Mineral potential mapping with a restricted Boltzmann machine
Mineral potential mapping with a restricted Boltzmann machine
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DOI：
10.1016/j.oregeorev.2014.08.012
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发表时间：
2015-12
影响因子：
3.3
通讯作者：
Yongliang Chen
中科院分区：
文献类型：
--
作者：
Yongliang Chen
受限玻尔兹曼机可以被训练为从具有未知复杂概率分布的训练样本群体中编码和重建训练样本。由于小概率样本的编码和重构误差相对较大，因此可以将其与训练样本群体区分开来。在矿产潜力测绘中，矿产潜力区域通常只占整个矿产勘查区域的一小部分，并且具有特定的证据图模式。假设整个矿产勘探区域被划分为均匀的单元，然后在所有单元上训练受限玻尔兹曼机。作为总训练细胞的一小部分，矿物电位细胞与非矿物电位细胞相比具有更大的编码和重建误差。基于训练单元的编码和重构误差，ASC和ASE被定义为两个矿产潜力指标，用于利用训练好的受限玻尔兹曼机进行矿产潜力映射。中国北疆阿勒泰地区地质环境复杂，被选为案例研究区。在研究区域的 9582 个训练单元上构建并训练具有 12 个可见单元和不同隐藏单元的受限玻尔兹曼机。采用ASC、ASE、后验概率和含矿概率来预测研究区各网格单元的矿产潜力。 AUC（曲线下面积）是机器学习中聚合分类性能的度量，用于衡量这四个矿产潜力指标的矿产潜力绘图性能。结果表明，四种矿产潜力指标表现相似。因此，可以训练受限玻尔兹曼机来绘制矿产潜力图，ASC和ASE是利用训练好的受限玻尔兹曼机进行矿产潜力图绘制的两个可行指标。
A restricted Boltzmann machine can be trained to encode and reconstruct training samples from a training sample population with an unknown complex probability distribution. Small probability samples can be differentiated from the training sample population due to their comparatively larger encoded and reconstructed errors. In mineral potential mapping, mineral potential areas usually take up only a small proportion of the whole mineral exploration region and have specific evidence map patterns. Assume that the whole mineral exploration region is divided into uniform cells and a restricted Boltzmann machine is then trained on all the cells. As a small proportion of the total training cells, mineral potential cells have much larger encoded and reconstructed errors compared to non-mineral potential cells. Based on the encoded and reconstructed errors of the training cells,ASCandASEare thus defined as two mineral potential indicators for mineral potential mapping with the trained restricted Boltzmann machine. The Altay district in northern Xinjiang in China, which is linked with a complex geological setting, is chosen as a case study area. Restricted Boltzmann machines with 12 visible units and differing hidden units are constructed and trained on 9582 training cells in the study area. TheASC,ASE, posterior probability, and deposit-bearing probability are used to predict the mineral potential of each grid cell in the study area. TheAUC(area under the curve), a measure of aggregated classification performance in machine learning, is applied to measure the mineral potential mapping performance of these four mineral potential indicators. The results show that the four mineral potential indicators perform similarly well. Therefore, a restricted Boltzmann machine can be trained to map mineral potentials, and theASCandASEare two feasible indicators for the mineral potential mapping with the trained restricted Boltzmann machine.