Groundwater Mapping and Modeling Using Towed Transient Electromagnetic Data Based on Deep Learning

The capturing subsurface structure through geophysical measurements can gain a more comprehensive understanding of groundwater distribution. While geophysical electromagnetic methods yield subsurface resistivity data, converting this into hydrological information is not straightforward. Well-logging offers insights into rock strata vertically but lacks spatial detail on large-scale lithological variations. Consequently, merging geophysical and well-logging data for extensive hydrogeological modeling has emerged as a crucial research area. In this study, we introduce convolutional neural networks and bi-directional long short-term memory (CNNs-BiLSTM) network to process massive towed transient electromagnetic (tTEM) datasets. Our network incorporates the depth-of-investigation (DOI) and smooth constraints for effective tTEM data inversion. We further validate the network's effectiveness and generalization capacity using synthetic models and real tTEM data from Switzerland's Aare Valley region. Furthermore, by combining the limited well-logging data, we establish a spatial clay content distribution model using an optimal inversion interpolation method. Leveraging this lithology model, we employ the groundwater modeling system (GMS) platform to determine regional groundwater levels. Our numerical simulation aligns closely with results obtained via the top of the saturated zone (TSZ) method and exhibits strong agreement with observed water table data, affirming the reliability of our comprehensive hydrogeological model. Our proposed method and workflow present an innovative approach to effective hydrological modeling utilizing large-scale geophysical electromagnetic data.