关联规则算法在粤西庞西垌地区元素异常组合研究中的应用
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摘要
以粤西庞西垌矿床远景区1∶5万水系沉积物地球化学测量及异常查证数据为基础,应用Python编程语言开展关联规则算法的应用案例研究。结果显示,Apriori算法可以有效挖掘元素组合之间的关联规则数据集。当Au、Cu、Sb在异常值范围内时,出现As为异常的可能性是95.5%。Apriori算法挖掘的关联规则符合实际,组合异常的强规则与研究区已知矿化地段的异常组合重合性较高。可以推论,面对海量的地球化学数据,逐个进行元素分析较为耗时而且无法观测到元素之间的关系,通过关联规则算法找出元素异常组合规律的办法,使之最大限度地保留元素之间的相关信息,可以用来寻找隐藏的元素组合以及其中的潜在相关性。未来构建指示找矿的成矿关联规则数据库并进行矿床预测,将比运用传统的方法更加便捷。
We conducted a case study on the application of association rule algorithm(programmed in Python)using the original 1∶50000 geochemical survey and anomaly verification data of stream sediments in the Pangxidong deposit prospect district in the southern section of the Qinzhou Bay-Hangzhou Bay orogenic belt.The results showed that the Apriori algorithm can effectively mine the association rule itemsets of elemental combinations.For example,we found that As had a 95.5% probability being abnormal when Au,Cu and Sb in the itemset were in abnormal range.The association rules selected by Apriori algorithm were in line with survey results;and the strong rules of combination anomalies had high agreement with the anomaly combinations of known mineral deposits in the study area.Facing with massive geochemical survey data,it is often time-consuming to understand elements one by one,and in many cases it is impossible to observe the relationship among them.Therefore,it is advantageous to expose the abnormal combination rules of elements using association rule algorithm.By doing so,related information among various elements can be stored to a great extent and used to find the hidden combinations of elements and potential correlations among them.And compared to traditional methods,it can be more convenient and effective to establish data bases of metallogenic association rules and to carry out mineral deposit prediction.
We conducted a case study on the application of association rule algorithm(programmed in Python)using the original 1∶50000 geochemical survey and anomaly verification data of stream sediments in the Pangxidong deposit prospect district in the southern section of the Qinzhou Bay-Hangzhou Bay orogenic belt.The results showed that the Apriori algorithm can effectively mine the association rule itemsets of elemental combinations.For example,we found that As had a 95.5% probability being abnormal when Au,Cu and Sb in the itemset were in abnormal range.The association rules selected by Apriori algorithm were in line with survey results;and the strong rules of combination anomalies had high agreement with the anomaly combinations of known mineral deposits in the study area.Facing with massive geochemical survey data,it is often time-consuming to understand elements one by one,and in many cases it is impossible to observe the relationship among them.Therefore,it is advantageous to expose the abnormal combination rules of elements using association rule algorithm.By doing so,related information among various elements can be stored to a great extent and used to find the hidden combinations of elements and potential correlations among them.And compared to traditional methods,it can be more convenient and effective to establish data bases of metallogenic association rules and to carry out mineral deposit prediction.
引文
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[11]周永章,李兴远,郑义,等.钦-杭结合带成矿地质背景及成矿规律[J].岩石学报,2017,33(3):667-681.
[2]张旗,周永章.大数据助地质腾飞:2018第11期大数据专题“序”[J].岩石学报,2018,34(11):3167-3172.
[3]周永章,王俊,左仁广,等.机器学习、深度学习及Python语言[J].岩石学报,2018,34(11):3173-3178.
[4] MAYER-SCHONBERGER V,CUKIER K.Big data:a revolution that will transform how we live,work,and think[M].Hangzhou:Zhejiang People's Publishing House,2013:1-261.
[5] AGRAWAL R,IMIELINSKI T,SWAMI A.Mining association rules between sets of items in large databases[J].ACM SIGMOD Record,1993,22(2):207-216.
[6]郭涛,张代远.基于关联规则数据挖掘Apriori算法的研究与应用[J].计算机技术与发展,2011,21(6):101-103,107.
[7]常力恒,朱月琴,张戈一,等.面向矿产资源信息的空间关联性分析[J].岩石学报,2018,34(2):314-318.
[8]周永章,张良均,张奥多,等.地球科学大数据挖掘与机器学习[M].广州:中山大学出版社,2018:1-360.
[9]周永章,陈烁,张旗,等.大数据与数学地球科学研究进展:大数据与数学地球科学专题代序[J].岩石学报,2018,34(2):256-263.
[10]梁锦,周永章,李红中,等.钦-杭结合带斑岩型铜矿的基本地质特征及成因分析[J].岩石学报,2012,28(10):3361-3372.
[11]周永章,李兴远,郑义,等.钦-杭结合带成矿地质背景及成矿规律[J].岩石学报,2017,33(3):667-681.