Enhancing Regional Seismic Velocity Models With Higher-Resolution Local Results Using Sparse Dictionary Learning

Hao Zhang, & Yehuda Ben-Zion

Published January 22, 2024, SCEC Contribution #13439

We use sparse dictionary learning to develop transformations between seismic velocity models of different resolution and spatial extent. Starting with data in the common region of both models, the method can enhance a regional lower-resolution model to match the style and resolution of local higher-resolution results while preserving its regional coverage. The method is demonstrated by applying it to two-dimensional VS and three-dimensional VP and VS regional and local velocity models in southern California. The enhanced reconstructed regional results exhibit clear visual improvements, especially in the reconstructed VP/VS ratios, and better correlations with geological features. Moreover, the reconstructed regional VP, VS models outperform the original ones in comparison of simulated earthquake waveforms to observations. The improved fitting to observed waveforms extends beyond the domain of the overlapping region. The developed dictionary learning approach provides physically interpretable results and offers a powerful tool for additional applications of data enhancement in earth sciences.

Citation
Zhang, H., & Ben-Zion, Y. (2024). Enhancing Regional Seismic Velocity Models With Higher-Resolution Local Results Using Sparse Dictionary Learning. Journal of Geophysical Research: Solid Earth,. doi: 10.1029/2023JB027016.