Morphological discrimination amongst geological rock surfaces of Zagros thrust belt via SAR backscattering modelling

Document Type : Original Article

Authors

1 School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran

2 Center of Excellence on Applied Electromagnetic Systems, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

3 Dept. of Geology, Exploration Directorate of National Iranian Oil Company, Tehran, Iran

Abstract

Nowadays, processing and interpretation of remote sensing satellite images is the only method of surface
geological rock surfaces mapping. This doubtlessly requires time-consuming field observations for
complementary morphological information, i.e. field measurements in geomorphology is unavoidable since
the hyper-spectral images that are used for geological mapping do not discriminate the lithologies texture
and cannot be used to determine the geological morphology. However, due to the impassable and fault cliffs,
comprehensive field operations within a geological map is almost impossible. Microwave or radar remote
sensing via Synthetic Aperture Radar (SAR) images is capable of obtaining the surface morphology and
alteration zones discrimination based on lithologies texture. To fulfill this aim, the Integral Equation Model
(IEM), which has been proposed by Fung et al. (1992) and has been developed and improved several times,
seems to be the most outstanding method being adopted to model the SAR backscattering coefficient against
the surface roughness. Nonetheless, it needs to be asserted that the Euclidean calculation of this parameter
is not capable enough to measure the morphology of a feature. In this paper, using the power-law geometry
capability, one can improve the alteration zones discrimination. To implement and evaluate the proposed
method of geomorphological mapping, IEM ๐œŽ° results for a region on the Zagros fold-thrust belt, in western
Iran, were compared with the satellite SAR backscattering data in the L-band (i.e. ALOS-PALSAR) and the
X-band (i.e. TerraSAR). Besides, the efficiency of the SAR data processing versus the geological field
observations provide an average of more than 20% improvement in terms of the power-law geometry in
comparison with the Euclidean geometry. Although this improvement for moderate rough formations is less
than 3% at high frequency (X-band), it is about 30% for rough formations at low frequency (L-band).

Keywords


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