Are there ways to streamline de-duplication of targets picked, extract peak to peak values, etc?
LorraineGodwin
Posts: 94 
in Oasis montaj
These questions were asked via email. I'm sharing them here in case others have similar questions.
Are there any ways to streamline the de-duplication of targets picked, extract Peak-to-Peak values, etc? Also some info on the picking dipoles function would be interesting. How this operates compared to say Blakeley, and comparable settings to use if a cut-off value for Blakeley test is provided by the client for example.
Answers:
De-duplication of targets picked:
We do have a Merge Targets function, however this is actually used for de-clustering as it merges to a central point, not the highest peak in the analytical signal grid. The double picks are often geological or large targets. Using the peak to peak tool could help identify these.
Peak to Peak (Find Magnetic Dipoles tool in UXO Marine)
This can be found below the Blakely method in the UXO Marine Mag Menu. Try it on a small or clipped dataset first.
You need a Blakely target database with picked targets (so no problem for your clients cut-off). Around each target it looks for a positive and negative. If you have a small max distance, it will only process small dipoles.
Note that you will get a peak-to-peak amplitude and you can calculate wavelength Dipole Separation Distance: C0 = sqrt((N1-N2)**2 +(E1-E2)**2).
Peak to peak amplitude and wavelength are really good for target deliverables. You will only get parameters for Dipoles, but sampling the residual grid to the target database should give you peak values for the monopoles (positive and negative)
When using large datasets, its best to split them up. This is the same for our batch fit modelling tool. We suggest trying batch fit, as you get magnetic moment and in some cases better magnetic depths than Euler.
Are there any ways to streamline the de-duplication of targets picked, extract Peak-to-Peak values, etc? Also some info on the picking dipoles function would be interesting. How this operates compared to say Blakeley, and comparable settings to use if a cut-off value for Blakeley test is provided by the client for example.
Answers:
De-duplication of targets picked:
We do have a Merge Targets function, however this is actually used for de-clustering as it merges to a central point, not the highest peak in the analytical signal grid. The double picks are often geological or large targets. Using the peak to peak tool could help identify these.
Peak to Peak (Find Magnetic Dipoles tool in UXO Marine)
This can be found below the Blakely method in the UXO Marine Mag Menu. Try it on a small or clipped dataset first.
You need a Blakely target database with picked targets (so no problem for your clients cut-off). Around each target it looks for a positive and negative. If you have a small max distance, it will only process small dipoles.
Note that you will get a peak-to-peak amplitude and you can calculate wavelength Dipole Separation Distance: C0 = sqrt((N1-N2)**2 +(E1-E2)**2).
Peak to peak amplitude and wavelength are really good for target deliverables. You will only get parameters for Dipoles, but sampling the residual grid to the target database should give you peak values for the monopoles (positive and negative)
When using large datasets, its best to split them up. This is the same for our batch fit modelling tool. We suggest trying batch fit, as you get magnetic moment and in some cases better magnetic depths than Euler.
Lorraine Godwin
Global Business Director
Global Business Director
0
Comments
-
Just to add to Lorraine's post, in the 9.3 release of Oasis montaj we added several new options for clustering and merging targets. When merging several close targets to determine the location of a new "clustered" target, you can now choose from three methods:
-Centroid of the individual targets
-Centroid of the individual targets weighted by their anomaly value
-Location of the maximum anomaly value of the individual targets
The new merged target can be assigned an anomaly value based on the data grid value at the location, or the average of the individual target anomaly values. You are also able to characterize the clusters based on the dispersion of the individual targets.
Product Manager1
This discussion has been closed.