AI-GEOSTATS: Samples in a block
Hi all I have a number of questions to all out there..any help/ pointers would be appreciated. 1. What is the optimum number of samples in a block of any particular size? What I have been able to track down so far has not really been helpful, this includes comments like: Samples should be spaced at half the first range...of the semivariogram samples should be spaced in such a way as to reduce the Krige variance etc. All of these appear to pre-suppose that you already have sampling. Is there any way that I can work out the theoretical number of samples in an e.g. 30x30m block assuming some a priori information (gold deposit, high nugget of e.g. 1.2 e6, pop.var having the same type of magnitude etc) ? 2. Does anyone know of a good idiots guide to GSLIB? I have bought the manual, however I have not found it particularly helpful for a first time user of the software (I want to start simulations of the ore body that I work on, prior to us commencing a capital intensive exploration programme). 3. Is there a good declustering programme out there? (This gets back to question 1), when developing the ore body the sample spacing is approx. 2.5m, when production commences the sample spacing changes to a 5x5 grid (ideally, this never really happens in actuality). What I want to do is to try and overcome the change of support issue, declustering is the only way that I can think of at the moment. 4. Does anyone know where I can get hold of a good ( preferably introductory) text on Probability Kriging? and is there any software that has been designed to run this? Thanks to all Yours Mark Burnett Ore Reserve Manager Deelkraal Gold Mine ** This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error please notify the system manager. This footnote also confirms that this email message has been swept by MIMEsweeper for the presence of computer viruses. www.netcom.co.za ** -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
Re: AI-GEOSTATS: Samples in a block
1. What is the optimum number of samples in a block of any particular size? Is there any way that I can work out the theoretical number of samples in an e.g. 30x30m block assuming some a priori information (gold deposit, high nugget of e.g. 1.2 e6, pop.var having the same type of magnitude etc) ? This part I can answer on the general mailing list (I think). Use the free unlimited use downloadable Kriging Game to be found on my pages at http://uk.geocities.com/drisobelclark/briefcase.html This package reads Geostokos type files, Geo-EAS type files, CSVs dumped from spreadsheets or you can type in data from the keyboard. Comments and queries to me please. Isobel Clark Do You Yahoo!? Get your free @yahoo.co.uk address at http://mail.yahoo.co.uk or your free @yahoo.ie address at http://mail.yahoo.ie -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
AI-GEOSTATS: PhD-position
Do you like (or know someone who likes): AMSTERDAM GEOSTATISTICS KALMAN FILTERING SOIL HYDROLOGY A POSITION AS A PHD-RESEARCHER then please read on. At the Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, we currently have a vacancy for a PhD-researcher on the project Incorporating process-knowledge in spatio-temporal kriging of soil variables. The aim of this project is to build and apply statistical models that can satisfactorily describe and predict the behaviour of soil variables over space and time. And that do so in a way that observations and physical process knowledge are both exploited. We believe that the direction to go forward is that of space-time Kalman filtering. Much more information on this project (such as the full project proposal) can be obtained from Gerard Heuvelink ([EMAIL PROTECTED], telephone (+)31 20 5257448, http://www.frw.uva.nl/soil/Welcome.html). We are seeking candidates that have finished a Masters in the (natural) sciences, preferably in the Earth Sciences, (Applied) Mathematics, Statistics or Econometrics. The person we seek loves to work with equations and loves to put them into computer code, but must at the same time find much motivation and inspiration from applying mathematical models to real world problems. Of course the project should lead to a PhD-thesis. The project lasts for four years, and must commence before the end of this year. Part of the project is a three months working visit to partners in the USA and/or Australia in the second year of the project. The monthly salary starts at about 1450 (currently 1 equals about 0.91 US$) in the first year to about 2050 in the fourth. Applications (letter and curriculum vitae) must arrive BEFORE 1 OCTOBER 2001. Please send your application to: Dr. Gerard B.M. Heuvelink IBED-Physical Geography Universiteit van Amsterdam Nieuwe Achtergracht 166 1018 WV Amsterdam THE NETHERLANDS Application through E-mail or FAX is also accepted: [EMAIL PROTECTED] / (+)31 20 5257431 -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
AI-GEOSTATS: Samples in a block
Hi Mark, it seems you have already realized the paradox of sampling in geostatistics: the more you know about the variable in question, the better you can optimize a sampling scheme for it. It isn't easy to break out of this paradox, and that's probably the reason that sampling has received relatively little attention in the geostatistical literature. You will not find much about it in most textbooks. You have probably already found a number of papers by Webster and McBratney from the beginning of the 80's (mainly in the Journal of Soil Science, I think). They described an algorithm for calculating the optimum grid spacing for a sampling scheme, given the maximum allowed kriging variance and a variogram. These papers, although relatively old, are still often quoted. Another paper from those days dealing with the optimal type of grid is Yfantis, E.A., Flatman, G.T. and Behar, J.V., 1987. Efficiency of kriging estimation for square, triangular and hexagonal grids. Mathematical Geology, 19(3): 183-205. I normally don't like to advertize my own work this much, but hey this was my Ph.D. thesis. I developed a simulated annealing - based algorithm that (among other things) optimizes for the same criterion as the Webster/McBratney papers, but that optimizes the optimal location of individual points, rather than optimal grid spacing. Although this might not be very useful in large, contiguous sampling areas, it considerably improves your sampling efficiency when you already have preliminary samples and/or many sampling constraints. Again, you need (to assume) a variogram. A couple of references to my work: -Van Groenigen, J.W. and Stein, A., 1998. Constrained optimization of spatial sampling using continuous simulated annealing. Journal of Environmental Quality, 27(5): 1078-1086. -Van Groenigen, J.W., Siderius, W. and Stein, A., 1999. Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma, 87: 239-259. -Van Groenigen, J.W., Pieters, G. and Stein, A., 2000. Optimizing spatial sampling for multivariate contamination in urban areas. Environmetrics, 11: 227-244. Also, you can download a preliminary software implementation of this algorithm from my website (see below). Of course, there is a lot a controversy in the geostatistical community about the use of kriging variance as a measure for interpolation error, since it does not take into account the actual values of the measured variable, which can give you problems when the intrinsic hypothesis doesn't hold (and it often doesn't). Although this has some truth to it, my philosophy is that that is exactly what makes it interesting for sampling optimization, since you don't have those values before sampling anyway However, the last of my references used an optimization criterion that doesn't involve kriging variance. Cheers, Jan Willem. ** Jan Willem van Groenigen University of California - Davis Dept. of Agronomy and Range Science 1 Shield Avenue Davis, CA 95616 - 8515, U.S.A. -- e-mail: [EMAIL PROTECTED] http://agronomy.ucdavis.edu/groenigen tel. (530) 752-3457 fax. (530) 752-4361 * -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
AI-GEOSTATS: re:samples in a block
Unfortunately as Mark has noted, if nothing is known then one will have to design a sampling plan using other criteriia, i.e., essentially meaning non-statistical criteria. Even if there were no spatial correlation and one only wanted to estimate the average value for the block one would need the variance. In such a case one might iterate, i.e., randomly select sample locations (the number being determined perhaps by budget and difficulty of sampling). After sampling at those locations, compute the sample variance and use this to predict a sample size (which will almost certainly be larger than the original size). One can of course use the data obtained at this stage to estimate the mean and also obtain a confidence interval, the new sample size will be related to the desired confidence interval width and desired confidence level. Having determined a new sample size, repeat the process (combining the original sample with the new data would not correspond to random sampling), if after several iterations it appears that the predicted sample size stablizes then you are through. Note that this process only focuses on the number of sample locations, the actual locations being selected randomly. This process can be improved on by using the theory of sequential sampling. With respect to geostatistics, we need to remember that the data (in common practice) is used for two different purposes.The first being the estimation/modeling of the variogram or covariance function, the second being the kriging (of whatever form or type). An optimal sampling plan for the one will generally not be an optimal plan for the other. There is an old issue of GEOSTATISTICS, the NACOG newsletter, that has a long list of papers on sampling design. A number of papers have been written since then as well. There is no definitive answer to the question since it depends on the question, i.e., what is the data to be used for? Donald E. Myers Department of Mathematics University of Arizona Tucson, AZ 85721 http://www.u.arizona.edu/~donaldm -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
