PRELIMINARY DETECTION OF GEOTHERMAL MANIFESTATION POTENTIAL USING MICROWAVE SATELLITE REMOTE SENSING

Atriyon Julzarika, Udhi Catur Nugroho

Abstract

The satellite technology has developed significantly. The sensors of remote sensing satellites are in the form of optical, Microwave, and LIDAR. These sensors can be used for energy and mineral resources applications. The example of those applications are height model and the potential of geothermal manifestation detection. This study aims to detect the potential of geothermal manifestation using remote sensing. The study area is the Northern of the Inverse Arc of Sulawesi. The method used is remote sensing approach for its preliminary detection with 4 steps as follow (a) mining land identification, (b) geological parameter extraction, (c) preparation of standardized spatial data, and (d) geothermal manifestation. Mining lands identification is using Vegetation Index Differencing method. Geological parameters include structural geology, height model, and gravity model. The integration method is used for height model. The height model integration use ALOS PALSAR data, Icesat/GLAS, SRTM, and X SAR. Structural geology use dip and strike method. Gravity model use physical geodesy approach. Preparation of standardized spatial data with re-classed and analyzed using Geographic Information System between each geological parameter, whereas physical geodesy methods are used for geothermal manifestation detection. Geothermal manifestation using physical geodesy approach in Barthelmes method. Grace and GOCE data are used for gravity model. The geothermal manifestation detected from any parameter is analyzed by using geographic information system method. The result of this study is 10 area of geothermal manifestation potential. The accuracy test of this research is 87.5 % in 1.96 σ. This research can be done efficiently and cost-effectively in the process. The results can be used for various geological and mining applications.

Keywords

The Northern of Inverse Arc of Sulawesi; geothermal manifestation; remote sensing; gravity model

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References

ASPRS. (2014). ASPRS Accuracy Standard for Digital Geospatial Data. ASPRS. United States of America.

Barthelmes, F., (2013). Deï¬nition of Functionals of the Geopotential and Their Calculation from Spherical Harmonic Models: Theory and formulas used by the calculation service of the International Centre for Global Earth Models (ICGEM); http://icgem.gfz-potsdam.de/ICGEM/. Scientiï¬c Technical Report STR09/02, Revised Edition, January 2013, Geo Forschung Zentrum Potsdam, DOI 10.2312/GFZ.b103-0902-26, URL http://doi.org/10.2312/GFZ.b1030902-26

Barthelmes, F., (2014). Global Models. In: Grafarend E (ed) Encyclopedia of Geodesy, Springer International Publishing, pp 1–9, DOI 10.1007/978-3-319-02370-0 43-1, URL http://dx.doi.org/10.1007/978-3-319-02370-0 43-1

Barthelmes, F., Kohler, W., (2012). International Centre for Global Earth Models (ICGEM). Journal of Geodesy, The Geodesists Handbook 2012 86(10):932–934, DOI 10.1007/s00190-0120584-1, URL http://dx.doi.org/10.1007/s00190-012-0584-1

Bucha, B., & Janak, J., (2013). Code and readme of a MATLAB-based graphical user interface program for computing functionals of the geopotential. PANGAEA, https://doi.org/10.1594/PANGAEA.808577.

Cataldi, R., (1995). Social acceptance: a sine qua non for geothermal development in the 21st century. Bulletin d'Hydrogiologie No 17 (1999). Centre d'Hydrogtologie, Universiti de Neuchdtel, Editions Peter Lang.

Claessens and Hirt, (2013). Ellipsoidal topographic potential: New solutions for spectral forward gravity modeling of topography with respect to a reference ellipsoid. Journal Of Geophysical Research: Solid Earth, VOL. 118, 5991–6002, doi:10.1002/2013JB010457.

EORC JAXA, (2018). Fundamentals of Remote Sensing. JAXA. Jepang.

Hirt, C., & M. Kuhn, (2012).Evaluation of high-degree series expansions of the topographic potential to higher-order powers, Journal Geophysical Research (JGR) - Solid Earth, in press. doi:10.1029/2 2012JB009492.

Hirt, C., M. Kuhn, W.E. Featherstone & F.Goettl, (2012).Topographic/isostatic evaluation of new-generation GOCE gravity field models, Journal of Geophysical Research - Solid Earth, B05407, doi: 10.1029/2011JB008878

Jin, H., Mountrakis, G., & Stehman, SV, (2014). Assessing Integration of Intensity, Polarimetric Scattering, Interferometric Coherence and Spatial Texture Metrics in PALSAR-Derived Land Cover Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 70-84.

Julzarika, A., (2018a). Penginderaan Jauh untuk Pendeteksian Awal Potensi Tembaga Di Sumbawa. Jurnal RISET Geologi dan Pertambangan, Vol.28, No.1, Juni 2018, 75-89. ISSN 0125-9849, e-ISSN 2354-6638 Ris.Geo.Tam Vol. 28, No.1, Juni 2018 (75-89) DOI: 10.14203/risetgeotam2018.v28.434.

Julzarika, A., (2018b). Mining land identification in Wetar Island using remote sensing data. J. Degrade. Min. Land Manage. 6(1): 1513-1518, DOI: 10.15243/jdmlm. 2018.061.1513.

Julzarika, A., Laksono, DP, Subehi, L., Dewi, EK, Kayat, Sofiyuddin, HA, & Nugraha, MFI, (2018). Comprehensive integration system of saltwater environment on Rote Island using a multidisciplinary approach. J. Degrade. Min. Land Manage. 6(1): 1553-1567, DOI: 10.15243/jdmlm. 2018.061.1553.

Julzarika, A., & Anggraini, N., (2017). Detection of Energy and Mineral Resources Potential in Efficient and Effective using Remote Sensing. Joint Convention Geology-Geophysyc, Petroleum Engineer. Malang.

Julzarika, A., Susanto, & Sutanto, A., (2013). Pengembangan Model Standar Pemanfaatan Data Penginderaan Jauh (Optik dan SAR) untuk Identifikasi Sumber Daya Mineral Tembaga. Laporan Penelitian Inhouse Tahun 2013. LAPAN. Jakarta.

Julzarika, A., (2015). Integration of Height Model using SRTM C, X SAR, Aster GDEM, and ALOS Palsar. Asian Conference on Remote Sensing.

KESDM. (1999). Kepmentamben no 1519.K/20/MPE/1999. KESDM, Jakarta.

Liu, L., Zhou, J., Yin, F., Feng, M., & Zhang, B. (2013). The reconnaissance of mineral resources through ASTER data–based image processing, interpreting and ground inspection in the Jiafushaersu area, West Junggar, Xinjiang (China). J. Earth Science.

Nielsen, A., (2010). Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations. IEEE Transactions on Image Processing (Volume20, Issue: 3, March 2011).

Prasad, K., & Prabhu, GK, (2011). Diag-AID: A Diagnostic Aid for Medical Image Enhancement using Colour Coding and Modified Histogram Equalisation Techniques. International Journal of Medical Engineering and Informatics, 3(3), 223-233.

Rajendran, S., Nasir, S., Kusky, TM, Ghulam, A., Gabr, S., & El-Ghali, MA, (2013). Detection of Hydrothermal Mineralized Zones Associated with Listwaenites in Central Oman using ASTER Data. Ore Geology Reviews, 53, 470-488.

Schölkopf, B., Smola, A., & Müller, KR, (1998). Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10(5), 1299-1319.

Tjahjaningsih, A., Julzarika, A., Sutanto, A., & Nugroho, UC, (2015). Pemanfaatan data penginderaan jauh untuk identifikasi tambang emas di Geumpang Aceh. Laporan penelitian inhouse 2015. LAPAN. Jakarta.

Youssef, AM, Pradhan, B., Sabtan, AA, & El-Harbi, HM, (2012). Coupling of Remote Sensing Data Aided with Field Investigations for Geological Hazards Assessment in Jazan Area, Kingdom of Saudi Arabia. Environmental Earth Sciences, 65(1), 119-130.

Zhou, J., Liu, L., Jiang, D., Zhuang, D., Mansaray LR, & Zhang B., (2013). Targeting Mineral Resources with Remote Sensing and Field Data in the Xiemisitai Area, West Junggar, Xinjiang, China. Journal Remote Sensing, 5(7), 3156-3171.

DOI: http://dx.doi.org/10.30536/j.ijreses.2018.v15.a2772

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