Application of convolution neural networks in gold exploration and prediction in Shandong Province
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摘要: 大数据和人工智能技术在矿产资源预测方面的应用已得到快速发展,但基于卷积神经网络机器学习技术的应用仍处于探讨和试验阶段,我国的矿产资源勘查预测实用化的例子和成果较少。针对上述问题,提出了将卷积神经网络应用到金矿勘查中,根据山东省某地金矿成矿区域的3×104 km2范围内的地质、矿产、地球物理、地球化学等实测专业数据资料,进行了2 000轮卷积神经网络的训练,最终得到了准确率0.95、损失率0.11的一维卷积神经网络模型。将这套卷积神经网络用于山东省其他未知地区,进行金矿床分布位置(勘查靶区)的预测试验,得到了较好的结果。Abstract: Rapid progress has been made in the application of big data and artificial intelligence technology in the prediction of mineral resources. However, the application of machine learning technology based on convolutional neural networks remains in the exploration and experimental stages, with few practical examples and accomplishments achieved in the exploration and prediction of mineral resources in China. This study proposed applying convolutional neural networks to the exploration of gold deposits. Specifically, a neural network was trained for 2000 rounds using measured geological, mineral, geophysical, and geochemical data collected from a mineralization region covering an area of 3×104 km2 in a gold deposit in Shandong Province. Consequently, a 1D convolutional neural network model with accuracy of 0.95 and a loss rate of 0.11 was obtained. This model was employed to predict the distribution locations of gold deposits (exploration target areas) in other unknown areas in Shandong Province, yielding encouraging outcomes.
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Key words:
- convolutional neural network /
- machine learning /
- gold exploration
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