Abstract

There are many challenges in the task of predicting ore deposits from big data repositories. The data are inherently complex and of great significance to the intervenient spatial relevance of deposits. The characteristics of the data make it difficult to use machine learning algorithms for the quantitative prediction of mineral resources. There are considerable interest and value in extracting spatial distribution characteristics from two-dimensional (2-d) ore-controlling factor layers under different metallogenic conditions. In this paper we undertake such analysis using a deep convolutional neural network algorithm named AlexNet. Training on the 2-d mineral prediction and classification model is performed using data from the Songtao–Huayuan sedimentary manganese deposit. It mines the coupling correlation between the spatial distribution of chemical elements, sedimentary facies, the outcrop of Datangpo Formation, faults, water system, and the areas where manganese ore bodies are present, as well as the correlation among different ore-controlling factors by employing the AlexNet networks. By comparing the training loss, training accuracy, verification accuracy, and recall of models trained by different scales of grids and different combinations of ore-controlling factor layers, we further discuss the most appropriate scale division and the optimal combination of ore-controlling factors to make the model achieve its strongest robustness. It is found that the prediction performance of AlexNet networks reaches its peak when selecting a grid division of 200 pixels × 200 pixels (the actual distance is 10 km × 10 km) and inputting the distribution layers of 21 chemical elements maps, lithofacies–paleogeographic map, formation and tectonic map, outcrop map of Datangpo Formation, and water system map. The training loss, training accuracy, verification accuracy, and recall of the optimal model are 0.0000001, 100.00%, 86.21%, and 91.67%, respectively. The proposed method is successfully applied to the 2-d metallogenic prediction in Songtao–Huayuan study area. And five metallogenic prospective areas from A to E are delineated with large probability for potential ore bodies.

中文翻译：

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深卷积神经网络在二维地质大数据勘探预测中的应用

摘要

从大数据存储库预测矿石储量的任务有很多挑战。数据天生就是复杂的，对于矿床之间的空间相关性具有重要意义。数据的特征使得难以使用机器学习算法进行矿产资源的定量预测。在不同成矿条件下从二维（2-d）控矿因素层中提取空间分布特征具有重大的意义和价值。在本文中，我们使用名为AlexNet的深度卷积神经网络算法进行了此类分析。利用松桃-华远沉积锰矿床的数据进行了二维矿物预测和分类模型的训练。运用化学方法挖掘了化学元素的空间分布，沉积相，大塘坡组露头，断层，水系与锰矿体存在区域之间的耦合相关性，以及不同控矿因素之间的相关性。 AlexNet网络。通过比较训练损失，训练准确性，验证准确性和由不同网格规模和控矿因素层的不同组合训练的模型的召回率，我们进一步讨论了最合适的规模划分和控矿因素的最佳组合以使模型达到最强的鲁棒性。发现选择200像素×200像素（实际距离为10 km×10 km）的网格划分并输入21个化学元素图，岩相-古地理图的分布层时，AlexNet网络的预测性能达到峰值。 ，地层和构造图，大唐坡地层露头图和水系图。最优模型的训练损失，训练准确性，验证准确性和召回率分别为0.0000001、100.00％，86.21％和91.67％。所提出的方法已成功应用于松桃—华远研究区的二维成矿预测。并划定了从A到E的五个成矿远景区，很有可能形成潜在的矿体。构造和构造图，大唐坡组露头图和水系图。最优模型的训练损失，训练准确性，验证准确性和召回率分别为0.0000001、100.00％，86.21％和91.67％。所提出的方法已成功应用于松桃—华远研究区的二维成矿预测。并划定了从A到E的五个成矿远景区，很有可能形成潜在的矿体。构造和构造图，大唐坡组露头图和水系图。最优模型的训练损失，训练准确性，验证准确性和召回率分别为0.0000001、100.00％，86.21％和91.67％。所提出的方法已成功应用于松桃—华远研究区的二维成矿预测。并划定了从A到E的五个成矿远景区，很有可能形成潜在的矿体。