SCEC Award Number 21148 View PDF
Proposal Category Individual Proposal (Data Gathering and Products)
Proposal Title Continued Development of OpenSHA/UCERFs in Support of OEF, Hazard and Risk Assessments, with a Focus on User Needs
Name Organization
Christine Goulet University of Southern California Edward Field United States Geological Survey Kevin Milner University of Southern California
Other Participants
SCEC Priorities 4c, 4d, 5a SCEC Groups CS, EFP, GM
Report Due Date 03/15/2022 Date Report Submitted 05/25/2022
Project Abstract
Development in 2021 was focused on preparations for the 2023 update to the USGS National Seismic Hazard Model (NSHM23). For this update, we improved upon the inversion-based methodology used in the Third Uniform California Earthquake Rupture Forecast (UCERF3), and extended it to the entire western US. We developed an updated plausibility filtering process by which the set of allowed multi-fault ruptures are determined. We also developed new filters using Coulomb stress transfer that improve the physical-consistency of the rupture set and add more connectivity to the model (Milner et al., 2022).

We retooled the simulated annealing algorithm used by UCERF3 to solve the inversion. We improved convergence by optimizing the java implementation of the algorithm and also switching to a new variable perturbation function that chooses random perturbations that are more likely to be kept in each annealing iteration. We also implemented a new set of uncertainty-weighted constraints where each constraint is evenly fit relative to its uncertainty.

To improve access to UCERF3 and future models, we greatly simplified and standardized file formats for storing model results for UCERF3, NSHM23, and similar models. We also released a beta version of a set of command line tools that allows end-users to build and modify UCERF3 and NSHM23 rupture sets and inversion solutions. These tools and improvements will increase accessibility and ease adoption of both current and future models.

We continued to support OpenSHA desktop applications, which were downloaded nearly 3,000 times in 2021.
Intellectual Merit The work described in this report helps achieve SCEC’s goal of integrating data and models into usable products that also support continued research. It improves upon previous approaches in multiple ways, including a more physically consistent set multi-fault ruptures, better inversion convergence and data fits than prior models, and better acknowledgement and sampling of model uncertainties. These innovations have been or will be published in peer-reviewed journals, as well as internally within the USGS.
Broader Impacts OpenSHA, and its implementation of the UCERF3 models, continues to be a valuable tool for the SCEC community. OpenSHA is used by engineers, researchers, and students, and was downloaded nearly 3,000 times in 2021.

The work on the 2023 update to the national seismic hazard model (NSHM23) will help to synthesize data from a number of different SCEC-funded projects (e.g., fault slip rate and paleoseismic studies) into useful and widely used models for the community. We also led a training session in 2021 to demonstrate new tools for building, modifying, and interacting with UCERF3 and NSHM23 results.
Exemplary Figure Figure 1
3D view looking north of faults model 3.1 from the Third Uniform California Earthquake Rupture Forecast (UCERF3) model discretized into 2 km x 2 km patches for Coulomb calculations. In this example, eight subsections of the Garlock fault are used as sources (green) with 1 m displacement and Coulomb stress changes are computed to all other patches (with contributions summed across all source patches). Receiver patches are colored by their sign with darker colors indicating greater amplitude, and subsection outlines are colored by the sum across all receiver patches (red is positive, blue negative). This shows the Coulomb-preferred co-rupture direction of the left-lateral Garlock Fault connecting to the Mojave section of the right-lateral San Andreas Fault (SAF). Coastlines are overlaid in black.
Credit: from Milner et al. (2022)