Processing Approaches for DAS-Enabled Continuous Seismic Monitoring

Distributed Acoustic Sensing (DAS) is creating a "field as laboratory" capability for seismic monitoring of subsurface changes. By providing unprecedented spatial and temporal sampling at a relatively low cost, DAS enables field-scale seismic monitoring to have durations and temporal resolutions that are comparable to those of laboratory experiments. Here we report on seismic processing approaches developed during data analyses of three case studies all using DAS-enabled seismic monitoring with applications ranging from shallow permafrost to deep reservoirs: (1) 10-hour downhole monitoring of cement curing at Otway, Australia; (2) 2-month surface monitoring of controlled permafrost thaw at Fairbanks, Alaska; (3) multi-month downhole and surface monitoring of carbon sequestration at Decatur, Illinois. We emphasize the data management and processing components relevant to DAS-based seismic monitoring, which include scalable approaches to data management, pre-processing, denoising, filtering, and wavefield decomposition. DAS has dramatically increased the data volume to the extent that terabyte-per-day data loads are now typical, straining conventional approaches to data storage and processing. To achieve more efficient use of disk space and network bandwidth, we explore improved file structures and data compression schemes. Because noise floor of DAS measurements is higher than that of conventional sensors, optimal processing workflow involving advanced denoising, deconvolution (of the source signatures), and stacking approaches are being established to maximize signal content of DAS data. The resulting workflow of data management and processing could accelerate the broader adaption of DAS for continuous monitoring of critical processes.