EAGER: Using machine learning to develop a calibrated, remote sensing-based age model to improve late Quaternary slip-rate estimates in arid environments

This study aims to improve the methods surrounding surface landform dating, and thus methods for determining rates of fault slip. Accurate slip rates are essential for tectonics and earthquake hazards research, and often require numerous surface ages. Such dating efforts can be challenging due to a lack of datable materials, cost concerns, or accessibility of field sites.

Recent investigations have directly correlated specific remote sensing observations to the absolute age of surface landforms. Using a large repository of remote sensing data and recent advances in data science and machine learning, this study will integrate multiple, distinct types of remotely sensed data with published age data, to develop a calibrated age model that will be applied to faulted landforms in southeastern California.

The methodology will be applied to the eastern Garlock fault, a major strike-slip fault in this region, and aid in answering longstanding questions about the role of the fault in southern California tectonics. This study will make a significant contribution to earthquake hazard analysis of many active faults in southeastern California, a region under threat of damaging earthquakes and with a population of more than 3 million people.

Improved earthquake hazard assessments are critical for federal, state, and local agencies and regulatory bodies, a broad spectrum of industry, and the public. The model produced by this study can also form a framework for future surface age studies around the world. Additionally, this study will contribute to the development of the STEM workforce by advancing the education and training of a female graduate student and at least two undergraduate students, as well as the professional development of two early-career researchers, including a female assistant professor.

Geologic slip rates are essential components of seismic hazard analysis and critical to addressing many pressing questions at the forefront of tectonics and seismological research. However, discrepancies of Late-Cenozoic and present-day slip rates continue to be debated, particularly when slip rate estimates span different timescales of activity. Discriminating true slip rate discrepancies from observational biases / limitations requires an accurate (and self-consistent) view of slip rates and their temporal and spatial variability.

However, obtaining robust slip rates remains challenging, due to lack of dateable materials, cost concerns, or accessibility of field sites. Addressing these challenges, recent investigations have directly correlated specific remote sensing indices to the absolute age of landforms. Using the broad combined repository of remote sensing data from the past 20-years and recent advances in data science and machine learning, the investigators will expand on these efforts and integrate several types of remotely sensed data with published geochronology data to develop a calibrated surface property-age model that will be applied to faulted landforms in the Eastern California shear zone / southern Walker Lane of southeastern California.

The ensemble model will incorporate different modeled responses between sensed values and surface age. Ensemble modeling uses a variety of statistical and computational models to fuse an array of single variate models to solve classification and regression problems. With the variety of remote sensors, bands, and spatial scales available, a wealth of data can be consolidated into a cohesive, robust model.

The investigators believe such rigorous data treatment may yield significantly improved uncertainties on resulting ages compared with individual models. When implemented, the proposed effort will yield a calibrated means of estimating surface ages using remote sensing data and new slip rates for the eastern Garlock fault.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.