شناسایی محدوده‌های امیدبخش کانی‌سازی آهن با استفاده از دورسنجی در محدوده کاشان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی معدن، دانشکده مهندسی علوم زمین، دانشگاه صنعتی اراک، اراک، ایران

2 گروه مهندسی معدن، دانشکده مهندسی معدن، دانشگاه صنعتی اصفهان، اصفهان، ایران

چکیده

در پژوهش حاضر به­منظور شناسایی پتانسیل و حضور کانه­سازی احتمالی آهن در محدوده کاشان استان اصفهان، ابتدا به کمک تلفیق داده­های سنجنده­های ASTER، EO-1 و ETM8 مطالعات سنجش از دور انجام شد. سپس اکتشاف مقدماتی محدوده­های امیدبخش با استفاده از روش ژئوفیزیکی مغناطیس­سنجی هوایی صورت گرفت. عملیات دورسنجی داده­های محدوده شامل مراحل پیش­پردازش همانند تصحیح هندسی به روش تصویر به تصویر، تصحیح اتمسفری و تکنیک­های پردازش ترکیب رنگی کاذب، نسبت­گیری باندی، تحلیل مولفه­های اصلی انتخابی، طبقه­بندی نظارت شده با استفاده از روش نقشه­برداری زاویه طیفی و در نهایت طبقه­بندی به روش دمای سطح زمین است. در نتیجه این فرآیند، نقشه پهنه­های دگرسانی مرتبط با کانی­زایی آهن منطقه مورد مطالعه مشخص شد. عملیات برداشت مغناطیسی هوابرد با فاصله خطوط پرواز 5/7 کیلومتر از یکدیگر در محدوده­ای به وسعت تقریبی 5/852 کیلومتر مربع صورت گرفته است. برای انجام عملیات پردازش و تفسیر کیفی داده­های مغناطیسی همانند اعمال تصحیحات و فیلترهای مختلف نظیر برگردان به قطب، گسترش به سمت بالا تا ارتفاع­های مختلف، فیلتر پایین­گذر، فیلترهای مشتق شامل گرادیان افقی کل و سیگنال تحلیلی، از نرم­افزار Geosoft Oasis montaj استفاده شد. درنهایت به­منظور بررسی روند بی­هنجاری­های مغناطیسی مشاهده شده بر روی سطح، تعیین شکل تقریبی توده کانسار و تخمین عمق آن، مدلسازی وارون سه­بعدی داده­ها انجام گرفت. نتایج پژوهش حاضر از طریق تلفیق دو روش سریع و نسبتاً ارزان سنجش از دور و مغناطیس­سنجی هوابرد همراه با مدل­سازی وارون سه­بعدی داده­های مغناطیسی، نشان می­دهند که محدوده کاشان از نظر کانه­سازی آهن دارای پتانسیل بالایی است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Application of remote sensing to determine promising areas of iron mineralization in Kashan district

نویسندگان [English]

  • Reza Ahmadi 1
  • عبدالرضا قره شیخ بیات 1
  • سید نادر نائب پور 2
1 Mining Engineering Department, Faculty of Geo- Engineering Sciences, Arak University of Technology, Arak, Iran
2 Mining Engineering Department, Faculty of Mining Engineering, Isfahan University of Technology, Isfahan, Iran
چکیده [English]

Introduction
The studied area, which is a part of the central Iran zone and the magmatic belt of Urmia-Dokhtar zone, is located in Isfahan province, Kashan district. Due to the volcanic and plutonic evolutions of Urmia-Dokhtar zone, various types of iron mineralization can be found in this zone in the form of hydrothermal, skarn and volcanic. Since the ASTER and EO-1 sensors are powerful in the short-wave infrared (SWIR) and visible-near-infrared (VNIR) spectrum bands, respectively, therefore, in this research, the combination of these two bands from these two different sensors was used to increase the precision and accuracy of iron prospecting through remote sensing in the Kashan district.
Materials and methods
The remote sensing process of the studied region comprises two stages. The first stage is pre-processing and data preparation before entering the processing stage. The second step is determining the best bands of ASTER and EO-1 ALI sensors and applying processing techniques containing false color composite (FCC), banding ratio (BR), Crosta selection method or directed principal component analysis (DPCA), supervised classification through spectral angle mapper (SAM) method and classification by the land surface temperature (LST) method which finally alteration-zoning map associated with iron mineralization in the studied region was produced.
Aeromagnetic data was acquired in an area of approximately 852.5 km2 in 754 stations with flight lines interspacing of 7.5 km on the alteration zones related to iron mineralization obtained by remote sensing method. Geosoft Oasis montaj software was employed for processing operation and qualitative interpretation of magnetic data via applying various corrections and filters including reduce to pole, upward continuation up to the variety of elevations, low-pass filter, derivative filters containing total horizontal derivative and analytical signal. To simulate and model the magnetic data, the studied area was divided into three-dimensional blocks with dimensions of 125*250*250 meters. At the end, to investigate the trend of magnetic anomalies observed on the surface, determination of approximate shape of the deposit and estimation of its depth, 3-D inverse modeling of the data was carried out using Lee and Oldenberg algorithm by UBC Mag3D 4.0 software.
Discussion and results
In this study, to identify phyllic alteration zones, bands 4, 6, and 7, argillic alteration zones, bands 4, 5, and 7, and propylitic alteration zones, bands 7, 8, and 9 from ASTER sensor was used as input to the component analysis method. The spectral angle mapping algorithm was applied with the data of both ASTER and EO-1 sensors which according to the obtained results, the ASTER sensor was better than the EO-1 to detect iron-related alterations. To calculate LST, radiometric and atmospheric temperature corrections were made on the band 10 ETM8 sensor whereas geometric and radiometric corrections were made on multispectral bands. The magnetometric studies of the region showed that the greatest changes in the intensity of the magnetic field are in the center of the study area and the continuation of these changes is towards the southeast of the area.
Conclusion
Based on the results of the recognition and prospecting phases by remote sensing method and the possibility of iron oxide in the area using airborne magnetometry, making the necessary corrections and applying various processes on the data, the anomaly zones of the area were identified. As a result of the three-dimensional modeling and inversion process of the magnetic data, two large masses located in the center and southeast of the region were identified. The results of the research through integrating two fast and relatively inexpensive methods of remote sensing and airborne magnetometry with 3-D inverse modeling of magnetic data, reveal that Kashan district has a high potential from viewpoint of iron ore-bearing.

کلیدواژه‌ها [English]

  • Land surface temperature method
  • ASTER sensor
  • EO-1 sensor
  • Kashan district
  • 3-D magnetic data inversion

مراجع

-اسدی هارونی، ه.، طباطبایی، س.ح. و رضائی، ر.، 1389. شناسایی و تفکیک زون­های آلتراسیون در محدوده ساری‌گونای با استفاده از داده­های ماهواره­ای TM، چهاردهمین همایش زمین­شناسی ایران و بیست و هشتمین گردهمائی علوم زمین، دانشگاه ارومیه.
-رادفر، ج. و علایی مهابادی، س.، 1372. نقشه زمین­شناسی 1:100000 کاشان، سازمان زمین­شناسی کشور.
-ضیاءظریفی، ا.، 1389. مبانی اکتشافات رادیومتریک ژئوفیزیکی، انتشارات دانشگاه آزاد اسلامی واحد لاهیجان، 312 ص.
 
 
 
-Abera, B.G., 2005. Application of remote sensing and spatial data integration modeling to predictive mapping of apatite-mineralized zones in the Bikalal layered gabbro complex, Western Ethiopia. Master of Science Thesis in Geo-information Science and Earth Observation, Specialisation: Mineral Resource Exploration, Enschede the Netherlands.
-Aliani, F., Dadfar, S. and Maanijou, M., 2013. Detection of alteration zones of Haji Abad iron deposit with (SWIR+VNIR) data of ASTER sensor, Geosciences, v. 24(94), p. 73-80.
-Alilou, S.K., Norouzi, G.H., Doulati, F. and Abedi, M., 2014. Application of magnetometery, electrical resistivity and induced polarization for exploration of polymetal deposits, a case study: Halab Dandi, Zanjan, Iran. 2nd International Conference on Advnces in Engineering Sciences and Applied MathematicsAt: Istanbul, Turkey.
-Armson, D., Stringer, P. and Ennos, A.R., 2012. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban Forestry & Urban Greening, v. 11, p. 245-255.
-Avdan, U. and Jovanovska, G., 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, v. 2016, 8 p.
-Azizi, H., Tarverdi, M.A. and Akbarpour, A., 2010. Extraction of hydrothermal alterations from ASTER SWIR data from east Zanjan, northern Iran. Advances in Space Research, v. 46(1), p. 99-109.
-Baranov, V., 1957. A new method for interpretation of aeromagnetic maps: pseudo-gravimetric anomalies. Geophysics, v. 22(2), p. 359-383. 
-Baranov, V. and Naudy, H., 1964. Numerical calculation of the formula of reduction to the magnetic pole. Geophysics, v. 29, p. 67-79.
-Behnam, S. and Ramazi, H., 2019. Interpretation of geomagnetic data using power spectrum and 3D modeling of Gol-e-Gohar magnetic anomaly. Journal of Applied Geophysics, v. 171, p. 103-129.
-Boloki, N. and Poormirzaee, R., 2009. Using ASTER image processing for hydrothermal alteration and key alteration minerals mapping in Siyahrud, Iran. International Journal of Geology, v. 2(3), p. 38-43.
-Cooper, G.R.J. and Cowan, D.R., 2006. Enhancing potential field data using filters based on the local phase. Computers & Geosciences, v. 32(10), p. 1585-1591.
-Crosta, A.P. and Moore, J.M.C.M., 1989. Enhancement of landsat thematic mapper imagery for residual soil mapping in SW Minas Gerais State Brazil: a prospecting case history in greenstone belt terrain. Proceedings of the 9th Thematic Conference on Remote Sensing for Exploration Geology, Calgary (Ann Arbor, MI: Environmental Research Institute of Michigan), p. 1173-1187.
-Crosta, A.P., De Souza Filho, C.R., Azevedo, F. and Brodie, C., 2003. Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. International Journal of Remote Sensing, v. 24(21), p. 4233-4240.
-Di Tommaso, I. and Rubinstein, N., 2007. Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews, v. 32(1-2), p. 275-290.
-Gupta, H.K. and Roy, S., 2006. Geothermal energy: an alternative resource for the 21st century. ElsevierScience, 292 p.
-Hewson, R.D., Cudahy, T.J., Mizuhiko, S., Ueda, K. and Mauger, A.J., 2005. Seamless geological map generation using ASTER in the Broken Hill-Curnamona province of Australia. Remote Sensing of Environment, v. 99(1-2), p. 159-172.
-Jin, M. and Dickinson, R.E., 2010. Land surface skin temperature climatology: benefitting from the strengths of satellite observations. Environmental Research Letters, v. 5(4), doi:10.1088/1748-9326/5/4/044004.
-Khaleghi, M. and Ranjbar, H., 2011. Alteration mapping for exploration of porphyry copper mineralization in the Sarduiyeh Area, Kerman Province, Iran, using ASTER SWIR Data. Australian Journal of Basic and Applied Sciences, v. 5(8), p. 61-69.
-Kustas, W.P., Li, F., Jackson, T.J., Prueger, J.H., MacPherson, J.I. and Wolde, M., 2004. Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sensing of Environment, v. 92, p. 535-547.
-Lelievre, P.G. and Oldenburg, D.W., 2006. Magnetic forward modelling and inversion for high susceptibility. Geophysical Journal International, v. 166(1), p. 76-90.
-Li, Y. and Oldenburg, D.W., 1996. 3-D inversion of magnetic data. Geophysics, v. 61(2), p. 394-408.
-Liu, S., Hu, X. and Zhu, R., 2018. Joint inversion of surface and borehole magnetic data to prospect concealed orebodies: A case study from the Mengku iron deposit, northwestern China. Journal of Applied Geophysics, v. 154, p. 150-158.
-Maanijou, M., Puyandeh, N., Sepahi, A.A. and Dadfar, S., 2015. Mapping of hydrothermal alteration of Dashkasan (Sari Gunay) epithermal gold mine using ASTER sensor images and XRD analysis. Journal of Geoscience, v. 24(95), p. 95-104.      
-Mohammadzadeh Moghaddam, M., Mirzaei, S., Nouraliee, J. and Porkhial, S., 2016. Integrated magnetic and gravity surveys for geothermal exploration in Central Iran. Arabian Journal of Geosciences, v. 9(7), p. 1-12.
-Phillips, N.D., 2002. Geophysical inversion in an integrated exploration program: Examples from the San Nicolas deposit, Doctoral dissertation, University of British Columbia.
-Prost, G.L., 2002. Remote sensing for geologists: a guide to image interpretation. CRC Press, 326 p.

-Rosid, M.S., Sari, N.I. and Jaman, A.P., 2020. Identification of gold mineralization zone in “GB” field, Jambi, Indonesia using 3D inversion magnetic dataIOP Conference Series: Earth and Environmental Science, v. 481(1), p. 012052.

-Rouse, J.W., Haas, R.H., Shell, J.A. and Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Freden, S.C., Mercanti, E.P., Becker, M.A. (Eds.), Third Earth Resources Technology Satellite-1 Symposium. Goddard Space Flight Center, Washington, D.C.: Scientific and Technical Lnformation Ofice, NASA. p. 309-317.
-Rowan, L.C. and Mars, J.C., 2003. Lithologic mapping in the Mountain Pass, California area using advanced space-borne thermal emission and reflection radiometer (ASTER) data. Remote sensing of Environment, v. 84(3), p. 350-366.
-Sabins, F.F., 1999. Remote sensing for mineral exploration. Ore Geology Reviews, v. 14(3-4), p. 157-183.
-Sobrino, J.A. and Raissouni, N., 2000. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, v. 21, p. 353-366.
-Sobrino, J.A., Jiménez-Muñoz, J.C. and Paolini, L., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, v. 90(4), p. 434-440.‏
-Soe, M., Kyaw, T.A. and Takashima, I., 2005. Application of remote sensing techniques on iron oxide detection from ASTER and Landsat images of Tanintharyi coastal area, Myanmar, Scientific and Technical Reports of Faculty of Engineering and Resource Science, Akita University, v. 26, p. 21-28.
-Taghavi, A., Maanijou, M., Lentz, D. and Sepahi, A.A., 2019. Partial sub-pixel and pixel-based alteration mapping of porphyry system using ASTER data: regional case study in western Yazd, Iran. International Journal of Image and Data Fusion, v. 10(4), p. 300-326.
-Voogt, J.A. and Oke, T.R., 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, v. 86, p. 370-384.
-Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A. and Zhao, S., 2015. An improved monowindow algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data. Remote Sensing, v. 7, p. 42-68.
-Weng, Q., Lu, D. and Schubring, J., 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, v. 89, p. 483-467.
-Williams, N.C., 2008. Geologically-constrained UBC–GIF gravity and magnetic inversions with examples from the Agnew-Wiluna greenstone belt, Western Australia, Doctoral dissertation, University of British Columbia.
-Yuhas, R.H., Goetz, A.F. and Boardman, J.W., 1992. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm, JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop, v. 1, AVIRIS Workshop.
 
 

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