Multi-source wavefield reconstruction of distributed acoustic sensing data using compressive sensing and seismic interferometry

Abstract

Seismic data recorded by distributed acoustic sensing (DAS) interrogator units on deployed optical fiber are being used for a variety of subsurface imaging and monitoring investigations. To reduce the costs of active-source DAS surveying applications, seismic interferometry can be applied to estimate inter-sensor wavefields from DAS records. However, recording long-term records for ambient interferometry requires considerable data storage and sections of DAS optical fibers may be unusable because of broadside sensitivity considerations from the DAS fiber orientation and due to localized coherent energy sources with amplitudes significantly larger than the ambient signal of interest. Compressive sensing, a wavefield reconstruction technique, can mitigate the problems of large data storage and unusable data. We apply compressive sensing–based multi-source wavefield reconstruction to estimate correlograms of ambient DAS records from a fiber array in Perth, Australia. The multi-source method uses all available virtual-source gathers for simultaneous wavefield reconstruction and is different from the conventional single-source method that separately reconstructs individual virtual-source gathers. Using the Fourier and curvelet transforms to sparsify interferometric wavefields, we show that multi-source reconstruction is applicable to the DAS data and that the Fourier multi-source reconstruction can improve the recovered wavefields by approximately 5–10 dB, compared to the Fourier and curvelet single-source wavefield reconstructions.