Neural Implicit Compact Representation to Compress Distributed Acoustic Sensing Data
Abstract
Optical fiber geophysics constitutes the most significant change in geophysical instrumentation since the digital revolution of the 1970s. Although optical fiber methods offer significant benefits in terms of sensor network siting, aperture, density, and ease-of-deployment, the resulting data volumes may strain computational resources. Distributed Acoustic Sensing (DAS) in particular is a new seismic observation method that utilizes repeated laser pulses along optical fibers up to 100 km in length to measure changes in phase changes of backscattered light that occur due to rapid straining rate of the fiber. The dense observations from DAS dramatically expands the ability of seismic observation and has been used for observing ocean surface gravity waves, detecting new tectonic faults, and near-surface imaging. Due to large data volumes, its real-time or large-scale applications are limited by the high cost of both data transmission and storage. Current state-of-the-art data compression techniques for DAS involve either a low compression rate (40%) for a lossless compression (Dong et al., 2022) or lossy compressions that retain the low rank representation of the data.
Here, we use a neural implicit compact representation of an off-shore DAS data set and achieve a lossy compression ratio on the order of 0.1%. We used the data from a 4-day ocean-bottom DAS experiment on the Ocean Observatory Initiative (OOI) Regional Cabled Array to test this approach. The fidelity of the reconstructed data is tested for specific seismological use cases: the extraction of dispersion curves in the frequency-wavenumber domains, the cross-correlation between and at single channels for ambient-noise imaging and monitoring, and impulsivity filters to detect transient events such as marine mammal vocalization. This lossy compression-reconstruction approach also showcases a new framework for the real-time application of the DAS data.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMOS25E0963N