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Re: AI-GEOSTATS: log-normal kriging error

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  • Isobel Clark
    Marta I am not familiar with the software you are using, but it looks like your lognormal standard errors are being back-transformed into raw units. If this
    Message 1 of 6 , Dec 3, 2003
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      Marta

      I am not familiar with the software you are using, but
      it looks like your lognormal standard errors are being
      back-transformed into 'raw' units. If this is the case
      part of the backtransform is to multiply the 'relative
      standard error' by the actual value of the estimate.

      That is, if your estimate is 0.52, the backtransformed
      standard error is multiplied by 0.52. If the estimate
      is 520, it is multiplied by 520. If you do a ratio
      between the standard error and the estimate, you will
      probably get equivalent 'relative' errors.

      We find it better to leave the standard errors in
      logarithms and use Sichel type confidence interval,
      using the lognormal model.

      Isobel
      http://uk.geocities.com/drisobelclark

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    • Gali Sirkis
      Dear list members, Please advise what to do in following case: The sparse dataset for kriging inlcudes only few (5-6) original data points + interpolated
      Message 2 of 6 , Dec 3, 2003
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        Dear list members,

        Please advise what to do in following case:
        The sparse dataset for kriging inlcudes only few
        (5-6) original data points + interpolated external
        data, that covering whole study area.
        One of the original data points seems completly not to
        fit to the main correlation line between original and
        external data, however mostly probable is not an
        error, but might represent different combination of
        data properties.
        Is there is any chance to use this outlying point?
        Does is sound feasible for you as specialists in
        statistical analysis to use the kriging method in this
        case?

        Many thanks in advance for your help,

        Gali Sirkis

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      • Monica Palaseanu-Lovejoy
        Hi, I am not sure i understood correctly your question. Fist of all, do the interpolated data have come from your sparse data interpolation? What method of
        Message 3 of 6 , Dec 3, 2003
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          Hi,

          I am not sure i understood correctly your question. Fist of all, do
          the interpolated data have come from your sparse data
          interpolation? What method of interpolation did you use in this
          case?

          After Burrough and McDonnel, 2000, you need at least 50 points to
          have reliable results through kriging. Certainly you can do it on less
          data, but until now i never saw a study considering this problem in
          depth (maybe there is literature out there, and if it does and
          anybody knows about it - i would like to know it also ;-))

          Secondly, if you know the outlier is not an error, but you interpret it
          as representing a different combination of properties than the rest
          of your data - i am not very sure it is wise to use it together with
          your rest of the data in any interpolation exercise. The outlier may
          represent a different population and in this case i cannot see any
          "physical" reason to treat all your data together if parts of the data
          represent different things. At least this is my opinion.

          Besides, if your data is not only sparse (5 or 6 data points .... it is
          really very sparse i think) but also far away in space, they can be
          at distances grater than the spatial correlation range, and in this
          case i really don't think you can use kriging .... you will have either
          a pure nugget effect or a very high nugget value and not a too high
          spatial correlation.

          Monica

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        • Gali Sirkis
          Hi Monica, thanks for quick reply. The interpolated data is a different data set with is by its nature (speaking about geological properties) should be
          Message 4 of 6 , Dec 3, 2003
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            Hi Monica,

            thanks for quick reply. The interpolated data is a
            different data set with is by its nature (speaking
            about geological properties) should be correlated with
            the sparse one.
            This is a geological data over not huge area - around
            20x30 kilometers. It should have at least some spatial
            correlation. The variogram is not of striking beauty
            :) but it is not a pure nugget effect, though.
            The only other way meaningfully interpolate between
            those sparse points, it seems to use the simple linear
            regression between those two datasets.
            The literature about kriging/interpolating for very
            sparse data would definitely help, if anybody know
            about, please let know.

            Thanks,

            Gali


            --- Monica Palaseanu-Lovejoy
            <monica.palaseanu-lovejoy@...> wrote:
            > Hi,
            >
            > I am not sure i understood correctly your question.
            > Fist of all, do
            > the interpolated data have come from your sparse
            > data
            > interpolation? What method of interpolation did you
            > use in this
            > case?
            >
            > After Burrough and McDonnel, 2000, you need at least
            > 50 points to
            > have reliable results through kriging. Certainly you
            > can do it on less
            > data, but until now i never saw a study considering
            > this problem in
            > depth (maybe there is literature out there, and if
            > it does and
            > anybody knows about it - i would like to know it
            > also ;-))
            >
            > Secondly, if you know the outlier is not an error,
            > but you interpret it
            > as representing a different combination of
            > properties than the rest
            > of your data - i am not very sure it is wise to use
            > it together with
            > your rest of the data in any interpolation exercise.
            > The outlier may
            > represent a different population and in this case i
            > cannot see any
            > "physical" reason to treat all your data together if
            > parts of the data
            > represent different things. At least this is my
            > opinion.
            >
            > Besides, if your data is not only sparse (5 or 6
            > data points .... it is
            > really very sparse i think) but also far away in
            > space, they can be
            > at distances grater than the spatial correlation
            > range, and in this
            > case i really don't think you can use kriging ....
            > you will have either
            > a pure nugget effect or a very high nugget value and
            > not a too high
            > spatial correlation.
            >
            > Monica


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          • Marcel Vallée
            Gail Sorry for not responding earlier to your request. Your explanatory comment to Monica does not convince me as a exploration and mining geologist. I think
            Message 5 of 6 , Dec 5, 2003
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              Gail

              Sorry for not responding earlier to your request.

              Your explanatory comment to Monica does not convince me
              as a exploration and mining geologist. I think her comments are
              wise and should be considered.

              A 20x30 km area is a large one even when dealing with very
              uniform geology. Even in such conditions, different properties
              may be encountered, either as faults, vein or fracturation
              system, small intrusive bodies, mineral showings or deposits,
              pollution zones, etc.

              Such a small sample set as you have ["few (5-6) original data
              points + interpolated external data"] that covering whole study
              area] does not allow you to really appraise the validity and/or
              the geological cause of this "outlier." (There might be a
              sampling or assaying cause also). In such a case, it should be
              shown as an anomaly, not averaged out or kriged out.

              Excluding sampling/analytical problems, the outlier only has a
              "detection"value, meaning that the geology is not as uniform as
              expected and that additional geological observations and sampling
              in the vicinity is required to elucidate this problem.

              We should view geostatistics as an ancillary tool to understand a
              two or three dimensional "geological universe." Whenever data ara
              as sparse as in your exemple, kriged values should not replace
              and/or eliminate the potential meaning of sparse field observations.

              Sincerely


              Marcel Vallée

              ========================

              Marcel Vallée Eng., Geo.
              Géoconseil Marcel Vallée Inc.
              706 Routhier St
              Québec, Québec,
              Canada G1X 3J9
              Tel: (1) 418, 652, 3497
              Email: vallee.marcel@...





              =========================================
              Gali Sirkis wrote:
              > Hi Monica,
              >
              > thanks for quick reply. The interpolated data is a
              > different data set with is by its nature (speaking
              > about geological properties) should be correlated with
              > the sparse one.
              > This is a geological data over not huge area - around
              > 20x30 kilometers. It should have at least some spatial
              > correlation. The variogram is not of striking beauty
              > :) but it is not a pure nugget effect, though.
              > The only other way meaningfully interpolate between
              > those sparse points, it seems to use the simple linear
              > regression between those two datasets.
              > The literature about kriging/interpolating for very
              > sparse data would definitely help, if anybody know
              > about, please let know.
              >
              > Thanks,
              >
              > Gali
              >
              >
              > --- Monica Palaseanu-Lovejoy
              > <monica.palaseanu-lovejoy@...> wrote:
              >
              >>Hi,
              >>
              >>I am not sure i understood correctly your question.
              >>Fist of all, do
              >>the interpolated data have come from your sparse
              >>data
              >>interpolation? What method of interpolation did you
              >>use in this
              >>case?
              >>
              >>After Burrough and McDonnel, 2000, you need at least
              >>50 points to
              >>have reliable results through kriging. Certainly you
              >>can do it on less
              >>data, but until now i never saw a study considering
              >>this problem in
              >>depth (maybe there is literature out there, and if
              >>it does and
              >>anybody knows about it - i would like to know it
              >>also ;-))
              >>
              >>Secondly, if you know the outlier is not an error,
              >>but you interpret it
              >>as representing a different combination of
              >>properties than the rest
              >>of your data - i am not very sure it is wise to use
              >>it together with
              >>your rest of the data in any interpolation exercise.
              >>The outlier may
              >>represent a different population and in this case i
              >>cannot see any
              >>"physical" reason to treat all your data together if
              >>parts of the data
              >>represent different things. At least this is my
              >>opinion.
              >>
              >>Besides, if your data is not only sparse (5 or 6
              >>data points .... it is
              >>really very sparse i think) but also far away in
              >>space, they can be
              >>at distances grater than the spatial correlation
              >>range, and in this
              >>case i really don't think you can use kriging ....
              >>you will have either
              >>a pure nugget effect or a very high nugget value and
              >>not a too high
              >>spatial correlation.
              >>
              >>Monica

              Dear list members,

              Please advise what to do in following case:
              The sparse dataset for kriging inlcudes only few
              (5-6) original data points + interpolated external
              data, that covering whole study area. One of the original data
              points seems completly not to
              fit to the main correlation line between original and
              external data, however mostly probable is not an
              error, but might represent different combination of
              data properties. Is there is any chance to use this outlying point?
              Does is sound feasible for you as specialists in
              statistical analysis to use the kriging method in this
              case?

              Many thanks in advance for your help,

              Gali Sirkis
              >
              >
              >
              > __________________________________
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              >



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