| GPD建模及其在矿产预测中的应用 |
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| 引用本文： | 秦飞龙,刘剑,颜文勇.GPD建模及其在矿产预测中的应用[J].重庆大学学报(自然科学版),2020,43(4):64-72. |
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| 作者姓名： | 秦飞龙 刘剑 颜文勇 |
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| 作者单位： | 成都工业学院 大数据与人工智能学院, 成都 611730;电子科技大学 数学科学学院, 成都 611731,成都工业学院 汽车与交通学院, 成都 611730,成都工业学院 大数据与人工智能学院, 成都 611730 |
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| 基金项目： | 数学地质四川省重点实验室开放基金资助项目（scsxdz2018yb03）；四川省科技厅计划项目（2019YJ0375）；成都工业学院博士基金项目（2018RC022）；成都工业学院实验室开放基金项目（2018ZH10）。 |
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| 摘 要： | 探讨矿产资源预测方法。通过研究发现地球化学元素含量分布规律与广义帕累托分布(GPD)理论原理吻合,从而构建矿产资源预测GPD模型,在此基础上对模型的阈值和参数选取进行了探讨。将设计的GPD模型应用到实际矿区进行矿产预测,结果发现设计出的模型能够有效圈定出地球化学元素异常分布区域,圈定出的区域范围与矿体分布吻合,有利于圈定找矿靶区。
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| 关 键 词： | 矿产预测 地球化学异常 GPD 阈值 参数 |
| 收稿时间： | 2019/4/3 0:00:00 |
The study of GPD and its application in mineral prediction |
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| QIN Feilong,LIU Jian and YAN Wenyong.The study of GPD and its application in mineral prediction[J].Journal of Chongqing University(Natural Science Edition),2020,43(4):64-72. |
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| Authors: | QIN Feilong LIU Jian and YAN Wenyong |
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| Institution: | School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, P. R. China;School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China,School of Automobile and Communications, Chengdu Technological University, Chengdu 611730, P. R. China and School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, P. R. China |
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| Abstract: | This paper is aimed at exploring the method of mineral resources prediction. Though study we found that the anomalous distribution of geochemical elements was consistent with the generalized Pareto distribution (GPD) of extreme value theory, on the basis of which a new algorithm called the GPD model of geological anomaly was designed. In the new model, the parameters and threshold selection of GPD were discussed and proved. The proposed GPD model of the geological anomaly was applied to identify anomalies in the actual mining area. The results show that the model can effectively identify the geochemical anomaly region of the study area, and the delineated anomaly region coincides with the distribution of ore bodies of actual engineering exploration. The application of the GPD model will be instrumental to the delineation of new prospecting targets. |
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| Keywords: | mineral prediction geochemical anomaly GPD threshold parameters |
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