High-Resolution Small-Fault Recognition in a Time–Frequency Domain

The detection of seismic small faults is vital in shale oil and gas exploration and development. Limited by the resolution of seismic exploration, it is difficult to effectively detect small faults. Recently, many fault characterization methods have been proposed. To overcome the obscurity of seismic resolution for small faults, time–frequency analysis algorithms and fault attributes have been employed to characterize small faults and stratigraphic inflection point. However, the traditional resolution of seismic time–frequency analysis algorithms greatly limits the accuracy of small fault identification. Therefore, there is a need to improve the resolution of seismic time–frequency analysis algorithms. Herein, we propose a new time–frequency analysis algorithm and workflow, high-order multichannel synchrosqueezing variational modal generalized S-transform (HMSVGST) based on variational mode decomposition and synchrosqueezing GST (SGST). The proposed algorithm differs from the original synchrosqueezing algorithm in that it decomposes and transforms the signal simultaneously, which preserves the original signal components and avoids interference between different signal components, thereby improving the time–frequency focusing ability. A high-order multichannel synchrosqueezing variational modal GST is employed to decompose the seismic data volume in the time–frequency domain, and the optimal surface voting technique is used to characterize small faults. We set the forward model with 5–30-m fault distance and the application of real seismic data; we show that the proposed method has a good ability to characterize small faults less than 10 m, which validated the proposed method.