Description of the Research Opportunity
A critical element of the USGS Earthquake Hazards Program (EHP) is the assessment of seismic hazard and risk, and one of the two main modeling components in this endeavor is an earthquake rupture forecast (ERF), which gives the probability of all possible damaging events in a region and for a specified timespan. The official models developed by the USGS influence not only building codes and earthquake insurance rates, but also other flagship products such as operational earthquake forecasting (real-time info on possibly triggered events), earthquake planning scenarios, and earthquake early warning. To date, our official National Seismic Hazard Models have been time-independent (ignoring aftershocks or otherwise triggered events), which is adequate for longer-term hazard and risk quantification (e.g., building codes) but questionable for shorter-term applications (e.g., setting insurance rates). A major priority for future ERF development is therefore the incorporation of full-time dependence (spatiotemporal clustering).
ERF development is a system-level, multidisciplinary problem in that a broad range of information can be brought to bear, including seismology, geology, geodesy, and earthquake physics with respect to science, earthquake engineering in terms of ensuring model usefulness, and information technology in terms of model deployment. Another challenge is the paucity of data needed to test competing hypotheses at the large magnitudes that dominate hazard and risk, which has two important manifestations: 1) the need for adequate representation of so-called epistemic uncertainties (to reflect our limited understanding of how things actually work); and 2) the need for more physics-based approaches (to augment the limited observations at large magnitudes). Model verification and validation is obviously important, but so too is model valuation (e.g., is a new model value added given it is a limited approximation as well?). Also important is having a robust computational/IT infrastructure, especially with respect to reproducibility.
This research opportunity focuses on the development and assessment of physics-based ERFs, referred to as multi-cycle physics-based earthquake simulators (see this special issue of Seismological Research Letters and the associated preface by Tullis (2012) for an overview and examples of these models). Rather than the traditional approach of inferring earthquake magnitudes from fault area or length using statistical scaling relationships, and associated frequencies of occurrence by matching fault slip-rate and/or paleoseismic recurrence intervals, these physics-based models apply tectonic loading to a fault system and utilize frictional properties on those faults to determine when and where earthquakes occur, with each earthquake transferring stress and thereby influencing the occurrence of subsequent events.
As argued by Field (2019, 2022), multi-cycle physics-based earthquake simulators represent our best hope for addressing several first-order ERF modelling challenges, including representation of time dependences and quantifying the propensity of multi-fault ruptures. In fact, it's hard to imagine a more important pursuit with respect to influencing the sophistication of ERFs a decade from now, yet there is very little effort within the USGS with respect to the development of these models. This research opportunity is aimed at rectifying this situation.
There are, however, formidable challenges associated with multi-cycle physics-based earthquake simulator development, any one of which could be the focus of this opportunity. Examples include the following:
- Development of a new, unique simulator model (e.g., with improved approximations of inertial/dynamic effects, 3D structures, non-elastic effects, off-fault yielding, and/or fluid effects).
- Evaluation of one or more existing models with respect to identifying robust and useful inferences (e.g., average rupture rates, elastic-rebound predictability, multi-fault rupture propensity, magnitude- and slip-scaling implications, magnitude-frequency distributions, influence of creep, spatiotemporal clustering, and other possible time dependencies).
- Address challenges with respect to representing epistemic uncertainties (error propagation).
- Address information technology challenges with respect to model development, maintenance, access, and reproducibility.
- These models cannot be initiated at current conditions, but rather need to be "spun up" for thousands of virtual years before realistic behavior emerges. Therefore, even if a model is a perfect representation of reality, there is still the challenge of how to condition it on the current state of the system (e.g., historical events), either via model "steering" or by searching for points in the simulation that looks like what has occurred historically.
- Develop machine learning approaches for inferring predictability from the large data sets generated by these simulators.
- Coordinate the analysis of existing models with respect defining and implementing community standards for data formats, evaluation metrics, documentation, accessibility, and reproducibility.
Proposals are not limited to the above examples. Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.
Proposed Duty Station(s)
Golden, Colorado
Moffett, California
Pasadena, California
Areas of PhD
Geophysics, seismology, geology, earthquake engineering, computer science, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications
Applicants must meet one of the following qualifications: Research Geophysicist, Research Geologist, Research Geodesist, Research Physicist, Research Engineer, Research Computer Scientist, or Research Statistician.
(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
