Protecting the weak signals in distributed acoustic sensing data processing using local orthogonalization: the FORGE data example

Abstract

The development of the distributed acoustic sensing (DAS) technique enables us to record seismic data at a significantly improved spatial sampling rate at meter scales, which offers new opportunities for high-resolution subsurface imaging. However, DAS recordings are often characterized by low signal-to-noise ratio (S/N) due to the presence of data noise, significantly degrading the reliability of imaging and interpretation. Current DAS data noise reduction methods remain insufficient in simultaneously preserving weak signals and eliminating various types of noise. Particularly, when dealing with DAS data that are contaminated by four types of noise (i.e., high-frequency noise, high-amplitude erratic noise, horizontal noise, and random background noise), it becomes challenging to attenuate the weak signals while maintaining fine-scale features. To address the issues raised above, we propose an integrated local orthogonalization (LO) method that can remove a mixture of different types of noise while protecting the useful signal. The proposed LO method effectively eliminates the aforementioned noise by concatenating multiple denoising operators including a bandpass filter, structure-oriented spatially-varying median filter, dip filter in the frequency-wavenumber domain, and curvelet filter. Next, the local orthogonalization weighting operator is applied to extract signal energy from the removed noise section. We demonstrate the robustness of the proposed LO method on various challenging DAS datasets from the FORGE geothermal field. The denoising results demonstrate that the proposed LO method can successfully minimize the levels of different types of noise while preserving the energy of weak signals.