Abstract
Multisource geoscience data can provide significant information for mineral exploration in a variety of ways. For example, remote-sensing images record the spectral characteristics of objects, and geochemical data represent the enrichment or depletion of geochemical elements, which reflect the physical and chemical attributes of geological features. In this study, a hybrid model comprising data fusion and machine learning was applied for lithological mapping. This process is illustrated through a case study of mapping several lithological units in the Cuonadong Dome, in the northeastern part of the Himalayas, China. In this process, multisource data fusion technology is first used to provide more abundant information by integrating geochemical data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote-sensing images, retaining both the geochemical patterns and the textural structure of the remote-sensing images. Then, a random forest metric learning (RFML) approach is employed to achieve a high classification performance based on the fused data. RFML adopts metric learning in the classification process of each decision tree calculation, making full use of the advantages of random forest and metric learning. Seven target lithological units were discriminated with 93.0% overall accuracy. This excellent performance demonstrates the effectiveness of the hybrid method in the geological exploration of areas in poor environments that have undergone limited geological research.
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References
Adelson EH, Anderson CH, Bergen JR, Burt PJ, Ogden JM (1984) Pyramid methods in image processing. RCA Eng 29(6):33–41
Aiazzi B, Alparone L, Barducci A, Baronti S, Pippi I (1999) Multispectral fusion of multisensor image data by the generalized Laplacian pyramid. IEEE Int Geosci Remote Sens Symp. https://doi.org/10.1109/IGARSS.1999.774572
Baghshah MS, Shouraki SB (2010) Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data. Pattern Recogn 43(8):2982–2992. https://doi.org/10.1016/j.patcog.2010.02.022
Bannari A, El-Battay A, Saquaque A, Miri A (2016) PALSAR-FBS L-HH mode and Landsat-TM data fusion for geological mapping. Adv Remote Sens 5(4):246–268. https://doi.org/10.4236/ars.2016.54020
Breiman L (1986) Probability and stochastic processes: with a view toward applications. Course Technology
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140. https://doi.org/10.1023/A:1018054314350
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Cao X, Ge Y, Li R, Zhao J, Jiao L (2019) Hyperspectral imagery classification with deep metric learning. Neurocomputing 356:217–227. https://doi.org/10.1016/j.neucom.2019.05.019
Cao H, Li G, Zhang Z, Zhang L, Dong S, Xia X, Liang W, Fu J, Huang Y, Xiang A (2020) Miocene Sn polymetallic mineralization in the Tethyan Himalaya, southeastern Tibet: a case study of the Cuonadong deposit. Ore Geol Rev. https://doi.org/10.1016/j.oregeorev.2020.103403
Cheng G, Yang C, Yao X, Guo L, Han J (2018) When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Trans Geosci Remote Sens 56(5):2811–2821. https://doi.org/10.1109/TGRS.2017.2783902
De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The Mahalanobis distance. Chemometr Intell Lab Syst 50(1):1–18. https://doi.org/10.1016/S0169-7439(99)00047-7
Ding H, Jing L, Li H, Tang Y, Ma H, Zhu B, Wang W, Qiu L (2018) A method and system for improving the resolution of geochemical layers. Chinese patent no. 201811275285.4
Dong Y, Du B, Zhang L (2015) Target detection based on random forest metric learning. IEEE J Sel Top Appl Earth Observ Remote Sens 8(4):1830–1838. https://doi.org/10.1109/JSTARS.2015.2416255
Eldosouky AM, Abdelkareem M, Elkhateeb SO (2017) Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt. J Afr Earth Sci 130:28–37. https://doi.org/10.1016/j.jafrearsci.2017.03.006
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Fu J, Li G, Wang G, Huang Y, Zhang L, Dong S, Liang W (2017) First field identification of the Cuonadong dome in southern Tibet: implications for EW extension of the North Himalayan gneiss dome. Int J Earth Sci 106(5):1581–1596. https://doi.org/10.1007/s00531-016-1368-2
Fu J, Li G, Wang G, Zhang L, Liang W, Zhang Z, Zhang X, Huang Y (2018) Synchronous granite intrusion and E-W extension in the Cuonadong dome, southern Tibet, China: evidence from field observations and thermochronologic results. Int J Earth Sci 107(6):2023–2041. https://doi.org/10.1007/s00531-018-1585-y
He H, Yang X, Li Y, Li Y, Wang L (2010) Multi-source data fusion technique and its application in geological and mineral survey. J Earth Sci Environ 32(1):44–47
Hunt GR (2017) Spectroscopic properties of rocks and minerals. CRC Press, London, pp 295–386
Jing L, Cheng Q (2010) A technique based on non-linear transform and multivariate analysis to merge thermal infrared data and higher-resolution multispectral data. Int J Remote Sens 31(24):6459–6471. https://doi.org/10.1080/01431160903439841
Kuhn S, Cracknell MJ, Reading AM (2018) Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: a demonstration study from the Eastern Goldfields of Australia. Geophysics 83(4):183–193. https://doi.org/10.1190/geo2017-0590.1
Le C, Zhong S, Chen K (2014) An efficient fusion method of distance metric learning and random forests distance for image verification. In: IEEE international conference on audio, language and image processing, pp 222–227. https://doi.org/10.1109/icalip.2014.7009790
Li G, Zhang L, Jiao Y, Xia X, Dong S, Fu J, Liang W, Zhang Z, Wu J, Dong L (2017) First discovery and implications of Cuonadong superlarge Be–W–Sn polymetallic deposit in Himalayan metallogenic belt, southern Tibet. Miner Depos 36(4):1003–1008. https://doi.org/10.16111/j.0258-7106.2017.04.014
Liu Z, Wu F, Liu X, Wang J, Yin R, Qiu Z, Ji W, Yang L (2019) Mineralogical evidence for fractionation processes in the Himalayan leucogranites of the Ramba Dome, southern Tibet. Lithos 340:71–86. https://doi.org/10.1016/j.lithos.2019.05.004
Van der Meer FD, Van der Werff HM, Van Ruitenbeek FJ, Hecker CA, Bakker WH, Noomen MF, Van Der Meijde M, Carranza EJM, De Smeth JB, Woldai T (2012) Multi-and hyperspectral geologic remote sensing: a review. Int J Appl Earth Obs Geoinf 14(1):112–128. https://doi.org/10.1016/j.jag.2011.08.002
Viera A, Garrett J (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37(5):360–363
Wang W, Cheng Q (2008) Mapping mineral potential by combining multi-scale and multi-source geo-information. In: IEEE international geoscience and remote sensing symposium, pp 1321–1324. https://doi.org/10.1109/igarss.2008.4779247
Wang Z, Dong Y, Zuo R (2019a) Mapping geochemical anomalies related to Fe–polymetallic mineralization using the maximum margin metric learning method. Ore Geol Rev 107:258–265. https://doi.org/10.1016/j.oregeorev.2019.02.027
Wang Z, Zuo R, Dong Y (2019b) Mapping geochemical anomalies through integrating random forest and metric learning methods. Nat Resour Res 28(4):1285–1298. https://doi.org/10.1007/s11053-019-09471-y
Wang Z, Zuo R, Dong Y (2020) Mapping Himalayan leucogranites using a hybrid method of metric learning and support vector machine. Comput Geosci. https://doi.org/10.1016/j.cageo.2020.104455
Wu F, Liu X, Liu Z, Wang R, Xie L, Wang J, Ji W, Yang L, Liu C, Khanal GP (2020) Highly fractionated Himalayan leucogranites and associated rare-metal mineralization. Lithos 352:105319. https://doi.org/10.1016/j.lithos.2019.105319
Xie X, Mu X, Ren T (1997) Geochemical mapping in China. J Geochem Explor 60(1):99–113. https://doi.org/10.1016/s0375-6742(97)00029-0
Xie J, Qiu H, Bai X, Zhang W, Wang Q, Xia X (2018) Geochronological and geochemical constraints on the Cuonadong leucogranite, eastern Himalaya. Acta Geochim 37(3):347–359. https://doi.org/10.1007/s11631-018-0273-8
Xie L, Tao X, Wang R, Wu F, Liu C, Li X, Zhang R (2020) Highly fractionated leucogranites in the eastern Himalayan Cuonadong dome and related magmatic Be–Nb–Ta and hydrothermal Be–W–Sn mineralization. Lithos 354:105286. https://doi.org/10.1016/j.lithos.2019.105286
Xiong C, Johnson D, Xu R, Corso JJ (2012) Random forests for metric learning with implicit pairwise position dependence. In: Proceedings of the 18th ACM international conference on knowledge discovery and data mining, pp 958–966. https://doi.org/10.1145/2339530.2339680
Yin A (2006) Cenozoic tectonic evolution of the Himalayan orogen as constrained by along-strike variation of structural geometry, exhumation history, and foreland sedimentation. Earth Sci Rev 76(1–2):1–131. https://doi.org/10.1016/j.earscirev.2005.05.004
Acknowledgements
Thanks are due to the associate editor and two reviewers for their comments and suggestions, which helped to improve this manuscript. This study was jointed supported by the National Natural Science Foundation of China (no. 41972303) and the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (MSFGPMR03-3).
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Wang, Z., Zuo, R. & Jing, L. Fusion of Geochemical and Remote-Sensing Data for Lithological Mapping Using Random Forest Metric Learning. Math Geosci 53, 1125–1145 (2021). https://doi.org/10.1007/s11004-020-09897-8
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DOI: https://doi.org/10.1007/s11004-020-09897-8