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Efficient Microseismic Signal Enhancement With Distributed Acoustic Sensing in Borehole Installations

Distributed Acoustic Sensing (DAS) is a powerful technology for a dense, high-resolution seismic monitoring, providing detailed images of the seismic wavefield. Such features make DAS particularly suitable for challenging environments and deep boreholes, such as those used in Carbon Capture and Storage (CCS) and Enhanced Geothermal Systems (EGS) projects. However, DAS recordings typically exhibit higher noise levels than conventional seismic sensors, limiting the detection and characterization of weak microseismic events. Additionally, large-scale monitoring campaigns generate massive data volumes, making computational efficiency essential for real-time applications. While existing denoising strategies show promising results, many rely on computationally demanding deep learning approaches or require extensive parameter tuning and model training, which limits their use during real-time acquisition.

We introduce a computationally efficient denoising method derived from image-processing techniques and tailored specifically for DAS datasets. The approach is designed to be robust on signals with a low signal-to-noise ratio (SNR) and easy to implement as a “plug-and-play” signal enhancement tool for DAS processing workflows. We evaluate its performance using DAS recordings from the Bedretto Underground Laboratory for Geosciences and Geoenergies, located in Switzerland, where multiple instrumented boreholes surrounding a stimulation well enable high-resolution monitoring of induced seismicity across hundreds of meters.

We first validate the method using synthetic data generated with realistic acquisition geometries and noise conditions based on Bedretto, and then apply it to continuous real DAS recordings from the site, assessing improvements in event detectability and source localization after denoising. Preliminary results show enhanced detectability of events with magnitudes below –2, including events that would otherwise be difficult to detect or analyze due to the low SNR.

Session: Fiber-Optic Sensing Applications in Seismology and Environmental Science [Poster]

Type: Poster

Room: Exhibit Hall A+B

Date: 4/17/2026

Presentation Time: 08:00 AM (local time)

Presenting Author: Giulio Pascucci

Student Presenter: Yes

Invited Presentation: 

Poster Number: 84

Additional Authors