从钻探数据到地球物理数据的矿产勘探领域调整

Youngjae Shin
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摘要

这项研究利用领域适应性来加强对不同地球科学数据集的整合,旨在改进矿体的识别。传统的矿产勘探方法在合并不同的地球科学数据类型时经常面临挑战,这导致模型在不同领域中表现不佳。领域适应是一种深度学习策略,旨在调整在一个领域(源)中开发的模型,使其在不同领域(目标)中表现良好。为了使在详细的、有标签的钻井数据(源)上训练的模型能够解释更广泛的、无标签的地球物理数据(目标),我们应用了领域对抗神经网络(DANNs),选择它们是因为它们在目标领域不提供标签的情况下具有强大的性能。这种方法通过被识别为候选矿石的区域与被标记为寄主岩的钻孔位置之间的最小重叠得到间接验证,t-分布式随机邻域嵌入(t-SNE)可视化提供了定性验证,显示出跨域数据整合的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Domain Adaptation from Drilling to Geophysical Data for Mineral Exploration
This study utilizes domain adaptation to enhance the integration of diverse geoscience datasets, aiming to improve the identification of ore bodies. Traditional mineral exploration methods often face challenges in merging different geoscience data types, which leads to models that do not perform well across varying domains. Domain adaptation is a deep learning strategy aimed at adapting a model developed in one domain (source) to perform well in a different domain (target). To adapt models trained on detailed, labeled drilling data (source) to interpret broader, unlabeled geophysical data (target), Domain-Adversarial Neural Networks (DANNs) were applied, chosen for their robust performance in scenarios where the target domain does not provide labels. This approach was indirectly validated through the minimal overlap between regions identified as candidate ore and borehole locations marked as host rocks, with qualitative validation provided by t-Distributed Stochastic Neighbor Embedding (t-SNE) visualizations showing improved data integration across domains.
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