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Methods to Evaluate and Improve the Modeling of Rupture Directivity in Assessment of Seismic Hazard

Description: 

In recent years, there have been advancements that model near-source effects of earthquake rupture on strong ground shaking, leading to an improved characterization of ground motions. Moving forward, modern techniques can be used to incorporate source characteristics and near-fault ground motion behavior that contribute to the azimuthally varying effects that result in rupture directivity. One example is the application of machine learning methods to support more automated integration of new predictor variables in model development and to allow more evaluation opportunities to assess residuals. Here, we utilize several techniques to take advantage of the plethora of synthetic data and its ability to supplement trends observed in data. We showcase two examples of how models can be developed using artificial neural networks (ANNs). We evaluate the performance of the ANN with existing methods, comparing misfit and evaluate how to improve upon these methods in the future.

One ANN approach uses a set of simulations with synthetic ground motions from the Southern California Earthquake Center (SCEC) CyberShake study to develop a ground motion model adapted to incorporate seismic directivity information using an ANN. This large database (TBs) enables us to train the model to better capture magnitude, period, and distance variations and how they relate to amplification from hypocenters located along finite-faults during training. In some cases, there is reduced misfit from better representing source features that aren’t included in base ground motion models that neglect hypocenter location (e.g., azimuthal variation, source-to-site terms). Another ANN method uses a shallow-layered neural network model to better fit a hypocenter-independent model. This method adjusts the median and aleatory variability to account for the averaged impact of various hypocenter distributions to fit the underlying directivity adjustment model. This method serves as a template to apply to other directivity models, improving computational efficiency and more readily enabling integration in hazard codes.

Session: The 2023 USGS National Seismic Hazard Model and Beyond [Poster Session]

Type: Poster

Date: 5/1/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Kyle

Student Presenter: No

Invited Presentation: 

Authors