An interpretation-based convolution neural network framework for geophysical data fusion and aquifer structure identification

Abstract

Identification of 3D realistic aquifer structures is essential for predicting physicochemical processes in groundwater systems. However, the characterization of highly heterogeneous aquifers remains challenging because it relies on the effective fusion of multiple geophysical data sources having wide areal coverage, as well as downhole geophysical data featuring high resolution. This study establishes a novel 3D convolutional neural network model to generate aquifer structure from 3D seismic data, constrained by sparse downhole sonic and lithology logs. In the model, the data fusion procedure is designed to follow the logics of conventional manual interpretation of multiple geophysical data, and to address the 3D spatial relationships between geophysical data and lithology. The method is implemented in a typical fluvial aquifer featuring coarse paleovalley sediments (sandstone) embedded in the tight surrounding rocks (claystone), in order to identify channelized sandstone from low-permeability claystone. It is confirmed that the proposed model reliably generates 3D aquifer structures based on seismic amplitudes, downhole sonic and lithology logs. The method is compared to traditional machine learning models that focus on 1D conversion from geophysical attributes to lithology. The results show that the newly-developed model performs more robustly and accurately because the use of 3D convolution allows considering the relationships between seismic amplitude, sonic velocity and lithology in both vertical and horizontal directions. Moreover, the inclusion of sonic logs constraint in the model, following the logics of manual seismic data interpretation, significantly improves the model accuracy. The method can find broad applications for the characterization of subsurface heterogeneity even featuring non-gaussian permeability distribution like the demonstrated fluvial aquifer.