Estimation of copper concentration of rocks using hyperspectral technology

Abstract

Rock geochemical information is important for mineral exploration and provides a theoretical basis for the rapid delineation of hidden minerals. Remote sensing technology provides the possibility of rapid and large-scale extraction of geochemical information from the earth’s surface. This study analyzed the relationship between copper concentration and rock spectra by first collecting 222 rock samples, and then measuring the copper concentration of rock samples in the laboratory and reflectance spectra using an ASD FieldSpec3 portable spectrometer. It finally established quantitative relationships between the original spectra, first-order derivative spectra and second-order derivative spectra and copper concentration, respectively, using the partial least squares support vector machine method (PLS-SVM). The results show that 1) The estimation accuracy of using second-order derivatives spectra as input parameters to establish a model for estimating copper concentration is the highest, and the determined coefficient (R²) between the predicted value and real value reaches 0.54. 2) When the copper concentration is less than 80 mg/kg, the inversion model of copper concentration established using PLS-SVM obtains a good result. The R² between the predicted copper concentration and the real copper concentration reached 0.70248. When the copper concentration is greater than 80 mg/kg, the inversion model of copper concentration established using partial least squares (PLS) obtains a good result. The R² between the predicted copper concentration and the real copper concentration reached 0.49. The R² between real copper concentration and copper predicted by the method of piecewise separate modeling reaches 0.816. Therefore, the method of segmental modeling has great potential to improve the accuracy of copper concentration inversion.

Keywords

copper concentration / rock / geochemical information / PLS-SVM / remote sensing

References

[1] Abdel-Rahman E M, Mutanga O, Odindi J, Adam E, Odindo A, Ismail R (2014). A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of swiss chard grown under different irrigation water sources using hyperspectral data. Comput Electron Agric, 106: 11–19

[2] Altmann Y, Halimi A, Dobigeon N, Tourneret J Y (2012). Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery. IEEE Trans Image Process, 21(6): 3017–3025

[3] Azizi H, Tarverdi M A, Akbarpour A (2010). Extraction of hydrothermal alterations from ASTER SWIR data from east Zanjan, Northern Iran. Adv Space Res, 46(1): 99–109

[4] Borel C C, Gerstl S A W (1994). Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sens Environ, 47(3): 403–416

[5] Choe E, Kim K W, Bang S, Yoon I H, Lee K Y (2009). Qualitative analysis and mapping of heavy metals in an abandoned Au–Ag mine area using NIR spectroscopy. Environ Geol, 58(3): 477–482

[6] Cortes C, Vapnik V N (1995). Support vector networks. Mach Learn, 20(3): 273–297

[7] Fassoniandrade A C, Zanotta D C, Guasselli L A, de Andrade A M(2017). Linear spectral mixing model for estimating optically active components in estuarine waters of Patos Lagoon, Brazil. Int J Remote Sens, 38(17): 4767–4781

[8] Freitas R M D, Haertel V, Shimabukuro Y E (2008). Linear spectral mixture model in moderate spatial resolution image data. Bol Ciênc Geod, 14(1): 55–71

[9] Gannouni S, Rebai N, Abdeljaoued S (2012). A spectroscopic approach to assess heavy metals contents of the mine waste of Jalta and Bougrine in the north of Tunisia. J Geogr Inf Syst, 4(3): 242–253

[10] Gong P, Yu B (2001). Conifer species recognition: effects of data transformation. Int J Remote Sens, 22(17): 3471–3481

[11] Haertel V F, Shimabukuro Y E (2005). Spectral linear mixing model in low spatial resolution image data. IEEE Trans Geosci Remote Sens, 43(11): 2555–2562

[12] Hede A N H, Kashiwaya K, Koike K, Sakurai S (2015). A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area. Remote Sens Environ, 171: 83–97

[13] Honarmand M, Ranjbar H, Shahabpour J (2012). Application of principal component analysis and spectral angle mapper in the mapping of hydrothermal alteration in the Jebal–Barez area, Southeastern Iran. Resour Geol, 62(2): 119–139

[14] Heylen R, Scheunders P (2016). A multilinear mixing model for nonlinear spectral unmixing. IEEE Trans Geosci Remote Sens, 54(1): 240–251

[15] Ibrahim W S, Watanabe K, Yonezu K (2016). Structural and litho-tectonic controls on Neoproterozoic base metal sulfide and gold mineralization in north Hamisana shear zone, south eastern desert, Egypt: the integrated field, structural, Landsat 7 ETM+ and ASTER data approach. Ore Geol Rev, 79: 62–77

[16] Kemper T, Sommer S (2002). Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Technol, 36(12): 2742–2747

[17] Khaleghi M, Ranjbar H, Shahabpour J, Honarmand M (2014). Spectral angle mapping, spectral information divergence, and principal component analysis of the ASTER SWIR data for exploration of porphyry copper mineralization in the Sarduiyeh area, Kerman Province, Iran. Appl Geomat, 6(1): 49–58

[18] Kooistra L, Wehrens R, Leuven R S E W, Buydens L M C (2001). Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal Chim Acta, 446(1–2): 97–105

[19] Liu M, Lin Q Z, Wang Q J, Li H (2010). Study on the geochemical anomaly of copper element based on reflectance spectra. Spectrosc Spect Anal, 30(5): 1320–1323

[20] Malley D F, Williams P C (1997). Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ Sci Technol, 31(12): 3461–3467

[21] Moros J, Vallejuelo S F O, Gredilla A, Diego A, Madariaga J M, Garrigues S, Guardia M. (2009). Use of reflectance infrared spectroscopy for monitoring the metal content of the estuarine sediments of the Nerbioi-Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country). Environ Sci Technol, 43(24): 9314–9320

[22] Qaid A M, Basavarajappa H T, Ranjbar H (2009). Application of principal component analysis to ASTER and ETM+ data for mapping the alteration zones in north east of Hajjah, Yemen. Southeast Asian J Surg, 9(2): 15–21

[23] Quarmby N A, Townshend J R G, Settle J J, White K H, Milnes M, Hindle T L, Silleos N (1992). Linear mixture modelling applied to AVHRR data for crop area estimation. Int J Remote Sens, 13(3): 415–425

[24] Ray T W, Murray B C (1996). Nonlinear spectral mixing in desert vegetation. Remote Sens Environ, 55(1): 59–64

[25] Rady A M, Guyer D E, Kirk W, Donis-González I R (2014). The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes. J Food Eng, 135(2): 11–25

[26] Ranjbar H, Honarmand M, Moezifar Z (2004). Application of the Crosta technique for porphyry copper alteration mapping, using ETM data in the southern part of the Iranian volcanic sedimentary belt. J Asian Earth Sci, 24(2): 237–243

[27] Salem S M, Arafa S A, Ramadan T M, El Gammal E S A(2013). Exploration of copper deposits in Wadi El Regeita area, Southern Sinai, Egypt, with contribution of remote sensing and geophysical data. Arab J Geosci, 6(2): 321–335

[28] Sawut M, Ghulam A, Tiyip T, Zhang Y J, Ding J L, Zhang F, Maimaitiyiming M (2014). Estimating soil sand content using thermal infrared spectra in arid lands. Int J Appl Earth Obs Geoinf, 33(12): 203–210

[29] Schuerger A C, Capelle G A, Di BenedettoJ A, Mao C, Thai C N, Evans M D, (2003). Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum, Flugge.). Remote Sens Environ, 84(4): 572–588

[30] Seasholtz M B, Kowalski B (1993). The parsimony principle applied to multivariate calibration. Anal Chim Acta, 277(2): 165–177

[31] Serrano L, Peñuelas J, Ustin S L (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens Environ, 81(2–3): 355–364

[32] Malekzadeh Shafaroudi A, Karimpour M H, Stern C R, Mazaheri S A (2009). Hydrothermal alteration mapping in SW Birjand, Iran, using the advanced spaceborne thermal emission and reflection radiometer (ASTER) image processing. J Appl Sci (Faisalabad), 9(5): 829–842

[33] Shi T, Liu H, Chen Y, Wang J, Wu G (2016). Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. J Hazard Mater, 308: 243–252

[34] Siebielec G, McCarty G W, Stuczynski T I, Reeves J B 3rd (2004). Near- and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. J Environ Qual, 33(6): 2056–2069

[35] Song Y, Li F, Yang Z, Ayoko G A, Frost R L, Ji J (2012). Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang River Delta, China. Appl Clay Sci, 64(4): 75–83

[36] Son Y S, Kang M K, Yoon W J (2014). Lithological and mineralogical survey of the Oyu Tolgoi region, Southeastern Gobi, Mongolia using ASTER reflectance and emissivity data. Int J Appl Earth Obs Geoinf, 26(1): 205–216

[37] Sridhar B B M, Han F X, Diehl S V, Monts D L, Su Y (2007). Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium. Int J Remote Sens, 28(5): 1041–1054

[38] Tang L L, Zhang X F, Liu Y H (2006). Research progress in the foundation and technology of remote sensing lithology information extraction. Mining Research and Development, 26(3): 68–73 (in Chinese)

[39] Tangestani M H, Moore F (2001). Comparison of three principal component analysis techniques to porphyry copper alteration mapping: a case study, Meiduk Area, Kerman, Iran. Can J Rem Sens, 27(2): 176–182

[40] Wang L, Bai Y L, Lu Y, Wang H (2012). Effect on retrieval precision for corn N content by spectrum data transformation. Remote Sensing Technology and Application, 26(2): 220–225 (in Chinese)

[41] Wen X, Hu G, Yang X (2007). A Simplified Method for Extracting Mineral Information from Hyperspectral Remote Sensing Image Using SAM Algorithm. In: Zhao P D, Agterberg F, Cheng Q M, eds. The 12th Conference of the international Association for mathematical Geology. China University of Geosciences, 526–529

[42] Wold S, Sjöström M, Eriksson L (2001). PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst, 58(2): 109–130

[43] Wu Y, Chen J, Ji J, Gong P, Liao Q, Tian Q, Ma H (2007). A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Sci Soc Am J, 71(3): 918–926

[44] Xue Y, Dai T G, Zou L, Xia H D, Liu J L (2007). Extracting alteration information based on SVM—taking Jidi area as an example. Geological Survey Re, 30(4): 315–320 (in Chinese)

[45] Yan J, Zhou K, Wang J, Wang S, Wang W, Dong L I (2013). Extraction of hyper-spectral remote sensing alteration information based on SAM and SVM. Computer Engineering and Applications, 49(19): 141–146 (in Chinese)

[46] Yang T, Xue Y, Dai T G (2008). Extracting alteration information by SVM based on spectrum and texture—taking Jidi area as the example. Earth & Environment, 36(1): 81–86 (in Chinese)

[47] Yang C B, Zhang C X, Liu F, Jiang Q G (2015). Study on the relationship between the depth of spectral absorption and the content of the mineral composition of biotite. Spectrosc Spect Anal, 35(9): 2583–2587

[48] Yoder B J, Pettigrewcrosby R E (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens Environ, 53(3): 199–211

[49] Zhang C, Ren H, Qin Q, Ersoy O K (2017). A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI). Remote Sens Lett, 8(6): 576–585

[50] Zhou K, Zhang N (2017). Extraction of alteration mineral information from moderate remote sensing images using MPS method. Photonirvachak (Dehra Dun), 46(2): 1–8

[51] Zhou G Z, Wang C Z, Yang F J, Li Y M (2009). Field collected plant spectrum denoising by logarithm transform and wavelet transform. J Infrared Millim W, 28(4): 316–320

[52] Zhu Y, Shen G R, Xiang Q Q, Wu Y (2017). Spectral characteristics of soil salinity based on different pre-processing methods. Chinese Journal of Soil Science, 48(3): 560–568 (in Chinese)

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