Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet-Domain Kalman Filter

Abstract

Crosscorrelation is a classical signal-processing technique that plays an important role in exploration and earthquake geophysics. Seismic velocity estimation utilizes the crosscorrelation between observed and predicted seismic records in traveltime tomography. The crosscorrelation between two stations represents the Green’s functions retrieved from ambient noises in passive seismic interferometry. It can be used to estimate the subsurface velocity and amplitude information. The calculation of crosscorrelation usually assumes that the input data are stationary; however, the real seismic data are often non-stationary, due to the presence of multiple wave-modes and background noises. The seismic crosscorrelations often have low signal-to-noise ratio and frequently fail to provide correct information for subsequent processing. To address this problem, we develop a comprehensive technique to reduce contamination and improve the quality of crosscorrelation in the wavelet domain. The new procedure includes the forward wavelet transformation of raw records, the crosscorrelation between wavelet coefficients, single-channel image object detection, multi-channel Kalman-filter object tracking, and inverse wavelet transformation to produce the new crosscorrelation gathers. We effectively remove the unwanted components associated with contaminated wave-modes as the proposed detection and tracking algorithm can accurately extract the target wave-mode. We validate the method for three datasets: a marine streamer survey, a borehole survey, and a broadband dataset from seismology stations. We demonstrate that the proposed method can significantly improve the signal-to-noise ratio of the seismic crosscorrelations, considerably enhancing the quality of the data for subsequent advanced crosscorrelation-based seismic processing.