RE: AI-GEOSTATS: validation of Simulation
Hi Stefano, First, I am not sure that stochastic simulation is necessary in your case since you seem to be only interested in what I would call a measure of local (or location-specific) uncertainty. MultiGaussian kriging would in theory give you exactly the same results at less computational cost...of course these days kriging doesn't look as sexy as simulation... To validate the results, I would use the concept of accuracy plot introduced by Clayton Deutch. This concept and others are explained in the paper: Goovaerts, P. 2001. Geostatistical modelling of uncertainty in soil science. Geoderma, 103: 3-26. http://www.terraseer.com/training/geostats/geoder01.pdf that you can download from my webpage. Of course, you might also want to check the reproduction of target statistics, such as histogram and variogram, but again it seems that in your case your focus is on these probabilities of exceeding a particular threshold. I discuss these issues in another publication: Goovaerts, P. 2006. Geostatistical modeling of the spaces of local, spatial and response uncertainty for continuous petrophysical properties. Chapter in book Stochastic Modeling II published by the American Association of Petroleum Geologists... and I could send you a copy if you are interested... An executable to compute the accuracy plot can be downloaded from: http://ekofisk.stanford.edu/SCRFweb/GSLIB/added.html Cheers, Pierre Pierre Goovaerts Chief Scientist at BioMedware Inc. Courtesy Associate Professor, University of Florida President of PGeostat LLC Office address: 516 North State Street Ann Arbor, MI 48104 Voice: (734) 913-1098 (ext. 8) Fax: (734) 913-2201 http://home.comcast.net/~goovaerts/ From: [EMAIL PROTECTED] on behalf of Stefano Pegoretti Sent: Tue 7/25/2006 10:38 AM To: ai-geostats@jrc.it Subject: AI-GEOSTATS: validation of Simulation Hallo! I'm a Ph.D. students who works with Indoor Radon Data, and it's the first time I join this list. I've a question for you: after post-processing several Sequential Gaussian Simulation to obtain a probability map of exceeding a given threshold, can someone suggests me a clever way to validate the results? (of course, I've a second dataset of measurements to use in this part of the work ; variography and sgs do not know this data...) Thanks a lot and best regards, stefano + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
AI-GEOSTATS: geostatistical simulation questions
List: I am interested in doing some geostatistical simulations using GSTAT and have some theoretical questions. I am attempting to model hourly rainfall accumulations over a large region, so there will almost always be zero rainfall somewhere. I can generate random fields of precipitation, using both conditional and unconditional simulations using GSTAT. However, I get negative values as well as (mostly) positive values. The simulated fields otherwise look very reasonable. The data I used (and must use) to estimate my variogram has zero values where no rainfall occurs. What does this suggest to you? I am using gaussian simulation. I have seen some references to more exotic geostatistcal simulation methods using bayesian or some other methods. Is this what I need? From reading the literature, I have seen that some researchers have successfully used indicator kriging and simulation. With GSTAT I can successfully do simulations using 'method : is' rather than 'method : gs', how using GSTAT do I model a continuous (non-binary) variable using the GSTAT syntax? Regards to all, Tom -- Thomas E Adams National Weather Service Ohio River Forecast Center 1901 South State Route 134 Wilmington, OH 45177 EMAIL: [EMAIL PROTECTED] VOICE: 937-383-0528 FAX:937-383-0033 + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
RE: AI-GEOSTATS: geostatistical simulation questions
Hi Thomas, I am assuming that you transform your data before conducting your (sequential?) Gaussian simulation. In this case, the backtransform would yield only positive values, assuming of course that like S-GeMS Gstat asks the user to specify the minimum and maximum of the target histogram. Regards, Pierre Pierre Goovaerts Chief Scientist at BioMedware Inc. Courtesy Associate Professor, University of Florida President of PGeostat LLC Office address: 516 North State Street Ann Arbor, MI 48104 Voice: (734) 913-1098 (ext. 8) Fax: (734) 913-2201 http://home.comcast.net/~goovaerts/ From: [EMAIL PROTECTED] on behalf of Thomas Adams Sent: Tue 7/25/2006 2:40 PM To: ai-geostats@jrc.it Subject: AI-GEOSTATS: geostatistical simulation questions List: I am interested in doing some geostatistical simulations using GSTAT and have some theoretical questions. I am attempting to model hourly rainfall accumulations over a large region, so there will almost always be zero rainfall somewhere. I can generate random fields of precipitation, using both conditional and unconditional simulations using GSTAT. However, I get negative values as well as (mostly) positive values. The simulated fields otherwise look very reasonable. The data I used (and must use) to estimate my variogram has zero values where no rainfall occurs. What does this suggest to you? I am using gaussian simulation. I have seen some references to more exotic geostatistcal simulation methods using bayesian or some other methods. Is this what I need? From reading the literature, I have seen that some researchers have successfully used indicator kriging and simulation. With GSTAT I can successfully do simulations using 'method : is' rather than 'method : gs', how using GSTAT do I model a continuous (non-binary) variable using the GSTAT syntax? Regards to all, Tom -- Thomas E Adams National Weather Service Ohio River Forecast Center 1901 South State Route 134 Wilmington, OH 45177 EMAIL: [EMAIL PROTECTED] VOICE: 937-383-0528 FAX:937-383-0033 + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
