Enriched DeepONet
An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
Abstract:
The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems. Despite recent progress, real-time earthquake localization remains challenging due to the need to learn a generalizable Eikonal operator. We introduce a novel deep learning architecture, Enriched-DeepONet (En-DeepONet), addressing the limitations of current operator learning models in dealing with moving-solution operators. Leveraging addition and subtraction operations and a novel `root' network, En-DeepONet is particularly suitable for learning such operators and achieves up to four orders of magnitude improved accuracy without increased training cost. We demonstrate the effectiveness of En-DeepONet in earthquake localization under variable velocity and arrival time conditions. Our results indicate that En-DeepONet paves the way for real-time hypocenter localization for velocity models of practical interest. The proposed method represents a significant advancement in operator learning that is applicable to a gamut of scientific problems, including those in seismology, fracture mechanics, and phase-field problems.
@article{haghighat2023novel,
title={An enrichment approach for enhancing the expressivity of neural operators with applications to seismology},
author={Haghighat, Ehsan and Waheed, Umair bin and Karniadakis, George},
journal={arXiv preprint arXiv:2306.04096},
url={https://arxiv.org/abs/2306.04096},
year={2023}
}Data and code related to this work will be shared here.
Requirements
Tested on python>=3.8 and python<=3.10. Install the requirements using the following command:
pip install -r requirements.txt