Horizon Picking Using AWFF-MT-LSTM Network with the Aid of Geological Simulation Modeling

Seismic horizon picking is vital in seismic interpretation, forming the foundation for reservoir exploration and seismic inversion. Traditional horizon picking methods heavily depend on geologists' experience, making the process time-consuming, labor-intensive, and prone to subjective interpretation. The emergence of deep learning (DL) offers new solutions for horizon picking. However, conventional DL models often struggle to segment seismic horizons accurately when faced with limited training samples and low-quality labels. To address these issues, we suggest a geological simulation modeling approach to generate synthetic datasets with field features for model training. Afterward, we suggest an adaptive weighted feature fusion multitask long short-term memory (AWFF-MT-LSTM) network, which treats horizon picking as two tasks, i.e., stratigraphic boundary segmentation and target waveform detection. Moreover, we propose the AWFF layer that balances the relationship between two tasks and shares feature information across two tasks to enhance model convergence speed. Finally, we apply our methods to field data to validate their feasibility.