DANTE: Physics-Informed Neural Operator for DAS-to-Velocity Waveform Reconstruction Without Co-located Seismometers

Abstract

Distributed Acoustic Sensing (DAS) converts existing fibre-optic cables into dense seismic arrays at near-zero deployment cost, but measures strain rate rather than particle velocity -- the quantity required by virtually all seismological analysis tools. Converting strain rate to particle velocity by numerical integration is ill-posed: the integration constant is undefined and noise accumulates without bound. We present DANTE (DAS-to-velocity via physics-informed neural operator for Acoustic-wave recoNstruction in heTErogeneous media), a Fourier Neural Operator (FNO) trained entirely on synthetic data that enforces two physics constraints: (i) the exact kinematic relation between DAS strain rate and the spatial gradient of particle velocity, and (ii) the one-dimensional elastic wave equation. These constraints resolve the undetermined integration constant and suppress noise without requiring co-located seismometers. On a test set of 200 heterogeneous synthetic wavefields, DANTE achieves a mean output SNR of $15.3\pm 8.8$ dB, Pearson correlation $r=0.907$, and SSIM $=0.976$, corresponding to a mean SNR improvement of approximately $+15$ dB over the best conventional baseline (trace stacking, $n=10$, $0.02\pm 0.06$ dB), and up to $+28.8$ dB on the most challenging samples. Zero-shot inference on seven real microseismic events from the Utah FORGE 2019 DAS dataset yields a kinematic residual of 0.003--0.005, five times lower than the synthetic test baseline, confirming generalisation to real field data with no fine-tuning and no seismometers.

15 pages, 7 figures, 2 tables. Preprint; to be submitted to Neural Networks (Elsevier)