SEARCH · Geology Reports

Results for “Algorithms”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59Linked to original sources

USGS2013

Predicting what helminth parasites a fish species should have using Parasite Co-occurrence Modeler (PaCo)

Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species–genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest .

Journal of Parasitology↗

USGS2005

An efficient strategy to estimate intensity and prevalence: Sampling metacercariae in fishes

Accurate estimates of population-level parameters of parasites, such as prevalence and mean intensity, require large sample sizes. The processing of such samples becomes an overwhelming task when parasites are abundant, as with trematode metacercariae in fishes. In the present study, a subsampling method reduced processing time while maintaining an accurate estimation of metacercariae prevalence and intensity across 3 trematode species and 2 fish species. By double sampling, we generated regression models to predict total intensity from a combination of subsamples. The key to this approach lies in choosing the best strategy from a large number of potential subsampling routines. We selected the most efficient routine by weighing the costs and benefits of each. This approach, however, could not provide an estimate of parasite abundance when no parasites occurred in the initial subsample. To estimate prevalence accurately, our subsampling algorithm prescribed an additional sampling sequence using a new, optimal regression model. In addition, we optimized the technique to measure three parasite species infecting a single host simultaneously. This efficient subsampling procedure decreased the overall processing time per host by up to 91% while obtaining accurate ( R 2 > 0.8) estimates for both prevalence and intensity.

Journal of Parasitology↗

USGS2001

Data files from “CWB Free-Field Strong-Motion Data from the 21 September Chi-Chi, Taiwan, Earthquake”

The Central Weather Bureau (CWB) of Taiwan completed a deployment of 1200 modern digital strong-motion instruments in 1996 at free-field sites and in buildings and bridges. Consequently, a very extensive set of strong-motion records were obtained for the M W = 7.6 Chi-Chi earthquake in 1999, including over 60 near-field records within 20 km of the fault ruptures. For documentation purposes, we included all relevant data files on the attached CD-ROM from our article, “CWB Free-Field Strong-Motion Data from the 21 September Chi-Chi, Taiwan, Earthquake” in this issue. We presented the data in four different ways to make the data more user friendly: (1) the original recorded data by accelerograph type; (2) the processed data in ASCII text format; (3) the processed data in SUDS format; and (4) the processed data in SAC format. (1)-(3) are exactly the same as in Lee et al. ( 2001 ), except the files are zipped (or compressed). (4) is provided for users using the SAC software on a Unix computer, and it also contains waveform plots of the data in PDF files for ease of viewing. The attached CD contains the full report of Lee et al. ( 2001 ) in 25 PDF files for viewing or printing. This full report described the data set and the data processing performed in details. It also contains plots of all processed data with an index map showing the fault, mainshock location, and the station location. We also provided a readme.txt on the CDROM to augment this Short Note with further explanations. We classify the recorded data files into four quality groups. Since the recorded strong-motion data are based on a triggering algorithm, a main concern is the amount of pre-event data and whether or not the record is long enough to cover the entire duration of the ground shaking at that station. In addition, we are concerned whether or not the record has defects (e.g., spikes, or a component was not recorded), and whether or not the record has absolute timing. In general, QA-class records are the best and can be used for any studies. QB-class records are the next best, except most of them do not have absolute timing. QC-class records cover the principal strong motions but may not have adequate pre-event data and/or post strong-motion data. QD-class records have some defects; they are included here for completeness and should not be used for most studies. Since most A800 accelerographs have a colocated A900 or A900A accelerograph, the user should use the A900 or A900A data files whenever possible. There are a readme file and six subdirectories containing the data files described in our article on the attached CDROM, under the directory of \LeeWHK: (1) readme.txt: an ASCII text file of this Short Note and additional explanations. (2) DOCfiles (subdirectory): documentary files in PDF format for viewing, and some files in ASCII text format for use in data processing. User should view or print the Reportxx.pdf files which describe the Chi-Chi strong-motion data set in details (xx is a number from 01 through 25). (3) Software (subdirectory): containing computer programs from the accelerograph manufacturers for viewing and converting the original recorded data; they are grouped by manufacturer. (4) ORGfiles (subdirectory): the original recorded data files grouped by accelerograph types; these files are for archival purposes only. (5) SUDfiles (subdirectory): the processed strong-motion data files in PC-SUDS format, and are grouped by quality class. (6) ASCfiles (subdirectory): the processed strong-motion data files are in ASCII text format and are grouped by quality class. (7) SACfiles (subdirectory): the processed strong-motion data files are in SAC format and are grouped by quality class. Within each quality class, the data are given by station. Each station has two files—xxx.pdf for viewing the three-component strong-motion data, and xxx_SAC.zip, a WINZIP file containing three SAC data files corresponding to the three components of the recorded acceleration. We use “xxx” here to denote the station name.

Bulletin of the Seismological Society of America↗

USGS2003

Aftershocks and triggered events of the Great 1906 California earthquake

The San Andreas fault is the longest fault in California and one of the longest strike-slip faults in the world, yet little is known about the aftershocks following the most recent great event on the San Andreas, the Mw 7.8 San Francisco earthquake on 18 April 1906. We conducted a study to locate and to estimate magnitudes for the largest aftershocks and triggered events of this earthquake. We examined existing catalogs and historical documents for the period April 1906 to December 1907, compiling data on the first 20 months of the aftershock sequence. We grouped felt reports temporally and assigned modified Mercalli intensities for the larger events based on the descriptions judged to be the most reliable. For onshore and near-shore events, a grid-search algorithm (derived from empirical analysis of modern earthquakes) was used to find the epicentral location and magnitude most consistent with the assigned intensities. For one event identified as far offshore, the event's intensity distribution was compared with those of modern events, in order to contrain the event's location and magnitude. The largest aftershock within the study period, an M ???6.7 event, occurred ???100 km west of Eureka on 23 April 1906. Although not within our study period, another M ???6.7 aftershock occurred near Cape Mendocino on 28 October 1909. Other significant aftershocks included an M ???5.6 event near San Juan Bautista on 17 May 1906 and an M ???6.3 event near Shelter Cove on 11 August 1907. An M ???4.9 aftershock occurred on the creeping segment of the San Andreas fault (southeast of the mainshock rupture) on 6 July 1906. The 1906 San Francisco earthquake also triggered events in southern California (including separate events in or near the Imperial Valley, the Pomona Valley, and Santa Monica Bay), in western Nevada, in southern central Oregon, and in western Arizona, all within 2 days of the mainshock. Of these trigerred events, the largest were an M ???6.1 earthquake near Brawley and an M ???5.0 event under or near Santa Monica Bay, 11.3 and 31.3 hr after the San Francisco mainshock, respectively. The western Arizona event is inferred to have been triggered dynamically. In general, the largest aftershocks occurred at the ends of the 1906 rupture or away from the rupture entirely; very few significant aftershocks occurred along the mainshock rupture itself. The total number of large aftershocks was less than predicted by a generic model based on typical California mainshock-aftershock statistics, and the 1906 sequence appears to have decayed more slowly than average California sequences. Similarities can be drawn between the 1906 aftershock sequence and that of the 1857 (Mw 7.9) San Andreas fault earthquake.

Bulletin of the Seismological Society of America↗

USGS2007

Stability and uncertainty of finite-fault slip inversions: Application to the 2004 Parkfield, California, earthquake

The 2004 Parkfield, California, earthquake is used to investigate stability and uncertainty aspects of the finite-fault slip inversion problem with different a priori model assumptions. We utilize records from 54 strong ground motion stations and 13 continuous, 1-Hz sampled, geodetic instruments. Two inversion procedures are compared: a linear least-squares subfault-based methodology and a nonlinear global search algorithm. These two methods encompass a wide range of the different approaches that have been used to solve the finite-fault slip inversion problem. For the Parkfield earthquake and the inversion of velocity or displacement waveforms, near-surface related site response (top 100 m, frequencies above 1 Hz) is shown to not significantly affect the solution. Results are also insensitive to selection of slip rate functions with similar duration and to subfault size if proper stabilizing constraints are used. The linear and nonlinear formulations yield consistent results when the same limitations in model parameters are in place and the same inversion norm is used. However, the solution is sensitive to the choice of inversion norm, the bounds on model parameters, such as rake and rupture velocity, and the size of the model fault plane. The geodetic data set for Parkfield gives a slip distribution different from that of the strong-motion data, which may be due to the spatial limitation of the geodetic stations and the bandlimited nature of the strong-motion data. Cross validation and the bootstrap method are used to set limits on the upper bound for rupture velocity and to derive mean slip models and standard deviations in model parameters. This analysis shows that slip on the northwestern half of the Parkfield rupture plane from the inversion of strong-motion data is model dependent and has a greater uncertainty than slip near the hypocenter.

California↗

USGS2012

Estimating pole/zero errors in GSN-IRIS/USGS network calibration metadata

Mapping the digital record of a seismograph into true ground motion requires the correction of the data by some description of the instrument's response. For the Global Seismographic Network (Butler et al., 2004), as well as many other networks, this instrument response is represented as a Laplace domain pole–zero model and published in the Standard for the Exchange of Earthquake Data (SEED) format. This Laplace representation assumes that the seismometer behaves as a linear system, with any abrupt changes described adequately via multiple time-invariant epochs. The SEED format allows for published instrument response errors as well, but these typically have not been estimated or provided to users. We present an iterative three-step method to estimate the instrument response parameters (poles and zeros) and their associated errors using random calibration signals. First, we solve a coarse nonlinear inverse problem using a least-squares grid search to yield a first approximation to the solution. This approach reduces the likelihood of poorly estimated parameters (a local-minimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a nonlinear parameter estimation problem to obtain the least-squares best-fit Laplace pole–zero–gain model. Third, by applying the central limit theorem, we estimate the errors in this pole–zero model by solving the inverse problem at each frequency in a two-thirds octave band centered at each best-fit pole–zero frequency. This procedure yields error estimates of the 99% confidence interval. We demonstrate the method by applying it to a number of recent Incorporated Research Institutions in Seismology/United States Geological Survey (IRIS/USGS) network calibrations (network code IU).

Bulletin of the Seismological Society of America↗

USGS2013

Geometry and earthquake potential of the shoreline fault, central California

The Shoreline fault is a vertical strike‐slip fault running along the coastline near San Luis Obispo, California. Much is unknown about the Shoreline fault, including its slip rate and the details of its geometry. Here, I study the geometry of the Shoreline fault at seismogenic depth, as well as the adjacent section of the offshore Hosgri fault, using seismicity relocations and earthquake focal mechanisms. The Optimal Anisotropic Dynamic Clustering (OADC) algorithm (Ouillon et al., 2008) is used to objectively identify the simplest planar fault geometry that fits all of the earthquakes to within their location uncertainty. The OADC results show that the Shoreline fault is a single continuous structure that connects to the Hosgri fault. Discontinuities smaller than about 1 km may be undetected, but would be too small to be barriers to earthquake rupture. The Hosgri fault dips steeply to the east, while the Shoreline fault is essentially vertical, so the Hosgri fault dips towards and under the Shoreline fault as the two faults approach their intersection. The focal mechanisms generally agree with pure right‐lateral strike‐slip on the OADC planes, but suggest a non‐planar Hosgri fault or another structure underlying the northern Shoreline fault. The Shoreline fault most likely transfers strike‐slip motion between the Hosgri fault and other faults of the Pacific–North America plate boundary system to the east. A hypothetical earthquake rupturing the entire known length of the Shoreline fault would have a moment magnitude of 6.4–6.8. A hypothetical earthquake rupturing the Shoreline fault and the section of the Hosgri fault north of the Hosgri–Shoreline junction would have a moment magnitude of 7.2–7.5.

California↗

USGS2014

The UCERF3 grand inversion: Solving for the long‐term rate of ruptures in a fault system

We present implementation details, testing, and results from a new inversion‐based methodology, known colloquially as the “grand inversion,” developed for the Uniform California Earthquake Rupture Forecast (UCERF3). We employ a parallel simulated annealing algorithm to solve for the long‐term rate of all ruptures that extend through the seismogenic thickness on major mapped faults in California while simultaneously satisfying available slip‐rate, paleoseismic event‐rate, and magnitude‐distribution constraints. The inversion methodology enables the relaxation of fault segmentation and allows for the incorporation of multifault ruptures, which are needed to remove magnitude‐distribution misfits that were present in the previous model, UCERF2. The grand inversion is more objective than past methodologies, as it eliminates the need to prescriptively assign rupture rates. It also provides a means to easily update the model as new data become available. In addition to UCERF3 model results, we present verification of the grand inversion, including sensitivity tests, tuning of equation set weights, convergence metrics, and a synthetic test. These tests demonstrate that while individual rupture rates are poorly resolved by the data, integrated quantities such as magnitude–frequency distributions and, most importantly, hazard metrics, are much more robust.

Bulletin of the Seismological Society of America↗

USGS2016

Rapid estimation of earthquake magnitude from the arrival time of the peak high-frequency amplitude

We propose a simple approach to measure earthquake magnitude M using the time difference ( T op ) between the body‐wave onset and the arrival time of the peak high‐frequency amplitude in an accelerogram. Measured in this manner, we find that M w is proportional to 2log T op for earthquakes 5≤ M w ≤7, which is the theoretical proportionality if T op is proportional to source dimension and stress drop is scale invariant. Using high‐frequency (>2  Hz) data, the root mean square (rms) residual between M w and M T op ( M estimated from T op ) is approximately 0.5 magnitude units. The rms residuals of the high‐frequency data in passbands between 2 and 16 Hz are uniformly smaller than those obtained from the lower‐frequency data. T op depends weakly on epicentral distance, and this dependence can be ignored for distances <200  km. Retrospective application of this algorithm to the 2011 Tohoku earthquake produces a final magnitude estimate of M 9.0 at 120 s after the origin time. We conclude that T op of high‐frequency (>2  Hz) accelerograms has value in the context of earthquake early warning for extremely large events.

Bulletin of the Seismological Society of America↗

USGS2016

An automatic P‐Phase arrival‐time picker

Presented is a new approach for picking P ‐phase arrival time in single‐component acceleration or broadband velocity records without requiring detection interval or threshold settings. The algorithm P PHASE P ICKER transforms the signal into a response domain of a single‐degree‐of‐freedom (SDOF) oscillator with viscous damping and then tracks the rate of change of dissipated damping energy to pick P ‐wave phases. The SDOF oscillator has a short natural period and a correspondingly high resonant frequency, which is higher than most frequencies in a seismic wave. It also has a high damping ratio (60% of critical). At this damping level, the frequency response approaches the Butterworth maximally flat magnitude filter, and phase angles are preserved. The relative input energy imparted to the oscillator by the input signal is converted to elastic strain energy and then dissipated by the damping element as damping energy. The damping energy yields a smooth envelope over time; it is zero in the beginning of the signal, zero or near zero before the P ‐phase arrival, and builds up rapidly with the P wave. Because the damping energy function changes considerably at the onset of the P wave, it is used as a metric to track and pick the P ‐phase arrival time. The P PHASE P ICKER detects P ‐phase onset using the histogram method. Its performance is compared with picking techniques using short‐term‐average to long‐term‐average ratio, and a picking method that finds the first P ‐phase arrival time using the Akaike information criterion. A large set of records with various intensities and signal‐to‐noise ratios is used for testing the P PHASE P ICKER , and it is demonstrated that P PHASE P ICKER is able to more accurately pick the onset of genuine signals against the background noise and to correctly distinguish between whether the first arrival is a P wave (emergent or impulsive) or whether the signal is from a faulty sensor.

Bulletin of the Seismological Society of America↗

USGS2016

Toppling analysis of the Echo Cliffs precariously balanced rock

Toppling analysis of a precariously balanced rock (PBR) can provide insight into the nature of ground motion that has not occurred at that location in the past and, by extension, can constrain peak ground motions for use in engineering design. Earlier approaches have targeted 2D models of the rock or modeled the rock–pedestal contact using spring‐damper assemblies that require recalibration for each rock. Here, a method to model PBRs in 3D is presented through a case study of the Echo Cliffs PBR. The 3D model is created from a point cloud of the rock, the pedestal, and their interface, obtained using terrestrial laser scanning. The dynamic response of the model under earthquake excitation is simulated using a rigid‐body dynamics algorithm. The veracity of this approach is demonstrated through comparisons against data from shake‐table experiments. Fragility maps for toppling probability of the Echo Cliffs PBR as a function of various ground‐motion parameters, rock–pedestal interface friction coefficient, and excitation direction are presented. These fragility maps indicate that the toppling probability of this rock is low (less than 0.2) for peak ground acceleration (PGA) and peak ground velocity (PGV) lower than 3 m/s 2 and 0.75 m/s, respectively, suggesting that the ground‐motion intensities at this location from earthquakes on nearby faults have most probably not exceeded the above‐mentioned PGA and PGV during the age of the PBR. Additionally, the fragility maps generated from this methodology can also be directly coupled with existing probabilistic frameworks to obtain direct constraints on unexceeded ground motion at a PBR’s location.

California↗

USGS2017

Combining multiple earthquake models in real time for earthquake early warning

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

Bulletin of the Seismological Society of America↗

USGS2019

GLASS3: A standalone multi-scale seismic detection associator

The automated global real-time association of phase picks into seismic sources comes with unique challenges when simultaneously monitoring at local, regional and global scales. High spatial variability in seismic station density, transitory seismic data availability, and time-varying noise characteristics of individual stations must be considered in the design of an associator that is fast and accurate with a low false association rate. These challenges are particularly apparent at the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC), which monitors seismicity in near-real time on local, regional, and global scales using seismic data from roughly 2,100 real-time seismic stations. In order to fully leverage this large dataset, NEIC developed a stand-alone, self-configuring seismic phase associator, GLASS3 (GLobal ASSociator 3) that simultaneously processes variably scaled 3D association webs, each with a unique set of nucleation criteria (e.g., nucleation stack threshold). GLASS3 has many useful features for real-time monitoring including its computational efficiency, instantaneous pick processing, and on-the-fly configurability such as the creation and removal of targeted association webs and updates to supporting station metadata. GLASS3 runs both as part of a real-time event processing system, and as a configurable standalone associator that can be applied to a large variety of seismic problems. Here we describe the GLASS3 algorithm and demonstrate (including input data and configuration files) its use in associating phase-ambiguous picks on multiple scales.

BSSA↗

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↗

USGS2022

The potential of using fiber optic distributed acoustic sensing (DAS) in earthquake early warning applications

As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground‐motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain‐derived ground‐motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground‐motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground‐motion metrics from DAS data. To obtain high‐quality ground‐motion observations, EEW‐compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.

Bulletin of the Seismological Society of America↗

USGS2022

Enumerating plausible multifault ruptures in complex fault systems with physical constraints

We propose a new model for determining the set of plausible multifault ruptures in an interconnected fault system. We improve upon the rules used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to increase connectivity and the physical consistency of ruptures. We replace UCERF3’s simple azimuth change rules with new Coulomb favorability metrics and increase the maximum jump distance to 15 km. Although the UCERF3 rules were appropriate for faults with similar rakes, the Coulomb calculations used here inherently encode preferred orientations between faults with different rakes. Our new rules are designed to be insensitive to discretization details and are generally more permissive than their UCERF3 counterparts; they allow more than twice the connectivity compared to UCERF3, yet heavily penalize long ruptures that take multiple improbable jumps. The set of all possible multifault ruptures in the California fault system is near-infinite, but our model produces a tractable set of 326,707 ruptures (a modest 29% increase over UCERF3, despite the greatly increased connectivity). Inclusion in the rupture set does not dictate that a rupture receives a significant rate in the final model; rupture rates are subsequently determined by data constraints used in an inversion. We describe the rupture building algorithm and its components in detail and provide comparisons with ruptures generated by a physics-based multicycle earthquake simulator. We find that greater than twice as many ruptures generated by the simulator violate the UCERF3 rules than violate our proposed model.

California↗

USGS2022

Real-time earthquake detection and alerting behavior of PLUM ground-motion-based early warning in the United States

We examine the real‐time earthquake detection and alerting behavior of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm and compare PLUM’s performance with the real‐time performance of the current source‐characterization‐based ShakeAlert System. In the United States (U.S.), PLUM uses a two‐station approach to detect earthquakes. Once a detection is confirmed, observed modified Mercalli intensity (MMI) distributions are forecast onto a regular grid, in which the preferred alert regions are grid cells with MMI 4.0+ forecasts. Although locations of dense station coverage allow PLUM to detect small ( M < 4.5) earthquakes typically not considered for EEW in the U.S., a PLUM detection on a small earthquake does not always generate an alert. This is because PLUM alerts are determined by current shaking distributions. If the MMI 4.0+ shaking subsides prior to detection confirmation by shaking at a second neighboring station, the prior MMI 4.0+ information will not be in the alert forecasts. Of the 432 M 3.0+ U.S. West Coast earthquakes in 2021, 33 produced ground motions large enough to be detected by PLUM. Twenty‐four generated MMI 4.0+ PLUM alerts, whereas ShakeAlert issued public EEW alerts for 13 of these earthquakes. We compare PLUM and ShakeAlert alert regions with ShakeMap and “Did You Feel It?” intensity distributions. Because PLUM alert regions surround stations observed to have strong ground motions (regardless of earthquake magnitude), PLUM alerts reliably include locations that experience significant shaking. This is not necessarily the case for ShakeAlert alert regions when there are large errors in magnitude or epicenter estimates. For two of the largest earthquakes in our real‐time dataset, the M 6.0 Antelope Valley and M 5.1 Petrolia earthquakes, the inclusion of PLUM would have improved real‐time ShakeAlert performance. Our results indicate that incorporation of PLUM into ShakeAlert will improve the robustness of the EEW system.

California, Oregon, Washington↗

USGS2023

A comprehensive fault system inversion approach: Methods and application to NSHM23

We present updated inversion‐based fault‐system solutions for the 2023 update to the National Seismic Hazard Model (NSHM23), standardizing earthquake rate model calculations on crustal faults across the western United States. We build upon the inversion methodology used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to solve for time‐independent rates of earthquakes in an interconnected fault system. The updated model explicitly maps out a wide range of fault recurrence and segmentation behavior (epistemic uncertainty), more completely exploring the solution space of viable models beyond those of UCERF3. We also improve the simulated annealing implementation, greatly increasing computational efficiency (and thus inversion convergence), and introduce an adaptive constraint weight calculation algorithm that helps to mediate between competing constraints. Hazard calculations show that ingredient changes (especially fault and deformation models) are the primary driver of hazard changes between NSHM23 and UCERF3. Updates to the inversion methodology are also consequential near faults in which the slip rate in UCERF3 was poorly fit or was satisfied primarily using large multifault ruptures that are now restricted by explicit b ‐value and segmentation constraints.

Bulletin of the Seismological Society of America↗