1887
Volume 44, Issue 1
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

Distributed Acoustic Sensing (DAS) provides dense spatial, robust and cost-effective seismic monitoring. However, automated analysis of DAS recordings remains challenging due to their variable and inherently two-dimensional structure, densely sampled in space and time. While explicit methods often struggle with this complexity, machine learning schemes are well-suited to handle such data but are limited by the need for large, labelled datasets, which are rarely available for DAS. Furthermore, unique properties of different DAS datasets make it necessary to have a representative labelled dataset for each new application, which limits the utility of pre-trained models. To overcome these limitations, we propose a fully automated, generalisable framework that synthesizes realistic, labelled DAS datasets using cycle-consistent adversarial networks. We demonstrate the benefits of such datasets by training a seismic phase arrival picker for microseismic DAS images by comparing two models: one trained on purely synthetic data and one trained on domain-translated data, using our proposed method. The latter model provides more accurate predictions by effectively reducing model generalisation issues. The end-to-end pipeline is fully automatable and provides a scalable machine learning tool for DAS-based seismic analysis.

EAGE Publications BV
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2026-01-01
2026-01-24

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Automated Realistic Labelled DAS Data Synthesis using CycleGANs and their Application to Phase Arrival Picking
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