Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Abstract

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

RockDNet: Deep Learning Approach for Lithology Classification

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“…Methods based on DL predominantly approach the task of geological element interpretation from the perspective of semantic segmentation. Liu et al [37] proposed a framework that combined multi-source data fusion techniques with a fully convolutional network (FCN) model to enhance the accuracy of geological mapping. The results showcased the framework's efficacy in accurately identifying geological features, including lithological units.…”

Geological Background Prototype Learning-Enhanced Network for Remote-Sensing-Based Engineering Geological Lithology Interpretation in Highly Vegetated Areas

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