ASTM D5924-1996(2010) 8125 Standard Guide for Selection of Simulation Approaches in Geostatistical Site Investigations《地质现场调查中模拟近似法选择的标准指南》.pdf

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1、Designation: D5924 96 (Reapproved 2010)Standard Guide forSelection of Simulation Approaches in Geostatistical SiteInvestigations1This standard is issued under the fixed designation D5924; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revis

2、ion, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.INTRODUCTIONGeostatistics is a framework for data analysis, estimation, and simulation in media whosemeasurable a

3、ttributes show erratic spatial variability yet also possess a degree of spatial continuityimparted by the natural and anthropogenic processes operating therein. The soil, rock, and containedfluids encountered in environmental or geotechnical site investigations present such features, and theirsample

4、d attributes are therefore amenable to geostatistical treatment. Geostatistical simulationapproaches are used to produce maps of an attribute that honor the spatial variability of sampledvalues. This guide reviews criteria for selecting a simulation approach, offering direction based on aconsensus o

5、f views without recommending a standard practice to follow in all cases.1. Scope1.1 This guide covers the conditions that determine theselection of a suitable simulation approach for a site investi-gation problem. Alternative simulation approaches consideredhere are conditional and nonconditional, i

6、ndicator and Gauss-ian, single and multiple realization, point, and block.1.2 This guide describes the conditions for which the use ofsimulation is an appropriate alternative to the use of estimationin geostatistical site investigations.1.3 This guide does not discuss the basic principles ofgeostati

7、stics. Introductions to geostatistics may be found innumerous texts including Refs (1-3).21.4 This guide is concerned with general simulation ap-proaches only and does not discuss particular simulationalgorithms currently in use. These are described in Refs (4-6).1.5 This guide offers an organized c

8、ollection of informationor a series of options and does not recommend a specificcourse of action. This document cannot replace education orexperience and should be used in conjunction with professionaljudgment. Not all aspects of this guide may be applicable in allcircumstances. This ASTM standard i

9、s not intended to repre-sent or replace the standard of care by which the adequacy ofa given professional service must be judged, nor should thisdocument be applied without consideration of a projects manyunique aspects. The word “Standard” in the title of thisdocument means only that the document h

10、as been approvedthrough the ASTM consensus process.2. Referenced Documents2.1 ASTM Standards:3D653 Terminology Relating to Soil, Rock, and ContainedFluidsD5549 Guide for The Contents of Geostatistical Site Inves-tigation Report4D5922 Guide for Analysis of Spatial Variation in Geostatis-tical Site In

11、vestigationsD5923 Guide for Selection of Kriging Methods in Geo-statistical Site Investigations3. Terminology3.1 Definitions of Terms Specific to This Standard:3.1.1 conditional simulation, na simulation approachwhere realizations of the random function model are con-strained by values at sampled lo

12、cations.3.1.2 drift, nin geostatistics, a systematic spatial variationof the local mean of a variable, usually expressed as apolynomial function of location coordinates.3.1.3 field, nin geostatistics, the region of one-, two- orthree-dimensional space within which a regionalized variableis defined.1

13、This guide is under the jurisdiction ofASTM Committee D18 on Soil and Rockand is the direct responsibility of Subcommittee D18.01 on Surface and SubsurfaceCharacterization.Current edition approved May 1, 2010. Published September 2010. Originallyapproved in 1996. Last previous edition approved in 20

14、04 as D592496(2004).DOI: 10.1520/D5924-96R10.2The boldface numbers in parentheses refer to a list of references at the end ofthe text.3For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume info

15、rmation, refer to the standards Document Summary page onthe ASTM website.4Withdrawn. The last approved version of this historical standard is referencedon www.astm.org.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.3.1.4 indicator v

16、ariable, na regionalized variable that canhave only two possible values, zero or one.3.1.5 kriging, nan estimation method where sampleweights are obtained using a linear least-squares optimizationprocedure based on a mathematical model of spatial variabilityand where the unknown variable and the ava

17、ilable samplevalues may have a point or block support.3.1.6 nonconditional simulation, na simulation approachwhere realizations of the random function model are uncon-strained by sample data.3.1.7 nugget effect, nthe component of spatial varianceunresolved by the sample spacing and the additional va

18、riancedue to measurement error.3.1.8 point, nin geostatistics, the location in the field atwhich a regionalized variable is defined. It also commonlyrefers to the support of sample-scale variables.3.1.9 realization, nan outcome of a spatial random func-tion or a random variable.3.1.10 regionalized v

19、ariable, na measured quantity or anumerical attribute characterizing a spatially variable phenom-enon at a location in the field.3.1.11 simulation, nin geostatistics, a numerical proce-dure for generating realizations of fields based on the randomfunction model chosen to represent a regionalized var

20、iable.3.1.12 smoothing effect, nin geostatistics, the reduction inspatial variance of estimated values compared to true values.3.1.13 spatial average, na quantity obtained by averaginga regionalized variable over a finite region of space.3.1.14 support, nin geostatistics, the spatial averagingregion

21、 over which a regionalized variable is defined, oftenapproximated by a point for sample-scale variables.3.2 Definitions of Other TermsFor definitions of otherterms used in this guide, refer to Terminology D653 andGuides D5549, D5922, and D5923. A complete glossary ofgeostatistical terminology is giv

22、en in Ref (7).4. Significance and Use4.1 This guide is intended to encourage consistency andthoroughness in the application of geostatistical simulation toenvironmental, geotechnical, and hydrogeological site investi-gations.4.2 This guide may be used to assist those performing asimulation study or

23、as an explanation of procedures forqualified nonparticipants who may be reviewing or auditing thestudy.4.3 This guide should be used in conjunction with GuidesD5549, D5922, and D5923.4.4 This guide describes conditions for which simulation orparticular simulation approaches are recommended. However,

24、these approaches are not necessarily inappropriate if the statedconditions are not encountered.5. Selection of Simulation Approaches5.1 Simulation Versus EstimationA common objective ofgeostatistical site investigations is to produce a two- orthree-dimensional spatial representation of a regionalize

25、d vari-able field from a set of measured values at different locations.Such spatial representations are referred to here as maps.Estimation approaches, including all forms of kriging, yieldmaps that exhibit a smoothing effect, whereas simulationapproaches yield maps that preserve the spatial variabi

26、lity ofthe regionalized variable.5.1.1 If mapped values of the regionalized variable arerequired to provide an estimate of actual values at unsampledpoints, then an estimation approach such as kriging is appro-priate.5.1.2 If mapped values of the regionalized variable are topreserve the spatial vari

27、ability of values at unsampled points,then simulation rather than estimation should be used.NOTE 1Preservation of in-situ spatial variability is important ifmapped values of the regionalized variable are to be entered in anumerical model of a dynamic process, and therefore, simulation shouldgenerall

28、y be used. For example, mapped values of transmissivity to beentered in a numerical model of ground water flow should be generated bysimulation (8). However, if the numerical process model is insensitive tospatial variations of the regionalized variable, then an estimation approachmay also be used.5

29、.2 Conditional Versus Nonconditional SimulationGeostatistical simulation methods are able to produce maps ofa regionalized variable that honor values observed at sampledpoints, a selected univariate distribution model, and a selectedmodel of spatial variation. The univariate distribution modelmay be

30、 that of the observed sample values or a model that isdeemed more appropriate. The model of spatial variation maybe that of observed sample values or a model of spatialvariation that is deemed more appropriate.5.2.1 If the simulated field need honor only a univariatedistribution model and a spatial

31、variability model, then anonconditional simulation approach is sufficient.5.2.2 If the simulated field is to honor values of theregionalized variable observed at sampled points in addition tohistogram and spatial variability models, then a conditionalsimulation approach should be used.5.2.3 If the r

32、egionalized variable exhibits a drift or otherfeature that is not explicitly considered in the geostatisticalmodel, then conditional simulation may be used to impart someof this feature in the simulated field.5.2.4 If part of the nugget effect exhibited by the sampledregionalized variable is due to

33、sampling error and the simula-tion is to reproduce in-situ spatial variability, then a conditionalsimulation approach may be used if it ensures that thedifferences between observed and simulated values of theregionalized variable at sampled points are consistent with thesampling precision.5.3 Gaussi

34、an Versus Indicator SimulationGaussian andindicator geostatistical simulation approaches each have theirown particular characteristics rendering them more suitable forsome applications than others. Simulation algorithms based onGaussian (normal) variables produce realizations in whichthere is a maxi

35、mum scatter of extreme high and low values.Simulation algorithms based on indicator variables, on theother hand, are intended to produce realizations that honor thespatial variability of extreme values.5.3.1 If the simulated regionalized variable is binary orcategorical, then an indicator-based simu

36、lation approachshould be used.D5924 96 (2010)25.3.2 If the simulated regionalized variable is continuousand the spatial variability of extreme values must be repro-duced, then this variable may be coded into a sequence ofindicator variables that should be simulated using an indicator-based approach.

37、5.3.3 If the simulated regionalized variable is continuousand the spatial variability of extreme values is unimportant,then a Gaussian-based simulation approach should be used.5.3.4 If the simulated regionalized variable is continuousbut may be grouped into two or more distinct populations, thenan i

38、ndicator-based approach may be used to simulate groupboundaries and a Gaussian-based approach may be used tosimulate the regionalized variable within each group.5.3.5 If available sample data are limited and a Gaussianmodel cannot be refuted, then a Gaussian-based simulationapproach is the conventio

39、nal default.5.4 Single Versus Multiple RealizationsGeostatisticalsimulation approaches may be used to generate one or morepossible maps of a regionalized variable that honor specifiedprobability distribution and spatial variation models and, ifdesired, data values at sampled points.5.4.1 If uncertai

40、nty in mapped values of the regionalizedvariable is the focus of a sensitivity analysis, then multiplerealizations should be simulated.5.4.2 If the simulated field is part of a Monte-Carlo sensi-tivity analysis, then a simulation approach capable of generat-ing equally probable realizations is requi

41、red.5.5 Point Versus Block SimulationGeostatistical simula-tion approaches may be used to generate maps of regionalizedvariables with either point or block support. These simulationapproaches must ensure that the spatial variability of simulatedvalues is consistent with the spatial averaging or chan

42、ge-of-support process.5.5.1 If the simulated regionalized variable has a pointsupport or the same support as the sampled variable, then apoint simulation approach should be used.5.5.2 If the simulated regionalized variable has a blocksupport discretized by a finite number of points, then pointsimula

43、tion followed by spatial averaging over the discretizedblocks is an approach that can be used provided the spatialaveraging process is known.5.5.3 If the simulated regionalized variable has a blocksupport and the spatial averaging process is arithmetic, then adirect block simulation approach may be

44、used.6. Keywords6.1 estimation; geostatistics; kriging; simulationREFERENCES(1) Journel, A. G., and Huijbregts, C., Mining Geostatistics, AcademicPress, London, 1978.(2) Isaaks, E. H., and Srivastava, R. M., An Introduction to AppliedGeostatistics, Oxford University Press, New York, 1989.(3) Marsily

45、, G. de, Quantitative Hydrogeology, Academic Press, Or-lando, 1986.(4) Luster, G. R., “Raw Materials for Portland Cement: Applications ofConditional Simulation of Coregionalization,” Ph.D. Thesis, Depart-ment of Applied Earth Sciences, Stanford University, Stanford, CA,1985.(5) Deutsch, C. V., and J

46、ournel, A. G., GSLIB Geostatistical SoftwareLibrary an Users Guide, Oxford University Press, New York, 1992.(6) Srivastava, R. M., “An Overview of Stochastic Methods for ReservoirCharacterization, in Stochastic Modeling and Geostatistics: Principles,Methods and Case Studies,” J. Yarus and R. Chamber

47、s, eds., AAPG,in press, 1995.(7) Olea, R.A., ed., Geostatistical Glossary and Multilingual Dictionary,Oxford University Press, New York, 1991.(8) Desbarats, A. J., and Dimitrakopoulos, R., “Geostatistical Modellingof Transmissibility for 2D Reservoir Studies,” SPE FormationEvaluation, 5(4), 1990, pp

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