SCEC2021 Poster #105: Estimating geodetic locking depth and long-term slip rate along the central San Andreas Fault using Neural Networks

Poster #105, Tectonic Geodesy

Estimating geodetic locking depth and long-term slip rate along the central San Andreas Fault using Neural Networks

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Poster Presentation

2021 SCEC Annual Meeting, Poster #105, SCEC Contribution #11477 VIEW PDF

Geodetic observations, such as from the Global Navigation Satellite System (GNSS), record deformation of the Earth’s crust due to strain accumulation on locked faults. Along a vertical strike-slip fault such as the San Andreas Fault, a geodetically determined long-term slip rate and locking depth may be directly proportional to the moment of a future earthquake. Locking depth is generally interpreted to correspond to the base of the seismogenic zone, but it can be difficult to estimate. Typically, the locking depth in geodesy-based fault models is prescribed, complicating its interpretation in terms of the physical behavior of the earth. As an alternative, we use a neural network to independ...ently estimate long-term slip rate and locking depth along the San Andreas Fault, using geodetic data from GNSS. Because there is insufficient real-world data available for training, we train the neural network using synthetic data generated from a model of a locked strike-slip fault. Then we apply the trained neural network to 1-D profiles of geodetic observations along the San Andreas Fault. We estimate an increase of locking depth from ~ 16.5 in the northern Cholame segment to ~ 24.2 km on the southern Carrizo segment and an increase in slip rate from ~ 33.2 mm/yr to ~36.2 mm/yr on the same segments. Our estimated slip rates agree with previous studies that prescribe locking depth and are consistent with grid search results on these segments of the San Andreas Fault; however, the neural network is 2.6 times faster to execute than a grid search over the same parameter space. This work serves as a proof-of-concept for the feasibility of estimating interseismic fault parameters with a neural network.
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Citation: Salek, S., & Evans, E. L. (2021, 08). Estimating geodetic locking depth and long-term slip rate along the central San Andreas Fault using Neural Networks. Poster Presentation at 2021 SCEC Annual Meeting.

Keywords: Neural Networks, Locking Depth, Slip Rate, Interseismic fault parameters, GNSS, SAF

Relevant SCEC Themes:

Community Models

Data-Intensive Computing