[ai-geostats] Re: Kriging Small Blocks
Jul The warning about kriging small blocks is about small relative to the sampling density. For example, less than about one-third of the grid spacing. The warning is the same as the one about 'point' kriging (mapping) The map is too smooth - or, at least, a lot smoother than the real surface would be. High value areas will be under-estimated and low value areas will be over-estimated. If your objective in kriging is to obtain general maps of an area with an idea of where the highs and lows are, then ordinary kriging is sufficient. The over- and under- estimations cancel out on average. In mining applications, where block kriging originated, most applications require a 'cutoff', where values below a certain value are not included in the 'plan'. In this case, mapping or estimating small blocks will result in an over-estimation of 'payable' ground and an under-estimation in average value. In pollution or environmental applications, the areas at risk will be under-estimated as will the true toxicity or risk factors. There are two major ways round this problem: (1) use a non-linear kriging approach such as disjunctive kriging or the multivariate gaussian. Ed Isaacs and Mohan Srivastava's book is th ebest reference for the latter. Rivoirard's book for DK. (2) simulation. There are lots of simulation methods around, which allow you to 'put back the roughness' and get an idea how bad the problem might be. GSLib is pretty good on this. Isobel http://geoecosse.bizland.com/course_brochure.htm If, as in mining, you wish to apply some sort ___ALL-NEW Yahoo! Messenger - so many all-new ways to express yourself http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] Fractals Semivariance
Title: Message Hello Syed, I washoping a reply from you :) I didn't think aboutthe problematic of anisotropy and thepotential use of ratios of fractal dimensions. It might be worth some further investigation. The physical meaning of fractals derived from directional variograms is tricky indeed. I was wondering if the average of all these fractal dimensions would be formally equal to the fractal dimension derived from omnidirectional variogram. My first guess wouldbe yes, but this would depend on the angular tolerance of the directional variograms. And would the average value of the fractal dimension have any reasonable physical meaning? Any experience with this? Thanks again for the kind help. Gregoire -Original Message-From: Syed Abdul Rahman Shibli [mailto:[EMAIL PROTECTED] Sent: 16 July 2004 19:23To: Gregoire DuboisCc: [EMAIL PROTECTED]Subject: Re: [ai-geostats] Fractals SemivarianceNot sure how anisotropic "fractal" spatial correlation models would fit in the whole scheme of things. You're essentially assuming a power law model (Brownian motion) to model the spatial correlation, which implicitly assumes a phenomena with an infinite capacity for dispersion, i.e. no range. The ratio of two fractal dimensions is not necessarily the same as the ratio of two ranges in the two directions of maximum and minimum continuity, which is the traditional measure of "anisotropy".However, practically speaking you can still calculate experimental variograms for two, three, or four separate directions and derive the log-log estimate of the fractal dimension from these separate variograms. I wouldn't know what this will physically mean, except to say that I have a phenomena with different capacities for dispersion in different directions. CheersSyed Dear all,athttp://www.umanitoba.ca/faculties/science/botany/labs/ecology/fractals/measuring.htmlone can read the following"The fractal dimension is estimated separately for each profile from the log-log plot of cell count against step size (D = 2 - slope, where 1 = D = 2). The average of these values plus one provides an estimate of the surface fractal dimension."Burrough's method (using the slope of the log-log plot of the semivariogram to calculate the fractal dimension of 1 dimensional transect or profile) could thus be extended to a 2 D case (a surface). Has anyone references discussing the use of Burrough's method when applied to a 2 D case?Unless one considers the investigated phenomenon completely isotropic, averaging the fractal dimensions derived from the slopes of directional log-log semivariograms may not provide any useful/reliable information.Has someone on the list any experience with this kind of issue?Thanks very much for any help.Best regards,GregoirePS: I know there are other techniques to calculate the fractal dimension ofa surface but I'm only interested in those involving the computation of thesemivariance.__Gregoire Dubois (Ph.D.)JRC - European CommissionIES - Emissions and Health UnitRadioactivity Environmental Monitoring groupTP 441, Via Fermi 121020 Ispra (VA)ITALYTel. +39 (0)332 78 6360Fax. +39 (0)332 78 5466Email: [EMAIL PROTECTED]WWW: http://www.ai-geostats.orgWWW: http://rem.jrc.cec.eu.int* By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm )* To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED]Signoff ai-geostats * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
[ai-geostats] sand channel modeling
Hi All, I am at the beginning of modelling fluvial sand channels using subsurface well data (logs cores). I would appreciate if you could send me some tips on the optimum way to do this using object modelling and indicator techniques. Are there any published work dealing with the geometrical parameters (e.g. thickness, width, length, amplitude etc.). Is there some cross plots relating channels body thickness measured from the well data to width? Regards, ___ Salah el-Shazly (DSC/92) Geologist Consultant, PDO Study Centre Petroleum Development Oman- POBox: 81, PC 113 Tel: (968) 674135; FAX: (968) 691470; Mobile: (968) 9898527 * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] sand channel modeling
Object-based models where first applied to fluvial channels, becouse the "simplicity" of their geometries, there is a lot of papers published regarding this subject: Journel, A. G., R. Gundeso, E. Gringarten, and T. Yao. 1998. Stochastic modelling of a fluvial reservoir: a comparative review of algorithms. Journal of Petroleum Science and Engineering 21. Deutsch, C. V., and T. T. Tran. 2002. FLUVISIM: a program for object-based stochastic modeling of fluvial depostional systems. Computers and Geosciences 28:525-535. Holden, L., R. Hauge, O. Skare, and A. Skorstad. 1998. Modeling of fluvial reservoirs with object models. Mathematical Geology 30:473-496. Wich soft do you use? Regards Oriol "Shazly, Salah DSC92" wrote: Hi All, I am at the beginning of modelling fluvial sand channels using subsurface well data (logs cores). I would appreciate if you could send me some tips on the optimum way to do this using object modelling and indicator techniques. Are there any published work dealing with the geometrical parameters (e.g. thickness, width, length, amplitude etc.). Is there some cross plots relating channels body thickness measured from the well data to width? Regards, ___ Salah el-Shazly (DSC/92) Geologist Consultant, PDO Study Centre Petroleum Development Oman- POBox: 81, PC 113 Tel: (968) 674135; FAX: (968) 691470; Mobile: (968) 9898527 * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats -- __ Oriol Falivene [EMAIL PROTECTED] http://www.ub.es/ggac tel. (+34) 93 4021373 fax (+34) 93 4021340 Fac. de Geologia, Univ. de Barcelona * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
[ai-geostats] Re: Kriging Small Blocks
Nicolau I was talking about kriging before cutoff is applied. If the cutoff is applied to the block estimates my comments stand. If you aply the cutoff to your data first and then krige, you get the opposite problem, because you will over-estimate every value and under-estimate the tonnage. My point (1) is that, if you wish to avoid conditional bias in your kriging, you could consider using a non-linear kriging method such as those mentioned. I have no experience with either, since I follow a different route in the correction of conditional bias in mineral resource estimation. Isobel http://geoecosse.bizland.com/whatsnew.htm --- [EMAIL PROTECTED] wrote: Isobel, So for mining purposes can't we just krige before applying the cut-off criteria? I mean, for most mining applications one will prefer to have a more realistic geologic block model and will always have the chance to evaluate his/her panels under the appropriate cut-off criteria, but applying that criteria after estimating small blocks, right? Could you please explain your point in solution (1) below? Thanks for indicating the literature. Thanks Nicolau Barros Engineer Mine Planning and Production Control Department Mineração Rio do Norte S.A. [EMAIL PROTECTED] +55 (93) 549 8215 Confidencialidade Esse e-mail e possíveis anexos podem possuir informações confidenciais e de interesse somente do destinatário. Portanto, se você recebeu esta mensagem por engano, favor comunicar imediatamente o remetente e deletá-la logo em seguida. Esteja ciente que o uso indevido do conteúdo das informações em questão é estritamente proibido. Confidentiality This message and any possible attached files may contain confidential information and only for interest of the intended recipient. If you have received this message by mistake, please notify the sender and delete the message immediately. Be aware that the unauthorized use of the above-mentioned information is strictly forbidden. -Mensagem original- De: Isobel Clark [mailto:[EMAIL PROTECTED] Enviada em: segunda-feira, 19 de julho de 2004 05:23 Para: [EMAIL PROTECTED] Cc: [EMAIL PROTECTED] Assunto: [ai-geostats] Re: Kriging Small Blocks Jul The warning about kriging small blocks is about small relative to the sampling density. For example, less than about one-third of the grid spacing. The warning is the same as the one about 'point' kriging (mapping) The map is too smooth - or, at least, a lot smoother than the real surface would be. High value areas will be under-estimated and low value areas will be over-estimated. If your objective in kriging is to obtain general maps of an area with an idea of where the highs and lows are, then ordinary kriging is sufficient. The over- and under- estimations cancel out on average. In mining applications, where block kriging originated, most applications require a 'cutoff', where values below a certain value are not included in the 'plan'. In this case, mapping or estimating small blocks will result in an over-estimation of 'payable' ground and an under-estimation in average value. In pollution or environmental applications, the areas at risk will be under-estimated as will the true toxicity or risk factors. There are two major ways round this problem: (1) use a non-linear kriging approach such as disjunctive kriging or the multivariate gaussian. Ed Isaacs and Mohan Srivastava's book is th ebest reference for the latter. Rivoirard's book for DK. (2) simulation. There are lots of simulation methods around, which allow you to 'put back the roughness' and get an idea how bad the problem might be. GSLib is pretty good on this. Isobel http://geoecosse.bizland.com/course_brochure.htm If, as in mining, you wish to apply some sort ___ALL-NEW Yahoo! Messenger - so many all-new ways to express yourself http://uk.messenger.yahoo.com ___ALL-NEW Yahoo! Messenger - so many all-new ways to express yourself http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] Sample sizes for point pattern analyses
Dear Mike. In point pattern analysis, your sample is supposed to be a homogeneous thinning of a more global underlying point process, and is therefore expected to share same characteristics. Usually Monte Carlo simulations are involved to test null hypothesis. The less point you have the less robust your test will be: each significant rejection of the null hypothesis are about to be generalized to the underlying process, whereas no conclusions have to be made about non-significant results (since larger sample could have lead, or not, to significant rejection of H0). Of this, what i understood was that no real minimum sample size can be defined. I found the manual of P.J. Diggle 2003 a valuable and clear source of information about points patterns : Diggle, P. J. (2003). Statistical analysis of spatial point patterns (2d edn). London, UK: Arnold. Hope it would help. sheers. Joseph. -- Joseph Le Cuziat PhD Student IMEP - ECWP --- Mike Saunders [EMAIL PROTECTED] a écrit : I have been surfing the internet and looking through a few older spatial spatistics books and could not find any recommendations on minimum sample size for point pattern analyses, specifically the Ripley's K(d) function. Is there a source citing this somewhere? Thanks, Mike R. Saunders Research Associate Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of Maine * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats = -- Joseph LE CUZIAT IMEP, FST St Jérôme, case 461, 13397 Marseille cedex 20, FRANCE ECWP, Province de Boulemane, BP 47 Missour, MAROC Créez gratuitement votre Yahoo! Mail avec 100 Mo de stockage ! Créez votre Yahoo! Mail sur http://fr.benefits.yahoo.com/ Dialoguez en direct avec vos amis grâce à Yahoo! Messenger !Téléchargez Yahoo! Messenger sur http://fr.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] a questions of using GSTAT to do Gaussian Conditional Simulation!
Feng Liu wrote: Hi, Dear List: I am trying to use Gstat to do Gaussian Conditional Simulation on my soil C content data. I got3 questions as following: 1.If I use "set nsim=100" to do 100 times simulation, does the simulations follow a single random path or they will follow 100 different random paths? What should I do if I want to let them follow different random paths. Single path; run the process with nsim=1; 100 times to get 100 different paths (and rename the output each time). 2. Is it possible for me to write all the results of 100 simulations to one single file? How? Not with maps (maps are univariate), you can when using ascii column files with prediction locations; 3. Gstat needs gnuplot to do the plot, but I could not get gnuplot compiled and start in windows, could you please tell me how to do it if you have experience on this. Install gnuplot, and make sure that a binary called gnuplot is present in the search path; the gnuplot folks tend to call it gnupltw32.exe or something like that; see also the set gnuplot = 'xxx'; command in gstat files. Also, I couldn't agree more with Ernesto: I find myself using gstat inside R or S-Plus almost 100% of the time I spend using gstat. -- Edzer * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] Re: Kriging Small Blocks
Hi I think the discussion below is missing some key points: 1. If the individual block estimates are to be used for actual selection at the time of mining, then conditional bias will impact the predicted recoveries and should be minimized. 2. However, if the block model is to be used for long term mine planning, the preparation of production schedules etc. etc., then it is unlikely that these same block estimates will be used for selection at the time of mining. In this scenario, it is sufficient to know the distribution of block grades within a mining period such as annual, semi-annual, or quarterly, etc. and conditional bias is irrelevant. 3. Now here is the rub. One cannot accurately estimate the distribution of block grades within a mining period without invoking conditional bias unless each block estimate is perfect, e.g., no error! If you read Michel Davids, Geostatistical Ore Reserve Estimation you will find that he also points out this apparent contradiction (page 313 section 11.3.2) The apparent contradiction is: 1. If the block grades are conditionally unbiased, then the distribution (histogram) of block estimates is necessarily smoothed. Thus, the prediction of in situ tones and grade above cutoff is inaccurate (biased)! 2. If the histogram of estimated block grades yields the correct in situ proportions and grades above cutoff (for all cutoff grades), then the block estimates are necessarily conditionally biased. I often refer to this as the kriging Oxymoron, and it appears to be very poorly understood with in the geostat community. Even Dr. Krige wrongly claims that conditional bias should be removed or minimized in a long term mine planning model, when in fact it is irrelevant. -Original Message- From: Isobel Clark [mailto:[EMAIL PROTECTED] Sent: Monday, July 19, 2004 8:50 AM To: [EMAIL PROTECTED] Cc: [EMAIL PROTECTED] Subject: [ai-geostats] Re: Kriging Small Blocks Nicolau I was talking about kriging before cutoff is applied. If the cutoff is applied to the block estimates my comments stand. If you aply the cutoff to your data first and then krige, you get the opposite problem, because you will over-estimate every value and under-estimate the tonnage. My point (1) is that, if you wish to avoid conditional bias in your kriging, you could consider using a non-linear kriging method such as those mentioned. I have no experience with either, since I follow a different route in the correction of conditional bias in mineral resource estimation. Isobel http://geoecosse.bizland.com/whatsnew.htm --- [EMAIL PROTECTED] wrote: Isobel, So for mining purposes can't we just krige before applying the cut-off criteria? I mean, for most mining applications one will prefer to have a more realistic geologic block model and will always have the chance to evaluate his/her panels under the appropriate cut-off criteria, but applying that criteria after estimating small blocks, right? Could you please explain your point in solution (1) below? Thanks for indicating the literature. Thanks Nicolau Barros Engineer Mine Planning and Production Control Department Mineração Rio do Norte S.A. [EMAIL PROTECTED] +55 (93) 549 8215 Confidencialidade Esse e-mail e possíveis anexos podem possuir informações confidenciais e de interesse somente do destinatário. Portanto, se você recebeu esta mensagem por engano, favor comunicar imediatamente o remetente e deletá-la logo em seguida. Esteja ciente que o uso indevido do conteúdo das informações em questão é estritamente proibido. Confidentiality This message and any possible attached files may contain confidential information and only for interest of the intended recipient. If you have received this message by mistake, please notify the sender and delete the message immediately. Be aware that the unauthorized use of the above-mentioned information is strictly forbidden. -Mensagem original- De: Isobel Clark [mailto:[EMAIL PROTECTED] Enviada em: segunda-feira, 19 de julho de 2004 05:23 Para: [EMAIL PROTECTED] Cc: [EMAIL PROTECTED] Assunto: [ai-geostats] Re: Kriging Small Blocks Jul The warning about kriging small blocks is about small relative to the sampling density. For example, less than about one-third of the grid spacing. The warning is the same as the one about 'point' kriging (mapping) The map is too smooth - or, at least, a lot smoother than the real surface would be. High value areas will be under-estimated and low value areas will be over-estimated. If your objective in kriging is to obtain general maps of an area with an idea of where the highs and lows are, then ordinary kriging is sufficient. The over- and under- estimations cancel out on average. In mining applications, where block kriging originated, most applications require a 'cutoff', where values below a certain value are not included in the 'plan'. In this case, mapping or estimating small
[ai-geostats] Re: Kriging Small Blocks
Ed I would differ from your explanation on one point. If you are merely declaring a mineral resource, i.e. mineral in the ground, then the conditional bias may not be relevant at the pre feasibility stage. However, as soon as you introduce any economic or technical parameters which entail selection, the conditional bias makes its appearance. In every project I have worked on, from pre-feasibility onwards, I have been asked for a grade/tonnage calculation - no matter how hand-waving it may be. The grade/tonnage curve will be affected by the conditional bias. By how much has to be assessed at the time. Most of Chapter 3 in Practical Geostatistics 1979 is devoted to working out what the (theoretical) global grade tonnage curve looks like when you adjust for the variance reduction. Even this will differ from the curve derived from the kriged estimates, no matter what size the block. The problem is even greater for environmental applications, especially toxic level risks. A 'global view' - i.e. a map - will not identify the true peaks because of the conditional bias. The fact that the overall average is unbiassed is irrelevant when trying to identify an area which is likely to be lethal. So, there is no contradiction. Conditional bias is unimportant (or irrelevant) until you apply some selection criterion. Yes, we agree. However, selection criteria can be relevant at very early stages of a project. It depends on your objective. Isobel http://uk.geocities.com/drisobelclark/practica.htm for free downloads of Practical Geostatistics 1979 PS: sorry I mis-spelled your name, I know it drives me nuts when people call me 'Clarke' ;-) ___ALL-NEW Yahoo! Messenger - so many all-new ways to express yourself http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] Fractals Semivariance
Gregoire, To be honest I have never attempted this, although as you said the angular tolerance, bandwidth, and lag tolerance will ultimately determine whether the directional fractal dimensions can be averaged to give an omnidirectional dimension, D. I would argue that two directional variograms each with a directional tolerance of about 45 degrees on either side of the azimuth in the two principal directions would yield an average D similar to an omnidirectional case, but this will not strictly be true the smaller the tolerances used. I have used simulated annealing to generate (stochastic) fractal fields with different dimensions in three directions X, Y, and Z in 3D space, e.g. assumption of fractional Gussian noise vertically with high Hurst exponent (persistence) and fractional Brownian motion laterally with lower Hurst exponent (anti-persistence). Cheers Syed Hello Syed, I was hoping a reply from you :) I didn't think about the problematic of anisotropy and the potential use of ratios of fractal dimensions. It might be worth some further investigation. The physical meaning of fractals derived from directional variograms is tricky indeed. I was wondering if the average of all these fractal dimensions would be formally equal to the fractal dimension derived from omnidirectional variogram. My first guess would be yes, but this would depend on the angular tolerance of the directional variograms. And would the average value of the fractal dimension have any reasonable physical meaning? Any experience with this? Thanks again for the kind help. Gregoire -Original Message- From: Syed Abdul Rahman Shibli [mailto:[EMAIL PROTECTED] Sent: 16 July 2004 19:23 To: Gregoire Dubois Cc: [EMAIL PROTECTED] Subject: Re: [ai-geostats] Fractals Semivariance Not sure how anisotropic fractal spatial correlation models would fit in the whole scheme of things. You're essentially assuming a power law model (Brownian motion) to model the spatial correlation, which implicitly assumes a phenomena with an infinite capacity for dispersion, i.e. no range. The ratio of two fractal dimensions is not necessarily the same as the ratio of two ranges in the two directions of maximum and minimum continuity, which is the traditional measure of anisotropy. However, practically speaking you can still calculate experimental variograms for two, three, or four separate directions and derive the log-log estimate of the fractal dimension from these separate variograms. I wouldn't know what this will physically mean, except to say that I have a phenomena with different capacities for dispersion in different directions. Cheers Syed Dear all, at http://www.umanitoba.ca/faculties/science/botany/labs/ecology/fractals/measuring.html one can read the following The fractal dimension is estimated separately for each profile from the log-log plot of cell count against step size (D = 2 - slope, where 1 = D = 2). The average of these values plus one provides an estimate of the surface fractal dimension. Burrough's method (using the slope of the log-log plot of the semivariogram to calculate the fractal dimension of 1 dimensional transect or profile) could thus be extended to a 2 D case (a surface). Has anyone references discussing the use of Burrough's method when applied to a 2 D case? Unless one considers the investigated phenomenon completely isotropic, averaging the fractal dimensions derived from the slopes of directional log-log semivariograms may not provide any useful/reliable information. Has someone on the list any experience with this kind of issue? Thanks very much for any help. Best regards, Gregoire PS: I know there are other techniques to calculate the fractal dimension of a surface but I'm only interested in those involving the computation of the semivariance. __ Gregoire Dubois (Ph.D.) JRC - European Commission IES - Emissions and Health Unit Radioactivity Environmental Monitoring group TP 441, Via Fermi 1 21020 Ispra (VA) ITALY Tel. +39 (0)332 78 6360 Fax. +39 (0)332 78 5466 Email: [EMAIL PROTECTED] WWW: http://www.ai-geostats.org WWW: http://rem.jrc.cec.eu.int * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats* By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the
RE: [ai-geostats] Re: Kriging Small Blocks
Hi Isabel If you go to www.isaaks.com and click on Otherstuff, you will find an example where the block estimates are conditionally biased (rather severely), but the grade tonnage curves are right on the money. Perhaps this will help clear the confusion. Ed -Original Message- From: Isobel Clark [mailto:[EMAIL PROTECTED] Sent: Monday, July 19, 2004 10:47 AM To: Edward Isaaks Cc: [EMAIL PROTECTED] Subject: [ai-geostats] Re: Kriging Small Blocks Ed I would differ from your explanation on one point. If you are merely declaring a mineral resource, i.e. mineral in the ground, then the conditional bias may not be relevant at the pre feasibility stage. However, as soon as you introduce any economic or technical parameters which entail selection, the conditional bias makes its appearance. In every project I have worked on, from pre-feasibility onwards, I have been asked for a grade/tonnage calculation - no matter how hand-waving it may be. The grade/tonnage curve will be affected by the conditional bias. By how much has to be assessed at the time. Most of Chapter 3 in Practical Geostatistics 1979 is devoted to working out what the (theoretical) global grade tonnage curve looks like when you adjust for the variance reduction. Even this will differ from the curve derived from the kriged estimates, no matter what size the block. The problem is even greater for environmental applications, especially toxic level risks. A 'global view' - i.e. a map - will not identify the true peaks because of the conditional bias. The fact that the overall average is unbiassed is irrelevant when trying to identify an area which is likely to be lethal. So, there is no contradiction. Conditional bias is unimportant (or irrelevant) until you apply some selection criterion. Yes, we agree. However, selection criteria can be relevant at very early stages of a project. It depends on your objective. Isobel http://uk.geocities.com/drisobelclark/practica.htm for free downloads of Practical Geostatistics 1979 PS: sorry I mis-spelled your name, I know it drives me nuts when people call me 'Clarke' ;-) ___ALL-NEW Yahoo! Messenger - so many all-new ways to express yourself http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
