Monitoring seismic velocity variations using DAS data: a workflow based on seismic swarm analysis

Monitoring temporal variations of seismic velocities (dv/v) is a key tool for investigating stress changes and damage processes in active tectonic regions. Traditional dv/v studies rely on dense seismic networks or on repeating earthquakes, which can limit their spatial resolution and applicability. Distributed acoustic sensing (DAS), by providing continuous and densely sampled measurements of the seismic wavefield, offers new opportunities to overcome these limitations and to develop high-resolution velocity monitoring strategies.

In this study, we investigate how dv/v can be estimated from DAS data by focusing on the analysis of seismic swarms. We develop a processing workflow and apply it to DAS data acquired from three submarine telecommunication fiber-optic cables of ~150 km each, with ~10,000 sensing points per cable, deployed in the central part of Chile (Abyss network). We first identify seismic swarms and quantify waveform similarity between events using multi-channel cross-correlation analysis. We then select event pairs exhibiting high waveform similarity across multiple DAS channels for further analysis. We analyze coda waves using a cross-spectral approach to estimate coherence and phase delays between events, and we infer relative seismic velocity variations from a linear regression of the measured time delays over selected coda time windows starting a few seconds after the S-wave arrival.

Through this work, we present a systematic framework for estimating dv/v from DAS-recorded seismic swarms and assess its sensitivity to event similarity, frequency band, and coda window selection. This work shows that seismic swarms, when recorded by dense DAS arrays, provide a promising basis for developing high-resolution seismic velocity monitoring strategies.