Physics-Informed U-Net Fourier Neural Operator for DAS Microseismic Localization at Utah FORGE

Microseismic source localization is a key diagnostic in stimulated reservoirs, supporting spa-
tiotemporal tracking of fracture activation, stress transfer, and operational risk. Distributed Acoustic
Sensing (DAS) provides dense strain-rate observations along fiber-optic cables, but the resulting data
volume and strong near-well heterogeneity motivate localization workflows that are both fast and
physically constrained. We present an Eikonal-regularized U-Net Fourier Neural Operator (U-FNO)
that predicts full first-arrival traveltime fields for a given source location in a known 2-D velocity
model. The architecture combines Fourier-domain operator learning to capture long-range kinematic
structure with a multiscale encoder–decoder to recover spatial detail. Training is guided by an
Eikonal-consistency loss, complemented by source anchoring and a non-negativity constraint to
encourage physically admissible solutions. We benchmark U-FNO against a vanilla FNO baseline
and fast-marching traveltime solutions across velocity models of increasing complexity (smooth
gradient, Marmousi, and Utah FORGE). In the FORGE model, U-FNO reduces traveltime RMSE
by up to 97% relative to the baseline and reaches comparable misfit in up to 50% fewer training time.
Field transferability is assessed using 15 DAS-recorded events from the Utah FORGE microseismic
catalogue. Models are fine-tuned on four events and evaluated on the remaining events. U-FNO converges
within 2 minutes (versus 45 minutes for FNO) and reduces the mean location error from 33.71 m to
29.28 m. These results indicate that physics-regularized neural operators with multiscale structure can
deliver accurate, scalable, near-real-time localization for high-volume DAS monitoring in complex
geothermal settings.