Abstract: 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.

Key words: geological big data, big data mining, association rules, Apriori algorithm, abnormal element association