Dear Mr. Matt Hall,
Thanks for sharing such enthusiastic notebooks!
I want to try DL for seismic interpretation and now I prepare data for it.
Could you please help me with some question on conversion of x,y coordinates to inline/crossline and consequent turning it mask image?
I assume it is strongly correlated with this notebook.
I have 3d seismic cube data which is currently 3d numpy array with dimensions (inline slice/crossline slice/depth slice). I labelled some polygons on this data using other software (Petrel).
3d seismic cube has CRS metadata and as polygons do.
I can export data from 3d cube in form of 2d image slice either inline, crossline or depth slice. Also I exported polygons in form of shapefile. After browsing through your notebooks I guess may be it is better to try to save and read polygons with ASCII rather than shapefile.
I managed to address them using geopandas library. Shapefile has x,y coordinates grid while that could be converted into belonging some cross section of inline/crossline/depth slice. Cube dimensions are [1436, 624, 151] while shapefile is in x,y coordinates.
Shortly, I want to try conversion as bruges library did. I looked the web and found this formula and tried but I am missing something as this does not correlate with real values.
I have all corresponding CRS data for cube.
My idea is to convert shapefile or ASCII to crossline/inline/depth dimensions, overlay it with data to check and create a mask image from it. Could you please help me how to do it?
I attach images for convenience what I have and what I want.
Example of rendering together inline slice/crossline slice/depth slice and polygon which are of orange color.
![2020-01-14_18h05_26](https://user-images.githubusercontent.com/45141680/72355412-7b800c00-36f8-11ea-933b-ff65e365b2fa.png)
Depth slice and same shapefile with polygons in python. Notice, that here polygons are in x,y meters dimensions
![2020-01-13_16h47_54](https://user-images.githubusercontent.com/45141680/72355266-2e039f00-36f8-11ea-85f4-ae66305957a4.png)
Geographic and data information
Example of desired output which here is along crossline
![2020-01-09_17h25_59](https://user-images.githubusercontent.com/45141680/72355879-4e802900-36f9-11ea-8bf3-5a21bf582000.png)
I did test conversion of polygon in OpendTect, but want to do it for all polygon in python. However, this is only visual inspection.
For example,
x = 116003.485084 y = 59713.656120 -> inline 5826 crossline 1244 -> inline = 789 crossline = 225
x = 115394.130714 y = 59377.500480 -> inline 5814 crossline 1269 -> inline = 775 crossline = 250
x = 116655.722511 y = 60282.412712 -> inline 5847 crossline 1217-> inline = 808 crossline = 198
I found this formula , but it did not match with this calculations or I am missing something.
Survey parameters:
min max
X | 105269.86 | 122544.19
Y | 39605.21 | 76201.71
Origin X: 122531.11
Origin Y: 40352.05
End first inline X: 106973.10
End first inline Y: 39618.29
End first crossline X: 120840.95
End first crossline Y: 76188.64
Number of inlines: 1436
Number of crosslines: 624
Inline interval: 25.00
Crossline interval: 25.00
Inline rotation from north: -92.70
CRS is["GK42-13N:Pulkovo 1942, Gauss-Kruger Z13 North Russia".
So could you tell me what is the formula for conversion? I feel that it is easy task, but miss something. Is there dependence on inline rotation angle?
I understand if it is out of scope of your expertise but I would be glad any suggestions on this task.
Thank you. I appreciate your time
Sincerely,
Kirill