Characterizing Subsurface Structures From Hard and Soft Data With Multiple-Condition Fusion Neural Network

Abstract

Accurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep-learning-based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non-linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple-condition fusion network (MCF-Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple-source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple-condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF-Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF-Net in applications of hydrogeological modeling.