basin effects for long periods (≥1 s) in the WUS, and by seismicity changes for short (0.2 s) and long (1 s) periods for both areas.

Earthquake Spectra↗

USGS2020

A global hybrid VS30 map with a topographic slope–based default and regional map insets

Time-averaged shear wave velocity over the upper 30 m of the earth’s surface ( V S 30 ) is a key parameter for estimating ground motion amplification as both a predictive and a diagnostic tool for earthquake hazards. The first-order approximation of V S 30 is commonly obtained through a topographic slope–based or terrain proxy due to the widely available nature of digital elevation models. However, better-constrained V S 30 maps have been developed in many regions. Such maps preferentially employ various combinations of V S 30 measurements, higher-resolution elevation models, lithologic, geologic, geomorphic, and other proxies and often utilize refined interpolation schemes. We develop a new hybrid global V S 30 map database that defaults to the global slope-based V S 30 map, but smoothly inserts regional V S 30 maps where available. In addition, we present comparisons of the default slope-based proxy maps against the new hybrid version in terms of V S 30 and amplification ratio maps, and uncertainties in assigned V S 30 values.

Earthquake Spectra↗

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

Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake

We developed an initial inventory of ground failure features from the November 30, 2018, magnitude 7.1 Anchorage earthquake. This inventory of 153 features is from ground-based observations soon after the earthquake (December 5–10) that include the presence or absence of liquefaction, landslides, and individual crack traces of lateral spreads and incipient landslides. This is not a complete inventory and simply shows general trends and examples of types and distribution of ground failures documented. Overflight observations (December 1–6) documented landslide and liquefaction presence or absence within the Chugach Mountains and along Cook Inlet in regions inaccessible to vehicles, which greatly expanded the geographic scope of this reconnaissance. Field-mapped ground-failure observations have been augmented with a set of 565 georeferenced and annotated images from both field and overflight reconnaissance cataloging additional ground-failure presence or absence from the Anchorage earthquake. Tidal erosion, fresh snowfall, limited daylight, and adverse flying conditions contributed significantly to the uncertainty and incompleteness of ground-failure observations during this reconnaissance. Notably, substantial liquefaction features at the mouths of the Little Susitna River (December 1) and Ingram Creek (December 5) were absent during subsequent overlapping missions (December 6 and 9, respectively) because of tidal action. A large rockfall observed on December 1 on Rainbow Peak was not observed during a subsequent December 5 overflight because of snow cover, which suggests that the general lack of observations of landsliding within the Chugach Mountains is uncertain.

Alaska↗

USGS2020

A machine learning approach to developing ground motion models from simulated ground motions

We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic-based GMM with empirically based GMMs derived from the globally based Next Generation Attenuation West2 data set, finding near-zero median residuals and similar amplitude and trends (with period) of total variability. Additionally, we find that the artificial neural network GMM has similar bias and variability to empirical GMMs from records of the recent Ridgecrest event, which neither GMM has included in its formulation. As simulations continue to better model broadband ground motions, machine learning provides a way to utilize the vast amount of synthetically generated data and guide future parameterization of GMMs.

California↗

USGS2020

Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence

The 2019 Ridgecrest, California, earthquake sequence, including an Mw 6.4 event on 4 July and an Mw 7.1 approximately 34 hr later, was recorded by 15 instruments within 55 km nearest‐fault distance. To characterize and explore near‐field ground motions from the Mw 6.4 foreshock and Mw 7.1 mainshock, we augment these records with available macroseismic information, including conventional intensities and displaced rocks. We conclude that near‐field shaking intensities were generally below modified Mercalli intensity 9, with concentrations of locally high values toward the northern and southern termini of the mainshock rupture. We further show that, relative to near‐field ground motions at hard‐rock sites, instrumental ground motions at alluvial near‐field sites for both the Mw 6.4 foreshock and Mw 7.1 mainshock were depleted in energy at frequencies higher than 2–3 Hz, as expected from ground‐motion models. Both the macroseismic and instrumental observations suggest that sediments in the Indian Wells Valley experienced a pervasively nonlinear response, which helps explain why shaking intensities and damage in the closest population center, Ridgecrest, were relatively modest given its proximity to the earthquakes.

California↗

USGS2020

The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics

Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground‐motion records from multiple data centers and processed these records using newly developed ground‐motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground‐motion metrics. In addition, we compute station and waveform metrics such as the time‐averaged shear‐wave velocity to 30 m depth ( ⁠ V S 30 "> V S 30 ⁠ ), finite‐rupture distances, and spectral accelerations. This data set includes 22,708 records from 133 events from 4 July 2019 (UTC) to 18 October 2019 with a magnitude range from 3.6 to 7.1. We expect that the rapid collection and dissemination of this information will facilitate detailed studies of these ground motions. In this article, we describe the data selection, processing steps, and how to access the data.

California↗