SCEC Award Number 17026 View PDF
Proposal Category Collaborative Proposal (Data Gathering and Products)
Proposal Title Implementing rapid, probabilistic association of earthquakes with source faults in the CFM for southern California
Name Organization
John Shaw Harvard University Egill Hauksson California Institute of Technology
Other Participants Andreas Plesch
Men-Andrin Meier
SCEC Priorities 3a, 2a, 3e SCEC Groups Seismology, EFP, CXM
Report Due Date 06/15/2018 Date Report Submitted 06/14/2018
Project Abstract
This project has developed a new, statistically robust way to identify the fault (or sets of candidate faults) in the Community Fault Model (CFM) that generated an earthquake using information typically provided soon after these events occur (Evans, 2016). This effort effectively bridges the information provided by increasingly sophisticated near real-time seismograph networks with comprehensive 3D Community Fault Models (CFMs) developed by SCEC (Plesch et al., 2007). Our method of earthquake-to-fault association was developed using comprehensive earthquake hypocenter and focal mechanism datasets in California through 2016 (after Hauksson et al., 2012; Yang et al., 2012) and the southern California Community Fault Model (CFM) (Plesch et al., 2007) to assess what properties of earthquakes serve as the best predictors of the fault on which they occurred. We used a series of training datasets for earthquakes that were known to have occurred on faults within the model, and established that proximity (distance), focal mechanism (nodal plane orientation), and earthquake history (spatial and temporal clustering) can be combined in a robust way to assign probability that a given earthquake was associated with one or more source faults in the model (or on a fault not included in the model). Notably, these training datasets were comprised of earthquakes that occurred in the decade since the release of CFM 2.0, to ensure that they did not influence the modeled fault geometries.
Objective earthquake-to-fault associations are of value as they provide an important measure of the activity of
Intellectual Merit This project relates to many key SCEC objectives and will improve our understanding of earthquake activity across southern California. In particular, our method for associating seismicity to faults will provide better delineation of fault structures and make possible more advanced seismicity studies by us and other SCEC researchers. Our analyses provide fundamental insights into earthquake activity, the crustal strain field, major faults, and crustal geophysics.
Broader Impacts The outreach activities consisted of publishing the results of the research in peer-reviewed journals. Also, the new catalog with fault distance probabilities is being distributed to researchers via the Southern California Earthquake Data Center (SCEDC). We have also presented results at SCEC workshops.
Exemplary Figure Figure 2: Effect of adding predictors to association model trained on CFM 2 faults, shown for the Laguna Salada fault; yellow events are miss-associated; (left) using only distance (Model 1); (middle) using distance and nodal planes (Model 2); (right) using distance, nodal planes and clustering (Model 3). (Evans et al., 2018)