Dictionary learning for integration of evidential layers for mineral prospectivity modeling
Dictionary learning for integration of evidential layers for mineral prospectivity modeling
复制标题
用于集成证据层以进行矿物前景建模的字典学习
DOI：
10.1016/j.oregeorev.2021.104649
复制
发表时间：
2021
影响因子：
3.3
通讯作者：
Sui Yanhui
中科院分区：
文献类型：
--
作者：
Chen Yongliang;Sui Yanhui
机器学习和深度学习异常检测器已成功地用于绘制矿产远景图。然而，建立用于矿产勘查制图的机器学习或深度学习异常检测模型往往需要在缺乏地面实况数据的情况下确定一组初始化参数。不适当的初始化参数会降低这些矿产勘查填图模型的性能。与大多数机器学习和深度学习算法不同，词典学习是一种只涉及线性变换的白盒算法，构建词典学习模型只需要一个原子总数的经验定义。为此，本文基于最小角度回归-Lasso(LARS-Lasso)算法和迭代收缩阈值算法(ISTA)，建立了两种字典学习异常检测器，用于矿产勘查建模。提出了使用字典学习技术进行矿产勘查建模的五步步骤：(A)基于输入数据构建过完备字典；(B)根据过完备字典将每个数据点转换为稀疏系数；(C)基于过完备字典和稀疏系数计算每个数据点的稀疏表示；以及(D)计算每个数据点与其稀疏表示之间的差的欧几里得范数，并将其用作数据点的矿产潜力。建立词典学习模型，对内蒙古中国金厂沟梁地区金矿找矿前景进行建模，并与Logistic回归模型和一类支持向量机模型在金矿找矿目标中的预测效果进行比较。结果表明：(A)字典学习模型的性能不低于Logistic回归(LGR)模型，优于一类支持向量机(OCSVM)模型；(B)所建立的模型划分的金矿远景区与研究区的地质成矿特征具有较强的一致性。因此，词典学习算法是一种高性能的矿产勘查建模技术。针对不同区域不同类型矿床的词典学习算法在矿产勘查目标中的有效性值得进一步检验。
Machine learning and deep learning anomaly detectors have been successfully used to map mineral prospectivity. However, the establishment of machine learning or deep learning anomaly detection models for mineral prospectivity mapping often requires the determination of a set of initialization parameters in the absence of ground truth data. Improper initialization parameters will degrade the performance of these mineral prospectivity mapping models. Different from most machine learning and deep learning algorithms, dictionary learning is a “white-box” algorithm involving only linear transformations, and building a dictionary learning model requires only an empirical definition of the total number of atoms. Therefore, in this paper, two dictionary learning anomaly detectors were established for mineral prospectivity modeling based on the least angle regression-Lasso (LARS-Lasso) algorithm and the iterative shrinkage-thresholding algorithm (ISTA). The following five-step procedure was proposed for mineral prospectivity modeling using the dictionary learning techniques: (a) an overcomplete dictionary is constructed based on the input data; (b) each data point is transformed into sparse coefficients according to the overcomplete dictionary; (c) the sparse representation of each data point is calculated based on the overcomplete dictionary and the sparse coefficients; and (d) the Euclidean norm of the difference between each data point and its sparse representation is calculated and used as the mineral potential of the data point. The dictionary learning models were established to model gold prospectivity in the Jinchanggouliang district, Inner Mongolia, China, and compared with the logistic regression model and one-class support vector machine model in gold exploration targeting. The result shows that (a) the performances of the dictionary learning models are no less than that of the logistic regression (LGR) model and better than that of the one-class support vector machine (OCSVM) model, and (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. Therefore, the dictionary learning algorithms are high-performance mineral prospectivity modeling techniques. It is worth to further test the effectiveness of the dictionary learning algorithms for different types of mineral deposits in different areas in mineral exploration targeting.