1.-1. Royer and A. Shtuka : Stochastic Imaging of Environmental Data
113
respect of the a priori distribution function and the spatial variability;
possibility of producing probability maps of the estimated values which
can be used in risk analysis protocols;
possibility of using non-parametric estimators (mode, median, .. ) for
estimating the unknown parameters;
• possible extension of the Stochastic Simulation to multivariate problems
(several parameters observed at the same location);
possibility of including constraints or diffusion equations within the
estimator;
8. CONCLUSION
The last point, together with the possibility of implementing additional constraints
such as cross-correlation functions, would make the sequential DSI simulation a
promising and interesting technique for future development, especially in
environmental applications where the Stochastic Simulation by Indicator can be
helpful for interpreting ecodata particularly in the following areas:
risk analysis of natural hazards
water resource monitoring
overflow forecasting
pollution monitoring
waste disposal
•
soil contamination problems
KEYWORDS
Risk assessment, stochastic modeling, mathematical model, geostatistic, simulation
GOCAD, pollution, environment.
REFERENCES
[1]
Deutsch C.V. and lournel A.G., GSLIB: Geostatistical Software Library and User's Guide.
Oxford University Press, New York, 340p. (1992).
[2]
Dowd P.A., A review of recent developments in Geostatistics. Computer and Geosciences,
Vo!. 17.10, 1481-1500 (1992).
[3]
Isaaks E.H. and Srivastava R.M., An Introduction to Applied Geostatistics. Oxford
University Press, 561 p. (1989).
[4]
lournel A.G., Geostatistics: Models and tools for the earth sciences. Mathematical Geology,
18(1): 119-140 (1986).
[5]
lournel A.G., Fundamentals of Geostatistics in Five Lessons. American Geophysical Union,
Washington, D.C. (ISBN 0-87590-708-3) (1989).
[6]
lournel A.G. and Huijbregts CH. 1., Mining Geostatistics. Academic Press, New York (1978).
[7]
lournel A.G., The indicator approach to estimation of spatial distributions. 793-806, AIME,
17th APCOM Symposium (1982).
[8]
lournel A.G., Non-parametric estimation of spatial distributions. Math. Geo!., 15:445-468
(1983).
113
respect of the a priori distribution function and the spatial variability;
possibility of producing probability maps of the estimated values which
can be used in risk analysis protocols;
possibility of using non-parametric estimators (mode, median, .. ) for
estimating the unknown parameters;
• possible extension of the Stochastic Simulation to multivariate problems
(several parameters observed at the same location);
possibility of including constraints or diffusion equations within the
estimator;
8. CONCLUSION
The last point, together with the possibility of implementing additional constraints
such as cross-correlation functions, would make the sequential DSI simulation a
promising and interesting technique for future development, especially in
environmental applications where the Stochastic Simulation by Indicator can be
helpful for interpreting ecodata particularly in the following areas:
risk analysis of natural hazards
water resource monitoring
overflow forecasting
pollution monitoring
waste disposal
•
soil contamination problems
KEYWORDS
Risk assessment, stochastic modeling, mathematical model, geostatistic, simulation
GOCAD, pollution, environment.
REFERENCES
[1]
Deutsch C.V. and lournel A.G., GSLIB: Geostatistical Software Library and User's Guide.
Oxford University Press, New York, 340p. (1992).
[2]
Dowd P.A., A review of recent developments in Geostatistics. Computer and Geosciences,
Vo!. 17.10, 1481-1500 (1992).
[3]
Isaaks E.H. and Srivastava R.M., An Introduction to Applied Geostatistics. Oxford
University Press, 561 p. (1989).
[4]
lournel A.G., Geostatistics: Models and tools for the earth sciences. Mathematical Geology,
18(1): 119-140 (1986).
[5]
lournel A.G., Fundamentals of Geostatistics in Five Lessons. American Geophysical Union,
Washington, D.C. (ISBN 0-87590-708-3) (1989).
[6]
lournel A.G. and Huijbregts CH. 1., Mining Geostatistics. Academic Press, New York (1978).
[7]
lournel A.G., The indicator approach to estimation of spatial distributions. 793-806, AIME,
17th APCOM Symposium (1982).
[8]
lournel A.G., Non-parametric estimation of spatial distributions. Math. Geo!., 15:445-468
(1983).