English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Eikonal equation plays a central role in seismic wave propagation andhypocenter localization, a crucial aspect of efficient earthquake early warningsystems. Despite recent progress, real-time earthquake localization remainschallenging due to the need to learn a generalizable Eikonal operator. Weintroduce a novel deep learning architecture, Enriched-DeepONet (En-DeepONet),addressing the limitations of current operator learning models in dealing withmoving-solution operators. Leveraging addition and subtraction operations and anovel `root' network, En-DeepONet is particularly suitable for learning suchoperators and achieves up to four orders of magnitude improved accuracy withoutincreased training cost. We demonstrate the effectiveness of En-DeepONet inearthquake localization under variable velocity and arrival time conditions.Our results indicate that En-DeepONet paves the way for real-time hypocenterlocalization for velocity models of practical interest. The proposed methodrepresents a significant advancement in operator learning that is applicable toa gamut of scientific problems, including those in seismology, fracturemechanics, and phase-field problems.

An enrichment approach for enhancing the expressivity of neural operators with applications to seismology