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Re: GEOSTATS: Assessing the success of simulation

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  • srahman@lgc.com
    Scarce datasets are more the norm than the exception for most disiciplines. Indicators take into account the connectivity of extreme values, which might be
    Message 1 of 4 , Jan 26, 2000
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      Scarce datasets are more the norm than the exception for most
      disiciplines. Indicators take into account the connectivity of extreme
      values, which might be applicable to a mapping of probabilities
      of exceedance of a contaminant. Nevertheless, indicator variograms,
      except at the median where you would find more "pairs", tend
      to have poor definition at the extreme ends. Not knowing much about
      the processes behind a certain environmental disaster, except
      that prevailing weather and other conditions might have an impact
      on the distribution of contaminants, I would surmise that it will be
      difficult to find an "analogy", like someone could in the geosciences,
      e.g. a channel sand, whether encountered in Tashkent or
      Muskogee, Oklahoma, is the end result of a similar kind of depositional
      process.

      I know of some sites where the problem of uncertainty, at least of
      first order (higher scale) uncertainty, is tackled in a more "deterministic"
      manner.The approach comes under various guises, frequently
      names such as "scenario modeling", "deterministic modeling", etc
      are used. The idea is to subjectively come up with a ranked list of
      higher level uncertainties that might presumably have a bigger impact
      on the eventual mapping result, before zooming in on the details of
      any particular "scenario" or model. One can additionaly apply a
      probablistic technique to this first phase of scenario modeling, e.g.
      Boolean simulation, but frequently due to the sheer number of variables
      of first order uncertainty, scenarios are arrived at deterministically.
      To take an oil and gas example, the following process will be applied:

      OBJECTIVE: Arrive at oil recovery factor for a carbonate pool
      FIRST ORDER UNCERTAINTIES:

      Fault sealing capacity
      Completely sealed
      Partly sealed
      Fully transmissible
      Fracture distribution
      Dense
      Medium
      Low density
      Geological model
      Pinnacle
      Platform

      and so forth. In such cases, lower order permeability distributions might
      be a secondary consideration. Presumably in your case, "wind direction"
      might be a factor, coupled with such others as "rainfall distribution", or
      "amount of contaminant leaked to the environment". Each of the above scenarios
      will result in a greater variance of results than say, "wind speed" or "amount
      of rainfall". It matters less how much rain fell compared to how such rain
      is actually distributed, e.g. is it a residential area? a national park? a
      desert?

      Syed
      Landmark Graphics








      Tom Charnock <Tom.Charnock@...> on 01/26/2000 07:51:40 PM








      To: ai-geostats@...

      cc: (bcc: Syed Abdul Rahman/SINGPROD1/Landmark)



      Subject: Re: GEOSTATS: Assessing the success of simulation









      Hi

      Thanks to Pierre for the advice about indicator and multigaussian kriging. I've
      looked
      at indicators. However the problem I have with all the techniques I've tried is
      that we will have to apply to very scarce data. The consequence is that it is
      very hard to generate a reasonable variogram on the basis of the data alone.
      This means I have to use some feeling about the nature of the process and my
      experience with large datasets to define (ok fiddle) a SV model which I think is
      about right. I'm not particularly happy about doing this for the untransformed
      data, I'm less happy doing it for indicators. Comments anyone?

      Multigaussian I have not tried, can anyone point me at any software etc. Would
      disjunctive kriging also do the same things for me?


      Tom

      PS thanks also to Phaedon who responded directly


      > -----Original Message-----
      > From: Pierre Goovaerts [mailto:goovaert@...]
      > Sent: 24 January 2000 22:33
      > To: Tom Charnock
      > Cc: 'AI-Geostatistics'
      > Subject: Re: GEOSTATS: Assessing the success of simulation
      >
      >
      > Hi Tom,
      >
      > You can look at the paper by C. Deutsch
      > Deutsch, C.V. 1997. Direct assessment of local accuracy and precision.
      > In: Baafi, E.Y., Schofield, N.A. (Eds.), Geostatistics Wollongong '96.
      > Kluwer Academic Publishers, Dordrecht, pp. 115--125.
      >
      > The corresponding Gslib program, called accplt, can
      > be downloaded from:
      > http://www.ualberta.ca/~cdeutsch/
      >
      > Note that if the objective is simply to estimate a probability
      > of contamination over the same support than the data, I don't
      > see the benefit of using stochastic simulation over a more
      > straightforward (indicator or multiGaussian) kriging approach.
      >
      > Regards,
      >
      > Pierre
      >
      >
      >
      > On Mon, 24 Jan 2000, Tom Charnock wrote:
      >
      > > Hi
      > >
      > > I've been looking at simulation to generate probability maps of
      > > contamination exceeding a given level. I am looking at how
      > > we might apply
      > > the technique in the initial stages of an accident,
      > > consequently, though I
      > > have large datasets to try the techniques on, I only use a
      > > small subset when
      > > generating the realisations. My question is whether there
      > > is any standard
      > > method for assessing the validity of the probability map
      > > using the remaining
      > > data. I.e. I'm looking for some kind of jackknifing
      > > procedure but for
      > > simulation rather than estimation.
      > >
      > > cheers
      > >
      > > Tom
      > >
      > > PS can anyone point me at any literature about introducing
      > > known trends into
      > > simulation codes.
      > >
      >
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