Geostat_Bimodal data

[EifLeffie]

Hi,

I am using geostat analysis to particularly kriging to interpolate my data but I cant seem to get a better result. I am new to kriging and geostat and am doing some readings and research about it but its taking so much time that I cant finish the interpolation as early as planned.

I would highly appreciate if anyone could give me an advise on this. The data that I am interpolating is bimodal and I wonder if there is an appropriate procedure for this. Thank u so much for any advise you can give.

Eif

[EricKrause]

For bimodal data like this, I suggest using Simple kriging, then apply a Normal Score Transformation.  Change the "Approximation method" to Gaussian Kernels.  Also, if your data is clustered, consider cell declustering before the normal score transformation.

[EifLeffie]

Hi Eric,

Thanks for the help Eric.
I got a result showing a high root mean square (239) and average standard error (234.16), mean error is also high (-7). I wonder if there is a technique to reduce these. I have tried changing the model type like Hole effect and other types hoping to at least reduce the errors but they are all resulting to high error values. Would there be some other trick here to reduce the error? My apology as I am not familiar with this method. I would be glad if there is any procedure on how to reduce errors in this type of processing.

I would highly appreciate any help on this. Thanks.

Best regards,

Eif

[EricKrause]

The optimal model parameters completely depend on your data.  There isn't any one technique that is guaranteed to work.

Have you tried the Optimize Model button at the top?  Try using K-Bessel or Stable semivariogram types, then press the optimize model button.  Also, try changing Variable to semivariogram and optimize the model again.

If that doesn't work, you may want to try to remove trends (the option appears on the Wizard page right before the semivariogram).

[EifLeffie]

Hi Eric,

The datasets that I am working with has a trend and the plan is to remove it. The trend is a U shape which can be removed using the second order polynomial. However, I have confusion in identifying the directional influence in the datasets. For instance, in page 104 of the manual of geostat analyst (Which I just found and is very useful indeed), the image (attached here) shows a strong influence on the southeast to northwest. I wonder how the strength of directional influence was detected. X  axis is west-east (left-right) direction while Y-axis is the north-south (up-down) heading.  Directional info on the image says: Location - 30 degrees; Horizontal - 120 degrees; Vertical -  -27degrees. Please kindly elaborate on this. Thank you.

Regards,
Eif

[EricKrause]

The Trend Analysis ESDA tool has a horizontal and vertical slide bar that allows you to rotate the graph.  As you move these sliders, you'll see the projected points (the green and blue points) change accordingly, and the trend lines will change to fit the new projected points.  By aligning the graph at different angles, you can see how the trend changes in different directions.

When you use trend removal in the Geostatistical Wizard, these directional trends will be automatically detected using local polynomial interpolation, and it will do its best to remove them before fitting the semivariogram.

One caveat is that it is often difficult (if not impossible) to differentiate trend, autocorrelation, and anisotropy.  They can all present themselves in ways that look identical.

Does that answer your question?

[EifLeffie]

Thanks Eric, but not really. I was wondering how the strength of the anisotropy can be detected. I have found an article regarding that clearly explained with the use of other software and I sorted it out already. I am also wondering if once the anisotropy is set to "TRUE", does the software automatically removes the effect of the directional influences or we need to do

Pardon me for the many questions, but it's actually taking me months already before I can finalize the kriging procedure as I am doing also some readings and research about the geostat and variograms. I am new to this geostat and semivariogram stuff. I don't want to give up on these and am keen to learn how I could minimize the errors so I could end up with the very good kriging results.

Cheers,
Eif

[EifLeffie]

Eric, sorry about my former post..

ERRATA ....OR WE NEED TO DO SOME FURTHER MODIFICATIONS...

Eif

[EricKrause]

When you set anisotropy to True, then hit the Optimize Model button at the top, the software will find the best angle and major/minor semiaxes based on cross-validation (lowest root-mean-square).  You can then manually alter these parameters if you want to.

[EifLeffie]

Thanks Eric. That's a big help!

Cheers,
Eif