Mendenhall Program: Earthquake-related opportunities

We seek a Mendenhall Fellow to examine the neotectonic evolution of the Cascade Range (CA, OR, WA), a dynamic, high-elevation landscape controlled by recent glaciation, magmatism, and faulting. We invite proposals to conduct interdisciplinary research to characterize the interactions and spatiotemporal patterns of climate, magmatism, tectonics, and natural hazards within the Cascade Range.

Extremely large (and impactful) geophysical events occur only rarely. As a result, it is difficult to estimate their occurrence frequency and geographic expression. Generalized statistical methods can be specialized using physical principles to improve the accuracy of hazard and risk estimates. 

This research opportunity focuses on constraining long-term fault slip rates used in earthquake rupture forecasts as part of the National Seismic Hazard Model. A major emphasis is developing and applying novel techniques, such as Bayesian methods, to characterize uncertainties.

We seek a Mendenhall Fellow to investigate earthquake sequence evolution and how it may vary regionally, supporting goals to forecast and communicate earthquake activity on a range of timescales. This could include earthquake sequence physics and/or statistics, near-real-time characterization, influences of spatial variation on seismic hazard forecasts, and implementing research into model updates

This research opportunity focuses on developing models for the ground-motion characterization of the U.S. National Seismic Hazard Model (NSHM). The opportunity encompasses basic and applied research on earthquake ground motions with future application to the NSHM. 

The USGS seeks to develop a framework to rapidly determine fault rupture dimensions in large earthquakes for accurate shaking estimation. This involves near-real-time collaboration among seismologists, engineers, geologists, and geodesists—harmonizing disparate seismological, faulting, imagery, and impact observations needed to constrain rupture complexity and the shaking distribution. 

This opportunity seeks to constrain past and future earthquake rupture characteristics along the Cascadia Subduction Zone. Proposals are encouraged that enhance and/or leverage the existing geologic record to constrain the magnitude, extent, and spatiotemporal clustering of earthquakes, as well as quantitative estimates of coseismic and interseismic deformation across multiple earthquake cycles. 

This Opportunity will test the hypothesis that submarine deposits left by episodic sediment gravity flows and sampled in cores provide clues about their triggering processes, particularly past great (M>8) earthquakes. These deposits (‘turbidites’) record massive sediment transport events, in which sediments may flow destructively hundreds of kilometers and pose hazards to offshore structures.

Physics-based models have the potential to fill in information gaps that are currently limiting practical seismic hazard analysis. This research opportunity seeks to advance our understanding of the links between these physical processes, the expected shaking at any location, and seismic hazard assessment, through the use of computer simulations and/or development of 3D geologic models.

Geologic Carbon Sequestration (GCS) is a technology to reduce emission of greenhouse gases and is gaining traction across the U.S. One major hurdle is the potential of induced seismicity associated with injection of CO2 underground. We propose to investigate the efficacy of hydromechanical model-based seismicity forecasting for GCS as a possible future important seismic hazard mitigation tool. 

The Fellow will use high-resolution information on crustal fault and seismic velocity structure provided by the distributed acoustic sensing (DAS)/fiber-optic technique to understand fault properties and characterize seismic hazard. DAS technique development with a connection to understanding fault geometry, seismic velocity structure, and improving ground motion predictions is encouraged.

We seek applicants to develop a machine-learning model that can be used for earthquake forecasting. This model should provide probabilistic forecasts of the future earthquake rate, earthquake locations, and sizes.