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Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model
- Source :
- Lithosphere, Vol 2024, Iss 1 (2024)
- Publication Year :
- 2024
- Publisher :
- GeoScienceWorld, 2024.
-
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.
Details
- Language :
- English
- ISSN :
- 19418264 and 19474253
- Volume :
- 2024
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Lithosphere
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.58f51713c6443879264ac8f25b711fc
- Document Type :
- article
- Full Text :
- https://doi.org/10.2113/2024/lithosphere_2023_273/6249795/lithosphere_2023_273.pdf