• M. Csörgö, Carleton University

We are currently incorporating Wiener and Brownian bridge processes into our work.

• Geological Survey of Canada,
• International Institute of Aerospace Survey and Earth Sciences
• Science University of Tokyo
• Italian Reasearch Council
• British Columbia Geological Survey
• University of Lisbon
• University of Cantrabria
• European Union Environment and Climate Programme

There has been a long history of prediction modeling at the Geological Survey of Canada (GSC). The models which have been developed at the Spatial Data Analysis Laboratory use the favourability function approach based on three mathematical frameworks; probability theory, Dempster-Shafer Evidential Theory and Zadeh's Fuzzy Set Theory. To measure the applicability of a particular model, prediction rates rather than success rates are used. The cross validation methods test for time and space robustness.

The methodology has been used in the Tsitika Creek area of British Columbia in Canada and the Fanhoes-Trancao area of Portugal. To properly interpret the results of a prediction model, the original data must be understood; for this purpose, we use data mining.

• Geological Survey of Canada,
• Alberta Geological Survey

This Open File presents in integrated and geographically co-registered format, seven recently-released digital earth science datasets: bedrock geology, mineral occurrences, 7-band LANDSAT TM data, ERS-1 radar data, radiometric data, magnetic data and lake sediment geochemical data for an area covering part of NTS 74M. It presents these data in image (raster) formats resulting from analysis and integration using GSC-developed programs and PCI® software.

• Geological Survey of Canada

The Spatial Data Analysis Laboratory has developed a software package for producing quantitative prediction models for landslide hazard mapping. In the models, we assume that:

• future landslides will occur under circumstances similar to the ones of past landslides in either the study area or in areas in which experts have obtained their knowledge on the relationships between the casual factors and the occurrences of the landslides; and
• that the spatial data representing the causal factors contained in the GIS database can be used to formulate the future landslide hazard.

There are four steps to producing a quantitative spatial prediction model:

1. preprocessing raw data,
2. obtaining data from a GIS database,
3. the modeling itself and
4. cross validation procedures.

The methodology has been used in Colombia, Canada, Peru, Spain and Portugal.

• Geological Survey of Canada
• PCI Geomatics Inc.

Programs were developed at the Spatial Data Analysis Laboratory for integration and analysis of geoscience spatial data. The programs are for use with PCI's EASI/PACE Image Analysis software. Included are a sample exercises and a roadmap of the software.

• Geological Survey of Canada,
• New Brunswick Department of Mines and Energy

As part of EXTECH II, a state of the art 200m line spacing helicopter-borne magnetic,electromagnetic and radiometric survey was carried out in the Bathurst mining camp in N.B., Canada. A prediction model to identify exploration targets for Volcanogenic Massive Sulphide (VMS) deposits has been developed based on the difference between the distribution functions of geophysical survey data of the study area and of mineralized zones.

To evaluate the prediction results of the potential map, we performed a "cross-validation" technique using 37 known VMS deposits in the camp. From the results of the cross-validation analysis, we would have "discovered" 17 (46%) of the 37 deposits, if we selected the 1% of the study area (40 km2) in the potential map which represents the highest relative potential for VMS deposits as an exploration target. Similarly, if the top 5% area was selected as the target area, then 24 (65%) of the 37 deposits would have been "discovered".