Geological anomaly extraction based on neighborhood constraint clustering

YANG Zhaoying; FENG Lei; JIANG Decai; ZHU Yueqin; YU Xianchuan

2019 Vol. 38, No. 12

Article Contents

Article navigation > Geological Bulletin of China > 2019 > 38(12): 2077-2084

Next Article Previous Article

YANG Zhaoying, FENG Lei, JIANG Decai, ZHU Yueqin, YU Xianchuan. Geological anomaly extraction based on neighborhood constraint clustering[J]. Geological Bulletin of China, 2019, 38(12): 2077-2084.

Citation:

YANG Zhaoying, FENG Lei, JIANG Decai, ZHU Yueqin, YU Xianchuan. Geological anomaly extraction based on neighborhood constraint clustering[J]. Geological Bulletin of China, 2019, 38(12): 2077-2084.

Geological anomaly extraction based on neighborhood constraint clustering

1.
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
2.
Key Laboratory of Geological Information Technology, Ministry of Natural Resources, Beijing 100037, China
3.
Development and Research Center, China Geological Survey, Beijing 100037, China
4.
Academy of Information Science and Technology, Beijing Normal University, Beijing 100875, China

More Information

Corresponding author: FENG Lei, fl@agrs.cn

Received Date: 23 April 2019

Revised Date: 16 July 2019

Publish Date: 15 December 2019

Abstract

Abstract

Geochemical anomalies often have a strong correlation with ore deposits. The study of effective methods for extracting geochemical anomalies is of great significance for prospecting. The advent of the era of big data and artificial intelligence poses new challenges for the extraction of geochemical anomalies that are automatic and independent of expert knowledge. Geostatistical research shows that it is a new geochemical prospecting idea to identify geochemical anomalies by identifying the special spatial forms of geochemical anomalies, such as lattices, bands, and rings. By analyzing the element value attribute and spatial position of geochemical data, this paper proposes a method based on neighborhood constrained clustering. After clustering geochemical elements, it can extract special shapes such as rectangle, ring and semi-ring and extract geochemical anomalies. In this paper, the geochemical data of two experimental areas in the Xiaoshan area of Henan Province were selected for experiments. The results of Experiment 1 show that the position of the rectangle appears consistent with the location of the known tungsten ore site, whereas the results of Experiment 2 show that the position of the ring is consistent with the location of the known copper orebody. The experiment proves the effectiveness of the method based on neighborhood constrained clustering in extracting geochemical anomalies.
- geochemical anomaly /
- neighborhood constraint /
- clustering

FullText(HTML)

References

[1]	Harris J R, Wilkinson L, Grunsky E C. Effective use and interpretation of lithogeochemical data in regional mineral exploration programs:application of Geographic Information Systems (GIS) technology[J]. Ore Geology Reviews, 2000, 16(3/4):107-143. Google Scholar
[2]	Gałuszka A. A review of geochemical background concepts and an example using data from Poland[J]. Environmental Geology, 2007, 52(5):861-870. doi: 10.1007/s00254-006-0528-2 CrossRef Google Scholar
[3]	Wackemagel H. Multivariate geostatistics:an introduction with applications[M]. Springer, 2003. Google Scholar
[4]	Grunsky E C, Agterberg F P. Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario[J]. Mathematical Geology, 1988, 20(7):825-861. doi: 10.1007/BF00890195 CrossRef Google Scholar
[5]	Cheng Q M. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China[J]. Ore Geology Reviews, 2007, 32(1/2):314-324. Google Scholar
[6]	Zuo R G, Xiong Y H. Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods[J]. Natural Resources Research, 2018, 27(1):5-13. doi: 10.1007/s11053-017-9357-0 CrossRef Google Scholar
[7]	Chen Y L. Mineral potential mapping with a restricted Boltzmann machine[J]. Ore Geology Reviews, 2015, 71:749-760. doi: 10.1016/j.oregeorev.2014.08.012 CrossRef Google Scholar
[8]	Zuo R G. Machine learning of mineralization-related geochemical anomalies:A review of potential methods[J]. Natural Resources Research, 2017, 26(4):457-464. doi: 10.1007/s11053-017-9345-4 CrossRef Google Scholar
[9]	Cheng Q M, Agterberg F P, Ballantyne S B. The separation of geochemical anomalies from background by fractal methods[J]. Journal of Geochemical Exploration, 1994, 51(2):109-130. doi: 10.1016/0375-6742(94)90013-2 CrossRef Google Scholar
[10]	Carranza E J M. Geochemical anomaly and mineral prospectivity mapping in GIS[M]. Elsevier, 2008. Google Scholar
[11]	Cheng Q M, Xu Y G, Grunsky E. Integrated spatial and spectrum method for geochemical anomaly separation[J]. Natural Resources Research, 2000, 9(1):43-52. doi: 10.1023/A:1010109829861 CrossRef Google Scholar
[12]	Cheng Q M. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China[J]. Ore Geology Reviews, 2007, 32(1/2):314-324. Google Scholar
[13]	Aitchison J. The statistical analysis of compositional data[M]. The Blackburn Press, 1986. Google Scholar
[14]	Egozcue J J, Pawlowsky-Glahn V, Mateu-Figueras G, et al. Isometric logratio transformations for compositional data analysis[J]. Mathematical Geology, 2003, 35(3):279-300. doi: 10.1023/A:1023818214614 CrossRef Google Scholar
[15]	周永章, 张良均, 张奥多, 王俊.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. Google Scholar
[16]	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. Google Scholar
[17]	焦守涛, 周永章, 张旗等.基于GEOROC数据库的全球辉长岩大数据的大地构造环境智能判别研究[J].岩石学报, 2018, 34(11):3189-3194. Google Scholar
[18]	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. Google Scholar
[19]	Chen Y L, Wu W. Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data[J]. Geochemistry Exploration Environment Analysis, 2017, 17(3):231-238. doi: 10.1144/geochem2016-024 CrossRef Google Scholar
[20]	Beucher A, Österholm P, Martinkauppi A, et al. Artificial neural network for acid sulfate soil mapping:Application to the Sirppujoki River catchment area, south-western Finland[J]. Journal of Geochemical Exploration, 2013, 125:46-55. doi: 10.1016/j.gexplo.2012.11.002 CrossRef Google Scholar
[21]	Chen Y L, Lu L J, Li X B. Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly[J]. Journal of Geochemical Exploration, 2014, 140:56-63. doi: 10.1016/j.gexplo.2014.02.013 CrossRef Google Scholar
[22]	O'Brien J J, Spry P G, Nettleton D, et al. Using Random Forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb-Zn-Ag deposits in the Broken Hill domain, Australia[J]. Journal of Geochemical Exploration, 2015, 149:74-86. doi: 10.1016/j.gexplo.2014.11.010 CrossRef Google Scholar
[23]	Xiong Y H, Zuo R G. Recognition of geochemical anomalies using a deep autoencoder network[J]. Computers & Geosciences, 2016, 86:75-82. Google Scholar
[24]	Zaremotlagh S, Hezarkhani A. The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran[J]. Journal of African Earth Sciences, 2017, 128:37-46. doi: 10.1016/j.jafrearsci.2016.08.018 CrossRef Google Scholar
[25]	时建民, 石绍山, 江山, 等.环形构造与地球化学异常相关性分析及其找矿意义[J].地质与资源, 2016, 25(2):181-185. doi: 10.3969/j.issn.1671-1947.2016.02.016 CrossRef Google Scholar
[26]	马生明, 朱立新, 刘海良, 等.甘肃北山辉铜山铜矿地球化学异常结构研究[J].地球学报, 2011, 32(4):405-412. doi: 10.3975/cagsb.2011.04.03 CrossRef Google Scholar
[27]	李庆谋, 成秋明.分形奇异(特征)值分解方法与地球物理和地球化学异常重建[J].地球科学, 2004(1):109-118. doi: 10.3321/j.issn:1000-2383.2004.01.019 CrossRef Google Scholar
[28]	Shi J B, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on pattern analysis and machine intelligence, 2000, 22(8):888-905. doi: 10.1109/34.868688 CrossRef Google Scholar