Multitask Two-Stage Deep Learning Seismic Strong and Weak Reflection Separation

The conventional seismic strong and weak reflection separation method (SRSM) encounters significant challenges due to the complexity of seismic data, the horizon time (or depth) accuracy of the target horizon, and the space-variant seismic wavelet, resulting in undesired seismic strong and weak reflection separation results. The popular seismic facies-guided trace-by-trace high-precision seismic SRSM can address the abovementioned issues and obtain high-precision seismic strong and weak reflection separation results; however, it still requires the horizon time of the target horizon, and its computational efficiency needs to be improved. In this article, in order to obtain high-efficiency high-precision seismic strong and weak reflection separation results without any horizon time, we propose a multitask two-stage deep learning seismic strong and weak reflection separation method (MTSM) based on the SRSM and the multitask deep learning network framework. MTSM consists of the SRSM-based seismic strong and weak reflection label automatic generation (SLG), the multitask two-stage seismic strong and weak reflection separation network (MTSN), and the energy balance loss function of MTSN. SLG aims to use SRSM and data augmentation to generate massive high-precision seismic strong and weak reflection labels, thereby providing sufficient high-precision training datasets for MTSN; MTSN aims to simultaneously output high-precision seismic strong and weak reflections, and the energy balance loss function of MTSN aims to address the imbalance between multiple loss functions resulting from the energy disparity between the seismic strong and weak reflections. An actual 3-D seismic dataset example demonstrates that MTSM has great potential as a technique for high-precision seismic strong and weak reflection separation.