GIS in Geoscience: The recent trends Mining and mineral exploration The use of GIS in mineral exploration is now widespread, allowing the integration of disparate digital datasets into a single, unified database. The recommended approach is to compile all of the available geoscientific data within the GIS in the context of an exploration model in order to produce a mineral potential map. Careful consideration must be given in developing the model so that all of the relevant, important aspects of the deposit being sought are represented. The model is also very important in deciding what weightages to apply to each of these aspects. In the final analysis, these weightages may be arbitrarily applied by a geologist, with an intimate knowledge of the model and the deposit. He also decides which factors related to the deposit are most important, ranging down to those of least importance (a knowledge based approach). Another approach, which is not applicable in all situations, is to use a statistical method in order to decide upon weightages. The final result is a combination of all of the weighted values, producing a map which ranks the study area by degrees of perceived prospects. One of the widely used statistical data integration technique is the Weights of Evidence Method suggested by Bonham-Carter et al. (1989) and Bonham-Carter (1994) in which the quantitative relationships between data sets representing the deposit recognition criteria and known mineral occurrences is analysed using Bayesian weights of evidence probability analysis. In this method the predictor maps are used as input maps and the end product is an output map showing the probability of occurrence and the associated uncertainty of the probability estimates of mineral deposits. In ample number of case examples, this approach has been applied using various GIS packages. GIS is increasingly important in customising and integrating a broad range of exploration data consisting of information on drill holes with summary stratigraphic logs, rock sample and drill hole sample geochemistry, mineral occurrences, magnetic and gravity images, digital geology, current and historic exploration details, roads and railways, localities, parks and reserve forests, restricted areas and integrated bibliography. IIRS has attempted to develop such a system i.e. Mineral Resource Information System, which is a database on mineral deposits, mainly iron and manganese ore deposits of the Iron Ore belt of Keonjhar and Singhbhum regions of Orissa and Jharkhand, India (see box). Similar type of database also exits with much more capabilities and information content like CBMap which is a two-part GIS database that assembles and displays information related to mineral exploration in Central America and the Caribbean Basin. Part 1, The Prospect Database locates and describes over 1000 base and precious metal mines and prospects and the second Part 2, The Land Status Database locates and describes over 2000 mineral concessions, national parks, forest reserves, reservations, and other areas of restricted mineral entry. The data from both the Prospect and the Land Status Databases can be overlaid on a series of detailed base maps including geology, geography, and shaded relief. (www.cbmap.net) 3-D and 4-D GIS The progress of GIS into three dimensions is a revolutionary change for the utility of the technology in oil and gas exploration and production – because depth is such a fundamental consideration. One example can be cited where Earth Science Association (ESA) has exploited ESRI’s extensions to ArcView - Spatial Analyst and 3-D Analyst – to put all of the fields of the Gulf of Mexico in their proper 3-D perspective. By clicking on a field or well in a 2-D map one can see the field or well in 3-D with variables for sands, and wells correctly rendered in 3-D. Also the relevant data can be visualised in 4-D, which can represent series of maps made on a variable that changes over time. For example, it is possible to see monthly or annual maps for oil, gas or water production from a reservoir draped over the 3-D polygon for the reservoir. This is not a simple animation tool – one can still pan and zoom within the viewer and use the ESA Hot Link tool to access Reports, Charts, make Notes or export chosen data to Excel. This technology has great power in quickly examining reservoir performance, identification of permeability barriers and reservoir compartmentalisation. Landslide Hazard Zonation Landslide Hazard Zonation (LHZ) refers to “the division of a land surface into homogeneous areas or domains and their ranking according to degrees of actual / potential hazard caused by mass-movement” (Varnes, 1984). In the recent past various methods and techniques have been proposed to analyse causative factors and produce maps portraying the probability of occurrences of similar phenomena in future. They are as direct and indirect methods. The direct method consists of geomorphological mapping in which past and present landslides are identified and assumptions are made on the factors leading to instability, after which a zonation is made of those sites where failures are most likely to occur. The indirect method includes two different approaches, namely the heuristic (knowledge driven) and statistical (data driven) techniques. In the heuristic approach, landslide-influencing factors such as slope, rock type, landform and land-use are ranked and weighted according to their assumed or expected importance in causing mass movements. In the statistical approach, the role of each factor is determined based on the relationship with the past/present landslide distribution. With the advancement of computing technology, it has become feasible to apply various statistical methods to analyse landslide phenomena and derive at reproducible hazard zonation maps. This is further facilitated by the rapid progress in the field of remote sensing, which provides most authentic information on earth surface features and processes involved. Moreover, information from remotely sensed data can be digitally processed and integrated with other ancillary information using GIS. Recently IIRS has contributed towards a national mission launched at the behest of Cabinet Secretary for landslide hazard mitigation in most critical areas of H.P. and Uttaranchal Himalayas, subsequent to Malpa and Okhimath landslides killing over 300 people in 1998. This project was a joint effort of 11 government departments coordinated by NRSA. The database was generated on 1:25,000 using IRS-LISS-III and PAN merged data products and data integration was carried out in ARC/INFO GIS using customised add-on software modules on Analytical Hierarchy Process (AHP). The hazard degree can be expressed by the Safety Factor, which is the ratio between the forces that make the slope fail and those that prevent the slope from failing. Using one of the simplest models, the so-called infinite slope mode Factor of Safety can be calculated on a pixel basis. For example, the following formula can be easily implemented in any raster based GIS.
in which: C' = effective cohesion (Pa = N/m2). g = unit weight of soil (N/m3). m = Zw/Z (dimensionless). gw= unit weight of water (N/m3). Z = depth of failure surface below the surface (m). Zw = height of watertable above failure surface (m). b = slope surface inclination (°). f' = effective angle of shearing resistance (°). Some parameters here can be taken from laboratory analysis as constants and the depth of failure surface can be taken as the thickness of the sliding material. The depth of the water table can be used to build up different scenarios such as slope stability in completely dry or saturated condition. We can also include the effect of seismic acceleration in the infinite slope model. |
