The synchrosqueezing generalized S-transform (SSGST) is commonly used to generate an isofrequency component of a signal by squeezing the decomposed frequency components of the signal. However, for seismic signals, the single-trace process can have a lack of lateral information in the squeezed results and lead to some discontinuous geologic information that will mislead the interpreter. Thus, to improve the stability of SSGST, we have developed a multichannel seismic trace squeezing method. Multichannel SSGST (MSSGST) considers the decomposed frequency components of neighboring traces of the analysis seismic trace and then reconstructs the center trace. Therefore, compared with SSGST, the multichannel processing improves the stability of the squeezing and produces more laterally continuous results that properly follow the geologic phenomenon. The effectiveness of MSSGST is validated using various field data. We use the application to demonstrate the potential of multichannel squeezing to perform well at (1) improving the energy focusing and continuity of the decomposed frequency components, (2) depicting the boundaries of geologic structures, and (3) identifying the thin layers.

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.

Seismic waves experience absorption and attenuation by rock strata during propagation, resulting in high-frequency energy loss. A common method to solve this problem is to use a scaling factor to dynamically scale wavelets in the time– frequency domain and utilise a high-frequency wavelet for replacing the original wavelet to achieve dynamic inverse filtering. However, because a stable scaling factor is used, this method suffers from the problems of band shift and low-frequency energy loss after processing. To overcome these problems, an innovative time–frequency domain dynamic inverse filtering method using an adaptive scaling factor is proposed in this paper. This method performs broadband wavelet dynamic inverse filtering by establishing the correlation between the scaling factor and time and frequency and expanding the low-frequency and high-frequency parts of the wavelet. This method can take into account both the low- and high-frequency parts of the wavelet during high-resolution processing and provides wide-frequency-band seismic data.