Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Skip to content

Regional Finite-Fault Models of the 2019 Mw7.1 Ridgecrest, California, Earthquake

Metadata Updated: July 6, 2024

This dataset complements the following publication: Goldberg, D.E. & Haynie, K.L (2021) Ready for Real-Time: Performance of Global Navigation Satellite Systems in 2019 Mw7.1 Ridgecrest, California, Rapid Response Products, Seismological Research Letters, doi: 10.1785/0220210278. The availability of low-latency, high-rate Global Navigation Satellite Systems (GNSS) waveforms makes it possible to compute joint seismic and geodetic finite-fault models of significant earthquakes (typically M 6.0 or larger) using regional data (i.e. from strong-motion accelerometers and real-time GNSS). Notably, real-time GNSS displacement data has reduced accuracy when compared to post-processed displacements, due to inherent challenges in estimating satellite clocks and orbits in real-time (see associated manuscript for details). Here, we present the results of joint strong-motion accelerometer and GNSS finite-fault inversions for the 2019 Mw7.1 Ridgecrest, California earthquake. We compare the results of the joint inversions that use post-processed GNSS to those making use of real-time GNSS displacements. Real-time GNSS displacements come from two different processing facilities: UNAVCO and Central Washington University (CWU). Two different weighting schemes (uniform and data norm weighting) are applied, resulting in a total of six joint inversions. A figure showing these six models is included here ("Finite-Fault Model Results") and is a reproduction of Figure 3 of the associated manuscript listed above. The inversion results are provided as text files with titles corresponding to their GNSS data processing type and the inversion data weighting scheme (e.g., "Strong-Motion and CWU Real-Time GNSS (Uniform Weight)." Please see the associated manuscript listed above for details about the GNSS processing types and weighting schemes applied. A summary table comparing the six models (above) and the USGS teleseismic inversion ( is titled "Finite-Fault Model Comparison Summary". The resulting models are also used to create an estimate of the source dimensions as input to the USGS ShakeMap ground motion estimates. Estimated source dimension information is available in the table titled "Source Dimension Estimates for ShakeMap".

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

Downloads & Resources


Metadata Created Date June 1, 2023
Metadata Updated Date July 6, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date July 6, 2024
Publisher U.S. Geological Survey
Identifier USGS:617b0d8dd34ea58c3c700023
Data Last Modified 20220104
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id b7f58944-72f5-4290-828b-fc81aa30a87c
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -118.5,34.5,-116.5,36.8
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash af835f3bbccdb3299b7f8e15f515affde304d418218218bb1666602ec02936f9
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -118.5, 34.5, -118.5, 36.8, -116.5, 36.8, -116.5, 34.5, -118.5, 34.5}

Didn't find what you're looking for? Suggest a dataset here.