3-D Basement Relief and Density Inversion Based on EfficientNetV2 Deep Learning Network

Gravity interface inversion is a critical technique in delineating the substructure of basins, providing essential technological and data support for oil and gas exploration. Traditional gravity inversion approaches often encounter issues such as suboptimal local solutions and limited resolution. Moreover, conventional deep learning inversion methods typically require extensive time for empirical parameter adjustment, hindering the achievement of optimal training outcomes. By utilizing Bouguer gravity anomaly data, this research pioneers the application of the EfficientNetV2 network in predicting 3-D basement relief interfaces and variations in overburden density. The network employs a composite scaling technique to adaptively adjust its width, depth, and input resolution, thereby identifying the most effective network configuration. Concurrently, the innovative Fused-MBconv convolutional module efficiently achieves superior results with a reduced number of network parameters. Specifically, in the Poyang Lake Basin study in Jiangxi Province, China, the EfficientNetV2 model demonstrated enhanced accuracy in predicting density variations of the basement interface and overlying strata compared to traditional methodologies.