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USGS2020

The community code verification exercise for simulating sequences of earthquakes and aseismic slip (SEAS)

Numerical simulations of sequences of earthquakes and aseismic slip (SEAS) have made great progress over past decades to address important questions in earthquake physics. However, significant challenges in SEAS modeling remain in resolving multiscale interactions between earthquake nucleation, dynamic rupture, and aseismic slip, and understanding physical factors controlling observables such as seismicity and ground deformation. The increasing complexity of SEAS modeling calls for extensive efforts to verify codes and advance these simulations with rigor, reproducibility, and broadened impact. In 2018, we initiated a community code-verification exercise for SEAS simulations, supported by the Southern California Earthquake Center. Here, we report the findings from our first two benchmark problems (BP1 and BP2), designed to verify different computational methods in solving a mathematically well-defined, basic faulting problem. We consider a 2D antiplane problem, with a 1D planar vertical strike-slip fault obeying rate-and-state friction, embedded in a 2D homogeneous, linear elastic halfspace. Sequences of quasi-dynamic earthquakes with periodic occurrences (BP1) or bimodal sizes (BP2) and their interactions with aseismic slip are simulated. The comparison of results from 11 groups using different numerical methods show excellent agreements in long-term and coseismic fault behavior. In BP1, we found that truncated domain boundaries influence interseismic stressing, earthquake recurrence, and coseismic rupture, and that model agreement is only achieved with sufficiently large domain sizes. In BP2, we found that complexity of fault behavior depends on how well physical length scales related to spontaneous nucleation and rupture propagation are resolved. Poor numerical resolution can result in artificial complexity, impacting simulation results that are of potential interest for characterizing seismic hazard such as earthquake size distributions, moment release, and recurrence times. These results inform the development of more advanced SEAS models, contributing to our further understanding of earthquake system dynamics.

Seismological Research Letters↗
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

Caltech/USGS Southern California Seismic Network (SCSN) and Southern California Earthquake Data Center (SCEDC): Data availability for the 2019 Ridgecrest sequence

The 2019 M6.4 and M7.1 Ridgecrest earthquake sequence occurred in the eastern California shear zone (ECSZ). The mainshock ruptured the Little Lake fault zone and aftershocks extended from the Garlock fault in the south, to the southern end of the 1872 M7.5 Owens Valley earthquake rupture in the north. We present data from the Southern California Seismic Network (SCSN) and partner seismic networks recorded by the SCSN in the region. These time series data and related products such as the SCSN earthquake picks and catalogs, available from the Southern California Earthquake Data Center (SCEDC), provide the most comprehensive seismic datasets for the 2019 Ridgecrest earthquake sequence.

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↗
USGS2020

Operational earthquake forecasting during the 2019 Ridgecrest, California, earthquake sequence with the UCERF3-ETAS model

The first Uniform California Earthquake Rupture Forecast, Version 3–epidemic‐type aftershock sequence (UCERF3‐ETAS) aftershock simulations were running on a high‐performance computing cluster within 33 min of the 4 July 2019 M 6.4 Searles Valley earthquake. UCERF3‐ETAS, an extension of the third Uniform California Earthquake Rupture Forecast (UCERF3), is the first comprehensive, fault‐based, epidemic‐type aftershock sequence (ETAS) model. It produces ensembles of synthetic aftershock sequences both on and off explicitly modeled UCERF3 faults to answer a key question repeatedly asked during the Ridgecrest sequence: What are the chances that the earthquake that just occurred will turn out to be the foreshock of an even bigger event?

California↗
USGS2020

The U.S. Geological Survey’s Rapid Seismic Array Deployment for the 2019 Ridgecrest Earthquake Sequence

Rapid seismic deployments following large earthquakes capture ephemeral near‐field recordings of aftershocks and ambient noise that can provide valuable data for seismological studies. The U.S. Geological Survey installed 19 temporary seismic stations following the 4 July 2019 M w 6.4 and 6 July 2019 (UTC) M w 7.1 earthquakes near the city of Ridgecrest, California. The stations record the aftershock sequence beginning two days after the mainshock and are expected to remain in the field through approximately January 2020. The deployment augments the permanent seismic network in the area to improve azimuthal coverage and provide additional near‐field observations. This article summarizes the motivation and goals of the deployment; details of station installation, instrumentation, and configurations; and initial data quality and observations from the network. We expect these data to be useful for a range of studies including detailing near‐field variability in strong ground motions, determining stress drops and rupture directivity of small events, imaging the fault zone, documenting the evolution of crustal properties within and outside of the fault zone, and others.

California↗
USGS2020

A high-resolution seismic catalog for the initial 2019 Ridgecrest Earthquake sequence: Foreshocks, aftershocks, and faulting complexity

I use template matching and precise relative relocation techniques to develop a high-resolution earthquake catalog for the initial portion of the 2019 Ridgecrest earthquake sequence, from July 4-16, encompassing the foreshock sequence and the first 10+ days of aftershocks following the M w 7.1 mainshock. Using 13,525 routinely cataloged events as waveform templates, I detect and precisely locate a total of 34,091 events. Precisely located earthquakes reveal numerous cross-cutting fault structures with dominantly perpendicular SW- and NW-strikes. Foreshocks of the M w 6.4 event appear to align on a NW-striking fault. Aftershocks of the M w 6.4 event suggest that it further ruptured this NW-striking fault, as well as the SW-striking fault where surface rupture was observed. Finally, aftershocks of the M w 7.1 show a highly complex distribution, illuminating a primary NW-striking fault zone consistent with surface rupture, but also numerous cross-cutting SW-striking faults. Aftershock relocations suggest that the M w 7.1 event ruptured adjacent to the previous NW-striking rupture of the M w 6.4, perhaps activating a subparallel structure southwest of the earlier rupture. Both the NW and SE rupture termini of the M w 7.1 rupture exhibited multiple-fault branching, with particularly high rates of aftershocks and multiple fault orientations in the dilatational quadrant NE of the NW rupture terminus.

California↗
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.

California↗
USGS2020

Airborne lidar and electro-optical imagery along surface ruptures of the 2019 Ridgecrest earthquake sequence, Southern California

Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the M 6.4 foreshock, occurred on July 4 on a ~17 km long, northeast-southwest oriented, left-lateral zone of faulting. Following the M 7.1 mainshock on July 5 (local time), extensive northwest-southeast-oriented, right-lateral faulting was then also mapped along a ~50 km long zone of faults, including sub-parallel splays in several areas. The largest slip was observed in the epicentral area, and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution lidar and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter, or ppsm) and high resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping (NCALM) acquired the airborne imagery with a Titan multispectral lidar system and DiMAC aerial digital camera, and USGS acquired GPS ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake Ranges.

Calilfornia↗
USGS2019

Preface to the focus section on the 30 November 2018 Mw 7.1 Anchorage, Alaska earthquake

On 30 November 2018, a magnitude ( ⁠ M w "> M w ⁠ ) 7.1 intraslab earthquake occurred under the most densely populated area of Alaska, ∼ 12    km "> ∼ 12 km north of downtown Anchorage. It generated the strongest ground shaking in the greater Anchorage area since the 1964 M w "> M w 9.2 Great Alaska earthquake. Although the 30 November 2018 earthquake did not cause deaths or serious injuries, its economical and psychological impacts on the local population were profound. It caused widespread power outages, structural and nonstructural damage to public and private buildings, and damage to roadways and railways, and resulted in closures of several schools and multiple businesses.

Alaska↗
USGS2020

Forecasting for a fractured land: A case study of the communication and use of aftershock forecasts from the Mw 7.8 2016 Kaikōura earthquake in Aotearoa New Zealand

Operational earthquake forecasts (OEFs) are represented as time‐dependent probabilities of future earthquake hazard and risk. These probabilities can be presented in a variety of formats, including tables, maps, and text‐based scenarios. In countries such as Aotearoa New Zealand, the U.S., and Japan, OEFs have been released by scientific organizations to agencies and the public, with the intent of providing information about future earthquake hazard and risk, so that people can use this information to inform their decisions and activities. Despite questions being raised about the utility of OEF for decision‐making, past earthquake events have shown that agencies and the public have indeed made use of such forecasts. Responses have included making decisions about safe access into buildings, cordoning, demolition safety, timing of infrastructure repair and rebuild, insurance, postearthquake building standards, postevent land‐use planning, and public communication about aftershocks. To add to this body of knowledge, we undertook a survey to investigate how agencies and GNS Science staff used OEFs that were communicated following the M w 7.8 2016 Kaikōura earthquake in Aotearoa New Zealand. We found that agencies utilized OEFs in many of the ways listed previously, and we document individual employee’s actions taken in their home‐life context. Challenges remain, however, regarding the interpretation of probabilistic information and applying this to practical decision‐making. We suggest that science agencies cannot expect nontechnical users to understand and utilize forecasts without additional support. This might include developing a diversity of audience‐relevant OEF information for communication purposes, alongside advice on how such information could be utilized.

northern part of South Island↗
USGS2020

When source and path components trade off in ground-motion prediction equations

Current research on ground‐motion models (also known as ground‐motion prediction equations [GMPEs]) and their uncertainties focus on the separate contributions of source, path, and site to both median values and their variability. Implicit here is the assumption that the event term, path term, and site term reflect only properties of the source, path, and site, respectively. Events with larger stress drop generate more high‐frequency energy, and thus more ground motion. Therefore, the correlation of high‐frequency (i.e., peak ground acceleration [PGA] or peak ground velocity [PGV]) event terms in GMPEs with stress drop is taken to be genuine. However, PGA and PGV ground‐motion observations of the 2014 M "> M 6.0 South Napa, California, earthquake clearly violate these assumptions. For this earthquake, high‐frequency ground‐motion residuals of recorded ground motion with respect to Next Generation Attenuation‐West2 Project (NGA‐West2) ground‐motion models show a dependence on distance, biasing the calculation of the event term by incorrectly mapping a regional attenuation effect into it. We examine the trade‐off between source and path effects for the South Napa earthquake and a well‐recorded California subset of the NGA‐West2 data. We fit near‐source (i.e., within 20 or 50 km) event terms and remaining differential geometrical spreading and anelastic attenuation terms in comparison to a simultaneous inversion for the source and path terms. This South Napa instance highlights one situation for which the high‐frequency event term can be interpreted as relative stress drop only when the distance dependence of the ground motions does not bias the residuals.

California↗
USGS2020

The potential of using dynamic strains in earthquake early warning applications

We investigate the potential of using borehole strainmeter data from the Network of the Americas (NOTA) and the U.S. Geological Survey networks to estimate earthquake moment magnitudes for earthquake early warning (EEW) applications. We derive an empirical equation relating peak dynamic strain, earthquake moment magnitude, and hypocentral distance, and investigate the effects of different types of instrument calibration on model misfit. We find that raw (uncalibrated) strains fit the model as accurately as calibrated strains. We test the model by estimating moment magnitudes of the largest two earthquakes in the July 2019 Ridgecrest earthquake sequence—the M 6.4 foreshock and the M 7.1 mainshock—using two strainmeters located within ∼50 km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within ∼0.1–0.4 magnitude units of the catalog moment magnitude. We then compare the temporal evolution of our strain‐derived magnitudes for the largest two Ridgecrest events to the real‐time performance of the ShakeAlert EEW System (SAS). The final magnitudes from NOTA borehole strainmeters are close to SAS real‐time estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.

British Columbia, California, Oregon, Washington↗
USGS2020

Rapid geodetic observations of spatiotemporally varying postseismic deformation following the Ridgecrest earthquake sequence: The U.S. Geological Survey response

The U.S. Geological Survey’s geodetic response to the 4–5 July 2019 (Pacific time) Ridgecrest earthquake sequence comprised primarily the installation and/or reoccupation of Global Navigation Satellite System (GNSS) monumentation. Our response focused primarily on the United States’ Navy’s China Lake Naval Air Weapons Station base (NAWSCL). This focus was because much of the surface rupture occurred on the NAWSCL and because of NAWSCL access restrictions only permitting Federal and State of California personnel. In total, we measured or are still measuring at 24 sites, 14 of which were on the NAWSCL and, as of this writing, operational. The majority of sites were set up as continuous stations logging at either 1 sample per second or 1 sample per 15 s. Two stations were recording a 200 m cross‐rupture aperture starting ∼10 hr after the M 6.4 event, and they recorded the coseismic displacements of the M 7.1. Approximately, 1 hr after the M 7.1 event, two new stations were recording a ∼200 m cross‐rupture aperture of the surface rupture. In the days following, we established the rest of the stations ranging to a distance of ∼15 km from the M 7.1 principal rupture trace. The lack of differential displacement across the M 6.4 rupture during the M 7.1 event suggests that it did not reactivate the M 6.4 plane. The lack of differential cross‐fault displacement for both events suggests that rapid shallow afterslip did not occur at those two locations. The postseismic time series from these stations shows centimeters of horizontal displacement over periods of a few months. They record a mixture of fault‐parallel and fault‐normal displacements that, in conjunction with analysis of more spatially complete Interferometric Synthetic Aperture Radar displacement fields, suggest that both poroelastic and afterslip phenomena occur along the M 6.4 and 7.1 rupture planes. Using preliminary data from these and other regional stations, we also explore the Ridgecrest sequence’s effect on regional GNSS time series and the differentiation of long‐term postseismic motions and secular deformation rates. We find that redefining a common‐mode noise filter using different GNSS stations that are assumed to be unaffected by the earthquakes results in small but systematic differences in the regional velocity field estimate.

Seismological Research Letters↗
USGS2020

Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004

An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four‐station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real‐time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ~10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.

Soufrière Hills Volcano↗
USGS2020

California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps

Historical seismic intensity data are useful for myriad reasons, including assessment of the performance of Probabilistic Seismic Hazard Assessment (PSHA) models and corresponding hazard maps by comparing their predictions to a dataset of historically observed intensities in the region. To assess PSHA models for California, a long and consistently interpreted intensity record is necessary. For this purpose, the California Historical Intensity Mapping Project (CHIMP) has compiled a dataset that combines and reinterprets intensity information that has been stored in disparate and sometimes hard-to-access locations. The CHIMP dataset also includes new observations of intensity from archival research and oral history collection. Version 1 of the dataset includes 46,502 intensity observations for 62 earthquakes with estimated magnitudes ranging from 4.7 to 7.9. The 162 years of shaking data show observed shaking lower than expected from seismic hazard models. This discrepancy is reduced, but persists, if historical intensity data for the largest earthquakes are smoothed to reduce the effects of spatial under-sampling. Possible reasons for this discrepancy include other limitations of the CHIMP dataset, the hazard models, and the possibility that California seismicity throughout the historical period has been lower than the long-term average. Some of these issues may also explain similar discrepancies observed for Italy and Japan.

California↗
USGS2020

Installation and performance of the Albuquerque Seismological Laboratory small-aperture posthole array

The Global Seismographic Network (GSN) has been used extensively by seismologists to characterize large earthquakes and image deep earth structure. Although the network’s original design goals have been met, the seismological community has suggested that the incorporation of small-aperture seismic arrays at select sites may improve performance of the network and enable new observations. As a pilot study for this concept, we have created a 500 m aperture, nine-element broadband seismic array around the GSN station ANMO (Albuquerque, New Mexico) at the U.S. Geological Survey Albuquerque Seismological Laboratory (ASL). The array was formed by supplementing the secondary borehole seismometer (90 m depth) at ANMO with eight additional 2.6 m posthole sites. Each station’s seismometer was oriented using a fiber optic gyroscope to within 2.0° of north. Data quality, particularly on the vertical components, is excellent with median power levels closely tracking the secondary sensor at ANMO at frequencies lower than 1 Hz. Horizontal component data are more variable at low frequencies (< 0:02 Hz), with the type of installation and local geography appearing to strongly influence the amount of tilt-induced noise. Throughout the article, we pose several fundamental questions related to the variability and precision of seismic wavefield measurements that we seek to address with data from this array. In addition, we calculate the array response and show a few examples of using the array to obtain back azimuths of a local event and a continuous narrowband noise source. The apparent velocity of the event across the array is then used to infer the local P-wave velocity at the ASL. Near real-time data collected from the array along with collocated meteorological, magnetic, and infrasound data are freely available in near-real time from the Incorporated Research Institutions for Seismology Data Management Center.

New Mexico↗
USGS2020

Very‐long‐period (VLP) seismic artifacts during the 2018 caldera collapse at Kīlauea, Hawaii

Throughout the 2018 eruption of Kīlauea volcano (Hawai‘i), episodic collapses of a portion of the volcano’s summit caldera produced repeated M w "> M w 4.9–5.3 earthquakes. Each of these 62 events was characterized by a very‐long‐period (VLP) seismic signal ( ⁠ &gt; 40 &#x2009;&#x2009; s "> > 40 s ⁠ ). Although collapses in the later stage of the eruption produced earthquakes with significant amplitude clipping on near‐summit broadband seismometers, the first 12 were accurately recorded. For these initial collapse events, we compare average VLP seismograms at six near‐summit locations to synthetic seismograms derived from displacements at collocated Global Positioning System stations. We show that the VLP seismic signal was generated by a radially outward and upward ramp function in displacement. We propose that at local distances the period of the VLP seismic signal is solely dependent on the duration of this ramp function and the instrument transfer function, that is, the seismic VLP is an artifact of the bandlimited instrument response and not representative of real ground motion. The displacement ramp function imposes a sinc‐function velocity amplitude spectrum that cannot be fully recovered through standard seismic instrument deconvolution. Any near‐summit VLP signals in instrument‐response‐corrected velocity or displacement seismograms from these collapse events are subject to severe band limitation. Similarly, the seismic amplitude response is not flat through the low‐frequency corner, for example, instrument‐response‐uncorrected seismograms scaled by instrument sensitivity are equally prone to band limitation. This observation is crucial when attempting to clarify the different contributions to the VLP source signature. Not accounting for this effect could lead to misunderstanding of the magmatic processes involved.

Hawaii↗