Citation: Zhang, B.; Jiang, Z.; Chen,
Y.; Cheng, N.; Khan, U.; Deng, J.
Geochemical Association Rules of
Elements Mined Using Clustered
Events of Spatial Autocorrelation: A
Case Study in the Chahanwusu River
Area, Qinghai Province, China. Appl.
Sci. 2022, 12, 2247. https://doi.org/
10.3390/app12042247
Academic Editor: Sławomir
Nowaczyk
Received: 20 January 2022
Accepted: 19 February 2022
Published: 21 February 2022
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Article
Geochemical Association Rules of Elements Mined Using
Clustered Events of Spatial Autocorrelation: A Case Study in
the Chahanwusu River Area, Qinghai Province, China
Baoyi Zhang
1,2
, Zhengwen Jiang
2
, Yiru Chen
2
, Nanwei Cheng
2
, Umair Khan
2
and Jiqiu Deng
1,2,
*
1
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment
Monitoring (Ministry of Education), Central South University, Changsha 410083, China;
zhangbaoyi@csu.edu.cn
2
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
jiangzhengwen@csu.edu.cn (Z.J.); 195011096@csu.edu.cn (Y.C.); 215011050@csu.edu.cn (N.C.);
umair77@csu.edu.cn (U.K.)
* Correspondence: csugis@csu.edu.cn; Tel.: +86-731-88877676
Abstract:
The spatial distribution of elements can be regarded as a numerical field of concentration
values with a continuous spatial coverage. An active area of research is to discover geologically
meaningful relationships among elements from their spatial distribution. To solve this problem, we
proposed an association rule mining method based on clustered events of spatial autocorrelation
and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China.
The elemental data for stream sediments were first clustered into HH (high–high), LL (low–low),
HL (high–low), and LH (low–high) groups by using local Moran’s I clustering map (LMIC). Then, the
Apriori algorithm was used to mine the association rules among different elements in these clusters.
More than 86% of the mined rule points are located within 1000 m of faults and near known ore
occurrences and occur in the upper reaches of the stream and catchment areas. In addition, we found
that the Middle Triassic granodiorite is enriched in sulfophile elements, e.g., Zn, Ag, and Cd, and the
Early Permian granite quartz diorite (P
1
γδ
o) coexists with Cu and associated elements. Therefore, the
proposed algorithm is an effective method for mining coexistence patterns of elements and provides
an insight into their enrichment mechanisms.
Keywords:
concentration field; spatial autocorrelation; association rules; Apriori algorithm;
element co-occurrence
1. Introduction
Spatial autocorrelation analysis focuses on the similarity of attributes, as well as spatial
similarity between one geological entity and adjacent entities. The spatial distribution of
concentrations of elements can be regarded as a numerical field with a continued spatial
coverage, which can be characterized by using spatial autocorrelation among different
elements. Korobova and Romanov (2009) stressed that the nonrandom characteristics
and spatial structure of geochemical data depend on the concentration field [
1
]. Analysis
of the concentration field includes comparison of samples to recognize anomalies and
using the spatial correlation among elements to explain geochemical processes. Geological
interactions between elements result in mutual influence and restriction. Therefore, it
is necessary to consider spatial auto- and cross correlation in geochemical studies. The
concentrations and spatial association of different elements are usually related to parent
lithostrata. Therefore, it is of great significance to study the distribution, enrichment,
and relationships among different elements to understand regional magmatism and ore-
forming process [2].
Tobler (1970) proposed the first law of geography: everything is related to everything
else, but near things are more related than distant things [
3
]. The measurement of spatial
Appl. Sci. 2022, 12, 2247. https://doi.org/10.3390/app12042247 https://www.mdpi.com/journal/applsci
