InverMulT-STP: Closed-Loop Transformer Seismic AVA Inversion With Synthetic Data Style Transfer Pretraining

Prestack seismic data amplitude variation with angle (AVA) inversion is critical in identifying oil and gas reservoirs. Recently, deep learning (DL) has gained significant popularity in AVA multiparameter inversion, often employing 1-D network on a trace-by-trace basis. However, the limited availability of well-log data (label) and the inability of 1-D network to capture spatial features result in predictions with inadequate lateral stability and structural consistency. At the same time, the adoption of multiple single-task learning strategy leads to low efficiency and weak generalization in multiparameter inversion. To address these challenges, we propose InverMulT-STP, a 2-D multitask closed-loop Transformer with synthetic data style transfer pretraining for prestack AVA inversion. The proposed semi-supervised learning (SSL) paradigm combines multitask learning (MTL) and transfer learning strategies to improve prestack AVA inversion. Specifically, the multitask closed-loop Transformer (InverMulT) utilizes a Transformer-based encoder-decoder architecture to extract comprehensive spatial structure information from angle gathers, enabling simultaneous and accurate multiparameter inversion. We enhance the cycle-consistency loss and introduce learned perceptual image patch similarity (LPIPS) as the criterion to achieve more detailed structures in the inversion results. To make balance on the inversion of different parameters, an adaptive weight update method (AWUM) is introduced for the weighting of each task in loss functions. To improve generalization on field data, neural style transfer (NST) is employed to generate the realistic synthetic dataset for network pretraining. From both synthetic and field data experiments, the inversion results show that the proposed method outperforms several existing DL inversion methods and commercial software.