Time-Frequency Spectral Decomposition Based on Dip Guided Window Function for Accurate Dip Estimation

The dips of underground reflectors indicate their trends and provide a wealth of information about geological structures. It is widely used in seismic data processing and interpretation. The time-frequency spectrum (TFS) can be directly used for dip estimation, and TFS can be obtained by spectrum decomposition techniques such as short-time Fourier transform, Stockwell transform, and continuous wavelet transform. In spectrum decomposition technologies, seismic data are usually multiplied by a window function in the time domain. At the same travel time, all traces in the seismic data are multiplied by the same window function, which burrs the difference in dip and leads to errors in dip estimation. To solve this problem, this letter advocates an algorithm that uses the dip to guide the window function. To enhance the robustness of the algorithm, we introduce the energy weighted average algorithm, and Hilbert transform, into the algorithm. The model testing has verified that our algorithm can estimate dips accurately, and the application of field seismic data has proved the practicability of this algorithm.