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

Ziye Wang,Renguang Zuo,Hao Liu

doi:10.3390/rs13234860

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.

Highlights

IntroductionPublished: 30 November 2021As a fundamental work in mineral exploration, geological mapping plays a crucial role by enabling the detection of geological features that involve lithological units and alterations
As a fundamental work in mineral exploration, geological mapping plays a crucial role by enabling the detection of geological features that involve lithological units and alterations
This study introduced an fully convolutional network (FCN)-8s model to promote the discrimination of highly similar lithological units, which can provide sufficient training samples and make it less expensive computationally than the patch-based approach; this is because no redundant operations and repeated calculations need to be performed on neighboring patches

Summary

Introduction

Published: 30 November 2021As a fundamental work in mineral exploration, geological mapping plays a crucial role by enabling the detection of geological features that involve lithological units and alterations. Several approaches have been applied for geological mapping to assist in the discovery of mineral deposits based on geological, geophysical, geochemical, and remote sensing data These methods can be summarized as traditional field surveys, statistical processes, and more recent machine learning technologies (e.g., random forest, support vector machine, and logistic regression) [1,2,3,4]. Wang et al [5] designed a hybrid method that comprises random forest and metric learning to delineate the spatial distribution of Himalayan leucogranites based on remote sensing images and geochemical data. This approach integrated the advantages of both geochemical and remote sensing, which is helpful for improving the recognition accuracy of Himalayan leucogranite. Deep learning algorithms are dominant in dealing with high-dimensional datasets for classification and prediction

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