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GIS in Geosciences

P. K. Champati Ray
Indian Institute of Remote Sensing
4, Kalidas Road, Dehradun – 248001
Email: [email protected]

Geographic Information Systems has seen tremendous growth due to rapid advancements in the field of information technology and availability of spatial information from remote sensors. Over the years GIS has found wide acceptance in various geological applications. The traditional geological applications, which involve visualisation of data, integration of multilayers of spatial information or modelling of geological parameters, have found the utility of GIS to various extents. Although the two/three dimensional data analysis was well-known to geologists in various forms such as mining geological analysis tools and multivariate analysis tools, the advent of GIS has made some of these tools available at a much cheaper cost to all sections of geological community. Thanks to the rapid development of GIS, GPS and RS technology and subsequent commercialisation (popularisation) leading to cost cutting and market expansion. Although the specialised mining geological packages and geostatistical packages still for the specialised geological applications, the commercially available GIS packages have incorporated some of these traditional geological analysis tools such as 3- dimensional analysis, geostatistics and multivariate statistics to cater to the needs of various general-to-special geological application.

Fig 1: Potential zones for Kimberlite Exploration

Geological Map Production
Geological mapping is a scale dependent exercise where various levels of information is presented suitable/possible at the required scale. This essentially means that when a map is prepared from large scale to small scale, some amount of information present at large scale is to be generalised. This generalisation, if done manually, requires quite a bit of human effort and time, which can be saved by utilising some of the basic GIS operations. Secondly, the information available on traditional hard copies, if stored electronically in the form of GIS data layers, can be easily accessible to policy makers or users for decision making and other application such as education, research and exploration. Apart from this, electronically stored data can be easily exchanged and transmitted through Internet. Geological maps need updation in order to include new information coming from subsequent exploration, mapping or technological development. This updation which is a continuous process can be done more efficiently on electronically stored database in GIS. Realising these various organisaions in India including Geological Survey of India., ONGC and many others have set up digital geological databases for mapping and exploration purposes.

Mineral Exploration
Mineral exploration invariably involves integration of geological, geophysical and geochemical data to understand existing deposits and predict new ones. The most of the data sets required for mineral exploration are spatial in nature and therefore, GIS provides an ideal tool to analyse and make prediction. Therefore, various attempts have been made to predict mineral potential areas based on modelling techniques. In one such attempt, Bonham-Carter et al. (1988, 1994) have shown how weights of evidence modelling can be applied in a GIS to predict gold in Nova Scotia. Apart from this, they have shown how to define an influence zone of a linear feature or a point feature using weights of evidence modelling. In another such case study, Khatediya and Joseph (1998) have used fuzzy integration rules to integrate lithological, structural and geochemical data to predict favourable areas for diamond bearing Kimberlites in Raipur of M.P., India (figure 1). Vixo and Bryan (1984) and Guinness and Arvidson (1982) have also made similar attempts to integrate geological and geophysical information related to oil exploration.

Fig 2: Minimum rainfall estimated for water table rise and areas showing negative fluctuation (depletion) in parts of Udaipur district, Rajasthan

Ground water exploration and management
Movement and occurrences of groundwater in any terrain condition can be assessed to a variable extent by spatial modelling of relevant parameters. In a hydrological case study in a drought prone district of West Bengal, it has been demonstrated how GIS helps in demarcating ground water prospective zones (Champati ray et al., 1993). In a similar case study in Imphal valley, 5 prospective zones were identified based on equal weightage integration of data layers such as hydromorphogeology, landuse, water table and electrical conductivity (Singh et al, 1993). Various case studies in different terrain condition have demonstrated thet GIS offers efficient tools to integrate spatial data to demarcate ground water prospective zones and recharge areas.

Fig 3: Groundwater vulnerability analysis using DRASTIC method in GIS

Groundwater quality data layers can also be analysed in GIS to monitor ground water potability. Temporal rainfall and tubewell data can be analysed in GIS to assess the prevailing ground water condition and accordingly stress areas can be identified for further monitoring and drought planning (figure 2). Groundwater, one of the main sources of safe drinking water, is prone to contamination due to possibility of mixing up with toxic elements from chemicals fertilizers, waste disposal sites, and industrial effluent. In a GIS based case study from Nebraska, Rundquist et al. (1991) have demonstrated ” Drastic Mapping”, which is a method to map the likelihood that ground water will become contaminated if a waste source is placed on surface (figure 3). In a similar case study, Cavallin et al (1995) have attempted to calculate groundwater intrinsic vulnerability, lithology and depth to water table using commercial GIS packages (ILWIS and Arc/Info).

Fig 4: GIS based landslide hazard zonation

Landside Hazard Zonation
In the recent past there has been various attempts to prepare landside hazard zonation map using heuristic and statistical in GIS (Carrara et al., 1991;Yan, 1998,van Westen, 1993, Champati ray, 1996). In one such attempt, Champati ray, (1996) have demonstrated that various geoenvironmental parameters such as lithology, structure, slope, aspect, landuse, drainage, road excavations and vegetations cover can be modelled using information value method / weights of evidence modelling/fuzzy logic to prepare landside hazard zonation maps, which indicate around 60-65 % of known landslides in high hazardous area (Figure 4 and 5).

Volcanic Hazards
Active volcanoes pose a significant danger to mankind; therefore, volcanic monitoring and vulnerability assessment are essential to minimize the devastating effect of such unstoppable natural phenomena. Towards this end remote sensing and GIS offer many solutions. In one such attempt for Mount Etna, Sicily (Jones, 1995) a digital hazard assessment model has been developed which accounts for spatial relationship between urban centers on the edifice, with the distribution of lava flows, volcanoclastic deposits, slope> 25 degree, volcanic covers and fault lines. The model represents a high-resolution digital hazard assessment model of the volcano, and identifies previously unrecognized areas of high vulnerability.

Landslide Hazard Zonation using information value method (Sataun Area. HP)

Earthquake Hazard
Various measurements related to earthquakes can be performed in GIS, and the role of GIS in earthquake monitoring and prediction is demonstrated by Towers and Gittings (1995). For example, the magnitude of early earthquakes can be assessed from the extent of the area over which the shock was felt with given intensity, and from the fault rupture and displacement which can then be calibrated against macro seismic information about similar earthquakes for which instrumental data are available. The assessment of the peak horizontal ground acceleration and velocity at certain distance from the causative fault can be emulated by using attenuation law in GIS (Joyner and Boore, 1981; Ambraseys, 1990). This enables to assess the magnitude of disaster well before for different levels of earthquake magnitudes. In this regard, Brabb (1995) has reported for different how a GIS based geological hazard database in California has been used to produce maps, such as a map showing the seismic shaking intensities in a repeat of the 1906 San Francisco earthquake; maps showing cumulative damage potential to different types of buildings; a map showing where earthquake triggered landslides will impact the county during a repeat of 1906 earthquake; map showing debris flow probability; and a map of liquefaction susceptibility.

GIS can be considered as a powerful tool for representation and analysis of spatial information related to geosciences. These not only facilitate multi-map integration using statistical and deterministic modeling techniques. Therefore, it can be concluded that GIS can be utilized to various extents in different types of geoscientific applications.


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