Cross-equalization for time-lapse sparker seismic data

Time-lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time-lapse seismic exploration has been conducted using a large-scale exploration system. However, for efficient monitoring of shallow subsurface, time-lapse monitoring based on the small-scale exploration system is required. Small-scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal-to-noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small-scale time-lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre-stack, post-stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre-stack data processing stage. In the post-stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high-resolution time-lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.