First, the topographical maps were scanned and then georeferenced in ERDAS Imagine 8.4 to a Lambert Conformal Conic projection system, as had been used for the previous geophysical surveys during seismic acquisition. Then a mosaic of the topographical maps was generated. Settlements, roads, drainage, topographic contours and other geographic information were then digitized from this mosaic of topographical maps. The geological atlas was then scanned and georeferenced in ERDAS Imagine 8.4, also to the appropriate projection system. Reference points were selected from the same maps.

The IRS 1-D PAN and LISS-III images, both obtained from NRSA, Hyderabad, were then also georeferenced to the appropriate projection system in ERDAS Imagine 8.4. Ground Control Points (GCPs) were selected from the mosaic of georeferenced topographical maps and GPS surveys in the field, after the GPS GCPs were matched with the GCPs obtained from the topographical map. By combining Bands 4, 3 and 2 of the LISS-III image, a false colour composite (FCC) was generated. Spatial enhancement was also done to the IRS 1-D PAN and LISS-III images. To take advantage of the spatial resolution and spectral resolution of the IRS 1-D PAN and LISS III images, respectively, both the images were “resolution merged” together to obtain a multi-spectral image having higher spatial resolution. Digital Image Processing was carried out in ERDAS Imagine 8.4. A modified geological atlas (Figure 2) was generated based on interpretations from the multi-spectral image having higher spatial resolution, following image radiometric enhancements. All the information in the georeferenced geological atlas (lithological boundaries as polygon features, faults and axial traces of folds as polyline features and bedding plane dip-strike data as point features) were digitized in ArcGIS 8.1. The road and railway network, settlement, drainage of the region was also updated using the merged satellite image. A land use map was also generated from the merged image following image classification.

The topographic contours and elevation points were interpolated using the Triangulated Irregular Network (TIN) method to generate the Digital Elevation Model (DEM) (Figure 3a), slope map (Figure 3b) and aspect map of the study area. In order to generate Digital Terrain Models (DTMs), the LISS-III, PAN and merged images were draped over the DEM. Figure 3c is a DTM generated by combining the LISS-III image with the DEM. These analyses were carried out in ArcGIS 8.1 using the 3-d Analyst module.

Similarly, all the time and depth structure contour maps of various stratigraphic horizons were interpolated using TIN to produce digital models for better 3-dimensional visualization (Figures 4a and b). A structural dip domain map was also created by IDW (Inverse Distance Weighted) interpolation technique using fault traces and fold axial traces as barriers (Mishra et al. 2002). Based on the X-Y coordinates, well locations and seismic lines were constructed and overlaid on top of all the layers. These steps were also carried out in ArcGIS 8.1.

Results and Discussion
Besides the digitization of settlements, roads, railway network, drainage, topographic contours, well locations, seismic line locations, oil and gas pipelines, etc., the following outputs have been generated using the various data sets and methods described above:

1. Merged multi-spectral higher resolution remote sensing data
2. Modified geological map
3. Structural dip domain map
4. Land use map
5. Digital Elevation Model, slope map and aspect map
6. Digital Terrain Models
7. Pseudo 3-dimensional models of stratigraphic horizons

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