Application of Pattern Recognition Methods to Study Spatial Localization of Polymetallic Mineralization in the Altai–Sayan Region

IF 0.9 4区 地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS
A. I. Gorshkov, O. V. Novikova, A. I. Livinskii
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引用次数: 0

Abstract—The Altai–Sayan mountain-folded belt is analyzed with the purpose of (1) revealing peculiarities of localization of large-scale polymetallic mineralization in the lineament-block structure of the region and (2) determining the geophysical and geomorphic peculiarities of the locations of these deposits using the Cora-3 pattern recognition algorithm. The lineament-block structure of the region is determined using morphostructural zoning. A spatial correlation between large and superlarge polymetallic deposits and morphostructural nodes is revealed. Based on this correlation, a dichotomy problem is solved, which is to divide the entire set of nodes in the region into two classes—ore-bearing and non-ore bearing. For this purpose, we used the Cora-3 logical recognition algorithm with training, for which the input data are geomorphological and geophysical parameters of the nodes. The training set of the algorithm was composed of the nodes where large and superlarge polymetal deposits are known. At the training stage, the algorithm identified the sets of the characteristic features that are peculiar to each class. Based on these features, all the nodes in the region were divided into ore-bearing and non-ore-bearing ones. As a result of recognition, the nodes in which deposits of the considered types and sizes are known were classified as ore-bearing, and, in addition to them, another 11 nodes were identified that meet the features determined in the work and can be considered potentially ore-bearing.

Abstract Image

应用模式识别方法研究阿尔泰-萨彦地区多金属矿化的空间定位
摘要 对阿尔泰-萨彦山地褶皱带进行分析的目的是:(1) 揭示该地区线状块体结构中大规模多金属矿化位置的特殊性;(2) 利用 Cora-3 模式识别算法确定这些矿床位置的地球物理和地貌特殊性。该地区的线状块体结构是通过形态构造分区确定的。揭示了大型和超大型多金属矿床与形态构造节点之间的空间相关性。根据这种相关性,我们解决了一个二分法问题,即把该地区的所有节点分为两类--含矿和不含矿。为此,我们使用了带有训练的 Cora-3 逻辑识别算法,其输入数据为节点的地貌和地球物理参数。该算法的训练集由已知存在大型和超大型多金属矿床的节点组成。在训练阶段,该算法确定了每一类特有的特征集。根据这些特征,该区域的所有节点都被分为含矿和不含矿节点。识别的结果是,已知矿藏类型和规模的节点被归类为含矿节点,除此之外,还识别出另外 11 个节点,这些节点符合工作中确定的特征,可被视为潜在含矿节点。
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来源期刊
Izvestiya, Physics of the Solid Earth
Izvestiya, Physics of the Solid Earth 地学-地球化学与地球物理
CiteScore
1.60
自引率
30.00%
发文量
60
审稿时长
6-12 weeks
期刊介绍: Izvestiya, Physics of the Solid Earth is an international peer reviewed journal that publishes results of original theoretical and experimental research in relevant areas of the physics of the Earth''s interior and applied geophysics. The journal welcomes manuscripts from all countries in the English or Russian language.
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