«Automation and Informatization of the fuel and energy complex»

Application of Getis-Ord Gi* and DBSCAN method when prospecting for mineral resources

UDC: 004:553.3/.9:622
DOI: -

Authors:

¹ Tyumen Industrial University, Tyumen, Russia

Keywords: mineralization clusters, minerals exploration mapping, spatial analysis, Getis-Ord Gi* method, geological exploration, DBSCAN method, hot and cold spots, geology, exploration strategies, mineral resources, clusters identification

Annotation:

Identification of mineralization clusters plays a vital role in mineral exploration mapping as it allows optimizing exploration efforts and minimizing costs. The authors of the article analyze the geospatial data mineralization using the Getis-Ord Gi* method based on weight matrix calculation as well as ompare it with the results of DBSCAN. The Getis-Ord Gi* method allows to identify hot spots (zones of minerals high concentration) and cold spots (zones of minerals low concentration) with high accuracy using statistical analysis of spatial autocorrelation. In contrast, the DBSCAN method based on density clustering showed lower reliability due to its dependence on the choice of parameters and the need for expert interpretation. The results of the study highlight the advantages of the Getis-Ord Gi* method in accuracy and statistical significance when identifying promising zones of mineralization. The authors of the article propose a geostatistical methodology for analyzing spatial data in order to identify significant zones and plan further exploration work. In future, it is proposed to use artificial neural network algorithms to predict mineralization clusters based on the identified clusters.

Bibliography:

1. Carranza E.J.M., Laborte A.G. Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) // Computers & Geosciences. – 2015. – Vol. 74. – P. 60–70. – DOI: 10.1016/j.cageo.2014.10.004
2. Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook / M. Yousefi, E.J.M. Carranza, O.P. Kreuzer [et al.] // J. of Geochemical Exploration. – 2021. – Vol. 229. – P. 106839. – DOI: 10.1016/j.gexplo.2021.106839
3. Bekti R.D., Dirgantara G.E., Sutanta E. Distance and AMOEBA Weights Matrices in Local Getis Ord-G Statistics to Identify Spatial Cluster of Gini Ratio // 2021 3rd Int. Conf. on Electronics Representation and Algorithm (ICERA), Yogyakarta, Indonesia, July 29–30, 2021. – IEEE, 2021. – P. 109–114. – DOI: 10.1109/ICERA53111.2021.9538666
4. Wang Zheye, Lam N.S.N. Extending Getis-Ord Statistics to Account for Local Space–Time Autocorrelation in Spatial Panel Data // The Professional Geographer. – 2020. – Vol. 72, Issue 3. – P. 411–420. – DOI: 10.1080/00330124.2019.1709215
5. Murad A., Khashoggi B.F. Using GIS for Disease Mapping and Clustering in Jeddah, Saudi Arabia // ISPRS Int. J. of Geo-Information. – 2020. – Vol. 9, Issue 5. – P. 328. – DOI: 10.3390/ijgi9050328
6. Kondo K. Hot and Cold Spot Analysis Using Stata // The Stata J.: Promoting communications on statistics and Stata. – 2016. – Vol. 16, Issue 3. – P. 613–631. – DOI: 10.1177/1536867X1601600304
7. Teso P.M., Katcho K. Assessing Forest Cover Change and Deforestation Hot-Spots in the North Kivu Province, DR-Congo Using Remote Sensing and GIS // American J. of Geographic Information System. – 2019. – Vol. 8, No. 2. – P. 39–54. – DOI: 10.5923/j.ajgis.20190802.01
8. Jana M., Sar N. Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India // Modeling Earth Systems and Environment. – 2016. – Vol. 2, Issue 2. – Article No. 60. – DOI: 10.1007/s40808-016-0122-x
9. Luong Thi-Bich-Thuy, Cao Thi-Hien. Use of Getis-Ord's statistic to detect hotspots and coldspots of COVID-19 in Hanoi City, Vietnam // World J. of Biology Pharmacy and Health Sciences. – 2023. – Vol. 15, Issue 3. – P. 102–109. – DOI: 10.30574/wjbphs.2023.15.3.0394
10. Çelik M., Dadaşer-Çelik F., Dokuz A.Ş. Anomaly detection in temperature data using DBSCAN algorithm // 2011 Int. Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey, June 15–18, 2011. – IEEE, 2011. – P. 91–95. – DOI: 10.1109/INISTA.2011.5946052
11. Deng D. DBSCAN Clustering Algorithm Based on Density // 2020 7th Int. Forum on Electrical Engineering and Automation (IFEEA), Hefei, China, Sept. 25–27, 2020. – IEEE, 2020. – P. 949–953. – DOI: 10.1109/IFEEA51475.2020.00199
12. Mineral Resource and Reserve Update for the Agbaou Gold Mine Côte d'Ivoire West Africa: Technical Report / A. Roux, K.K. Woodman, K. Harris, M. Alyoshin. – Vancouver: Endeavour Mining Corporation, 2015. – VIII, 121 p.
13. A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases / A. Ram, J. Sunita, A.S. Jalal, M. Kumar // Int. J. of Computer Applications. – 2010. – Vol. 3, No 6. – DOI: 10.5120/739-1038
14. Louhichi S., Gzara M., Ben Abdallah H. A density based algorithm for discovering clusters with varied density // 2014 World Congress on Computer Applications and Information Systems (WCCAIS), Hammamet, Tunisia, Jan. 17–19, 2014. – IEEE, 2014. – DOI: 10.1109/WCCAIS.2014.6916622
15. Okunev I.Yu., Kushnareva A.E. Al'ternativnye matritsy prostranstvennykh vesov sosedstva: metodika sozdaniya i ispol'zovaniya na primere rascheta lokal'nykh indikatorov prostranstvennoy avtokorrelyatsii // Vestn. S.-Peterb. un-ta. Nauki o Zemle. – 2023. – T. 68, № 2. – S. 390–413. – DOI: 10.21638/spbu07.2023.210
16. Statistika Getis-Ord Gi* dlya analiza soderzhaniya uglevodorodov v basseyne reki Trom"egan / V.A. Dobryakova, N.N. Moskvina, A.B. Dobryakov [i dr.] // InterKarto. InterGIS. – 2020. – T. 26, № 2. – S. 151–160. – DOI: 10.35595/2414-9179-2020-2-26-151-160
17. Petrov A.M., Popov A.N., Kuzyakov O.N. Sovershenstvovanie arkhitektury intellektual'nykh sistem upravleniya // Avtomatizatsiya i informatizatsiya TEK. – 2023. – № 4(597). – S. 15–22. – DOI: 10.33285/2782-604X-2023-4(597)-15-22