Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet-Domain Kalman Filter
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Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet‑Domain Kalman Filter Yang Zhao1 · Fenglin Niu2 · Zhishuai Zhang3 · Xiang Li4 · Jinhong Chen5 · Jidong Yang6 Received: 23 March 2020 / Accepted: 3 October 2020 © Springer Nature B.V. 2020
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-tonoise ratio of the seismic crosscorrelations, considerably enhancing the quality of the data for subsequent advanced crosscorrelation-based seismic processing. Keywords Crosscorrelation · Kalman filtering · Wave-mode separations · Object tracking
* Yang Zhao [email protected] Extended author information available on the last page of the article
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Surveys in Geophysics
1 Introduction Crosscorrelation is a powerful signal-processing tool and extensively adopted in pattern recognition and signal detection among different industries, such as acoustics, medical applications, and geophysics. It projects one signal onto another as a means of measuring how much of the second signal is present in the first, so it can detect the presence of known signals as components of more complicated signals (Keane and Adrian 1992). Crosscorrelation of pressure transients and acoustic waves was applied for leak detection and location in the pipeline network of water authorities (Hafezi and Mirhosseini 2015). An active radar with omnidirectional sensors used crosscorrelation to measure the
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