[A-HW17] Near Surface Investigation and Modeling for Groundwater Resources Assessment and Conservation

[AHW17-03] Applying Deep Learning for Unconsolidated Sediment Classification: An Exploratory Study

*Chun-Wei Huang¹, Si Ying - Yau², Sheng-Sheng Huang³ (1.Ming Chi University of Technology, General Education Center, 2.National Taiwan University, Department of Geography, 3.Ming Chi University of Technology, Department of Electronic Engineering)

Keywords:Hydrogeology, Groundwater, Artificial Intelligence

Hydrogeological exploration forms the basis for comprehending groundwater distribution. However, manually identifying hydrogeological conditions is both time-consuming and subjective. This study delves into the potential of 1-D Convolutional Neural Networks (1-D CNNs) to capture the geophysical features of unconsolidated sediments. We utilized geo-big data, including Electrical Resistivity (ER), Self-Potential (SP), and Gamma Ray (GR) data in Taiwan. The results demonstrated that our CNN-based model achieved an overall accuracy of 83.14% in identifying gravel, sand, and clay. Nevertheless, the precision and recall in identifying sand need improvement due to limited support in sample size.