Document Type : Original Research Paper

Authors

1 Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.

2 Department of Geology, Islamic Azad University-Tabriz Branch, Tabriz, Iran.

3 Department of Mining Engineering, Islamic Azad University –Ahar Branch, Ahar, Iran .

Abstract

Optimization of geochemical anomalies needs an orientation survey in which one of its important aspects is selecting an advanced data processing method. The main objective of this study is to recognize the blind and mineralization zones by employment of new processing techniques in order to establish an optimized exploration tool and reliable geochemical pattern for potentially promising areas in Gulan. In this respect 233 stream sediment samples were collected and analyzed for Cu, Pb, Zn, Mo, Co, Ni, Cr, As, and Y. The anomalous zones were detected by using PCA&FCMC methods. The FCMC results revealed Cu, Mo anomalous zones in Garachilar area. It shows secondary halos separation of Cu and Mo probably due to transportation of Mo in the form of molybdates by acidic solution around outcrops, and consequently their adjoined redeposition. Application of Fuzzy logic Based FCMC shows the emplacement of Cu and Mo in the same cluster and overlapping of their anomalies which indicate their paragenetic relation in the ore bearing solution. Comparative study of the methods (FCMC&PCA) revealed some how similar results in detecting Garachilar anomalies. But the PCA results not only indicate Garachilar as promising zones but also could detect western part of Lutkeh and blind anomalies of Namnig in the same trend of NW-SE. This study indicates that geochemical pattern detected by PCA is more effective in enhancement of halos and blind anomalies than FCMC. Moreover, the characterization of geochemical pattern by PCA can be optimized more precisely in eliminating lithological effect and its results can be used successfully as prospecting tool in the area.

Keywords

References
Bezdek, J. C., Ehrlich, R. & Full, W., 1984 - FCM: the fuzzy c-means Clustering algorithm”, J. Computers & Geosciences (10), pp.191-20
Brown, W., Groves, D. & Gedeon, T., 2003- Use of fuzzy membership input layers to combine subjective geological knowledge and empirical data in a neural network method for mineral-potential mapping: Natural Resources Research, International Association for Mathematical Geology, Special issue on Neural networks (12), pp.183-200
Crosta, A. P, Rabelo, A., 1993- Assessing Landsat/TM for Hydrothermal mapping in Central Western, Brazil, in processing of the 9th Thematic Conference Of Geologic Remote Sensing.
Du , Q. & Flower, E. J, 2008- Low-Complexity Principal Component Analysis for Hyperspectral Image Compression.International Journal of High Performance Computing Applications,pp.438-448  .
Fresman, A. E., 1939b- Geochemical and mineralgical methods of prospecting for useful minerals., In: U.S. Geol. surv. Circ.127, 37pp.
Howarth, R. J. & Sinding-Larson, R., 1983- In: R. J. Howarth(editor), Statistic and Data Analysis in Geochemical prospecting. Handbook of exploration Geochemistry, vol.2, Elsevier, Amesterdam, pp: 207-289.
Knox-Robinson, C. M., 2000- Vectorial fuzzy logic; a novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralization potential of the Kalgoorlie Terrane, Western Australia: Australian; Journal of Earth Sciences( 47), pp. 929-941.
Kramar, U., 1995- Application of limited fuzzy clusters to anomaly recognition in complex geological environments, Elsevier, Journal of Geochemical Exploration (55), pp. 81-92.
Loska, K. & Wiechuła, D., 2003- Application of principal component analysis for the estimation of source of heavy metal contamination in surface sediments from the Rybnik Reservoir, Journal of Chemosphere 51:723–733
Loughlin, W. P. G., 1991- Principal Component Analysis Alteration Mapping. Photogram. Eng. Remote Sensing.
Lovering, T. S., Huff, L. C., Almond, H., 1950- Dispersion of Copper from San Manual Copper Deposit., Pinal County, Arizona, Econ. Geol., 45:493-514.
Pasadakis, N., Obermajer, M. & Osadetz, K. G., 2004- Definition and characterization of petroleum compositional families in Williston Basin, North America using principal component analysis. Journal of Organic Geochemistry 35 (2004) 453–468.
Prinzhofer, A., Mello, M. R., Da Sila Freitas, L. C. & Takaki, T., 2000- A new geochemical characterization of natural gas and its use in oil and gas evaluation. In Mello M.R. and Katz, B.J.(Eds.), Petroleum systems and south Atlantic Margins.American Association of Petroleum Geologists Bulletin, Memoir 70:107-119.
Rantitsch, G., 2000- Application of fuzzy clusters to quantify lithological background concentrations in stream-sediment geochemistry; Elsevier, J. Geochem. Explor (71), pp. 73–82. 
Rose, A. W., Hawkes, H. E. & Webb, J. S., 1979- Geochemistry in mineral exploration.", Academic press, New York .N.Y., 2nd ed., 657pp.
Sabeti, H., Javaherian, A. & Araabi, N. D., 2007- Principal component analysis applied to seismic horizon interpretations .International congress of Petroleum Geostatistics , Cascais, Portugal, 10 - 14 September 2007.
Webb, J. S., 1958b- Observation of Geochemical exploration in tropical terrains. In: Symposium de exploration Geochemica 20th. Int. Geol. Congr. Mexico City, 1956, pp: 143-147.
Woodall, R., 1984- Success in mineral exploration Confidence in source and Ore deposit models." Geosci. Can., 11:127-132.