A Speech Enhancement-based Method for Denoising Microseismic Distributed Fiber-Optic Sensing (DFOS) data

The recent advances in seismic data acquisition technology allow to transform fiber-optic cables into dense arrays of geophones that sample nearly continuously the seismic wavefield. This technology, known as Distributed Fiber-Optics Sensing (DFOS) (or Distributed Acoustic Sensing, DAS), has the potential to make cost-effective microseismic monitoring operations in borehole installations. Unlike conventional geophones, fiber-optic cables can be easily installed behind well casings without interfering with injection or production activities, eliminating the need for drilling dedicated monitoring wells.

Despite these benefits, DFOS data is generally characterized by higher noise levels when compared to conventional seismometers. The development of efficient denoising techniques is therefore a critical step to improve the Signal-to-Noise Ratio (SNR) of DFOS recordings, enhancing the capability to detect and analyse microseismic events. Traditional filtering techniques often struggle to recover low-amplitude signals, leading to limited noise reduction performances. In this study, we propose an effective denoising workflow based on an adaptation of spectral-subtractive algorithms, typically used in the context of speech enhancement for audio signals. 

We validate this approach first simulating synthetic DFOS data resembling realistic data acquisition geometries and noise conditions. Then, our denoising workflow is further applied to real DFOS data recorded during the April 2022 stimulation campaign at the FORGE (US) EGS project. 

Our results from both synthetic and real DFOS data show significant SNR improvements, showcasing the robustness of our method even when the original data show poor SNR conditions. This algorithm outperforms standard filtering techniques, offering a promising solution for enhancing DFOS data and improving the detection of previously hidden signals.