While the single-channel matching pursuit decomposes a seismic trace into a series of wavelets, the multi-channel matching pursuit examines the lateral coherence of the seismic traces as a constraint to improve the lateral continuity of the decomposition. However, the presence of structures in the subsurface negatively affects the performance of the multi-channel matching pursuit. We proposed a structure-adapted matching pursuit method that uses a dynamic time-warping (DTW) algorithm to estimate the similarity between adjacent seismic traces and extract an optimal wavelet along the dip plane. This structure-adapted implementation would significantly speed up the convergence of the decomposition process.We modified the DTW algorithm by combining the Euclidean distance of the seismic trace and the first-order temporal derivative of the seismic trace. We also updated the amplitudes of all extracted wavelets simultaneously using the least-squares principle. This DTW-based, structure-adapted, multi-channel, least-squares matching pursuit would improve the robustness and accuracy of the seismic trace decomposition.

A high-resolution time?frequency spectrum is desirable for processing and interpreting seismic data. The standard W transform is a method that effectively preserves the resolution in the low-frequency region of the time?frequency spectrum of non-stationary seismic signals. To further increase the energy concentration of the time?frequency spectrum estimated with the standard W transform, we propose to combine the W transform with a chirp-modulated window. The chirp rate in the chirp-modulated window can control the rotation of the window in the time?frequency plane to achieve a better match with the time?frequency spectrum. Unlike the general linear chirplet transform, the chirp rate in the proposed algorithm can be directly determined with the estimated instantaneous frequency. It has been shown that the W transform with a chirp-modulated window maintains the resolution of the time?frequency spectrum and improves the energy concentration around the dominant frequency against noise. To speed up the computational process of the W transform with a chirp-modulated window, we formulate the transform as a matrix?vector multiplication, which can be accelerated by using GPU computations. The application of the proposed algorithm to synthetic and field data shows that the frequency anomalies can be easily identified with the proposed algorithm.

While the single-channel matching pursuit decomposes a seismic trace into a series of wavelets, the multi-channel matching pursuit examines the lateral coherence of the seismic traces as a constraint to improve the lateral continuity of the decomposition. However, the presence of structures in the subsurface negatively affects the performance of the multi-channel matching pursuit. We proposed a structure-adapted matching pursuit method that uses a dynamic time-warping (DTW) algorithm to estimate the similarity between adjacent seismic traces and extract an optimal wavelet along the dip plane. This structure-adapted implementation would significantly speed up the convergence of the decomposition process. We modified the DTW algorithm by combining the Euclidean distance of the seismic trace and the first-order temporal derivative of the seismic trace. We also updated the amplitudes of all extracted wavelets simultaneously using the least-squares principle. This DTW-based, structure-adapted, multi-channel, least-squares matching pursuit would improve the robustness and accuracy of the seismic trace decomposition.