Evolution Inversion: Co-Evolution of Model and Data for Seismic Reservoir Parameters Inversion

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-18
• Main Authors: Song, Cao, Lu, Minghui, Lu, Wenkai, Geng, Weiheng, Li, Yinshuo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolution; Data models; Deep learning (DL); elastic parameters; evolution; Feature extraction; Mathematical models; Noise reduction; physical parameters; Reservoirs; seismic inversion
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3440480

Seismic inversion is a critical research area in seismic data interpretation. Given the powerful feature extraction and representation capabilities of deep neural network (DNN), it has been widely adopted in the seismic reservoir parameters inversion. However, the majority of DNN-based inversion methods use 1-D models due to the scarcity of well-logging labels, which are only 1-D time series. The performance of higher-dimensional DNN-based inversion methods depends on the quality of the initial inversion results, leading to an interdependence between the model and data in the time and space dimensions. Here, we propose a model and data co-evolution method for seismic reservoir parameters inversion. It employs a 1-D DNN model-based closed-loop model to generate initial reservoir inversion results. Then, the evolutionary 2-D model learns spatial structural features constrained by the initial reservoir inversion results to improve the spatial continuity. We tested the proposed method on synthetic seismic data with multiple fault structures, achieving the lowest inversion error and highest inversion accuracy. It also exhibits the highest accuracy in real seismic data with the structural features of underground rivers being more pronounced.