Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model

Abstract

Lithostratigraphic modeling holds a vital role in mineral resource exploration and geological studies. In this study, we introduce a novel approach for automating pseudo-lithostratigraphic modeling in the deep subsurface, leveraging inversed geophysical properties. We propose a three-dimensional convolutional neural network with adaptive moment estimation (3D Adam-CNN) to achieve this objective. Our model employs 3D geophysical properties as input features for training, concurrently reconstructing a 3D geological model of the shallow subsurface for lithostratigraphic labeling purposes. To enhance the accuracy of pseudo-lithostratigraphic modeling during the model training phase, we redesign the 3D CNN framework, fine-tuning its parameters using the Adam optimizer. The Adam optimizer ensures controlled parameter updates with minimal memory overhead, rendering it particularly well-suited for convolutional learning involving huge 3D datasets with multi-dimensional features. To validate our proposed 3D Adam-CNN model, we compare the performance of our approach with 1D and 2D CNN models in the Qingniandian area of Heilongjiang Province, Northeastern China. By cross-matching the model’s predictions with manually modeled shallow subsurface lithostratigraphic distributions, we substantiate its reliability and accuracy. The 3D Adam-CNN model emerges as a robust and effective solution for lithostratigraphic modeling in the deep subsurface, utilizing geophysical properties.