It was found to be necessary to convert the velocity model generation code to Python because:

• R code was using too much RAM.
• R code was slow.
• Using less RAM was taking too much effort and freeing RAM is slow.
• Looping over smaller subsets was too slow, difficult to work with.
• Everything is global, wasting RAM and messy.
• Libraries duplicating RAM of input data, not releasing it.
• Combining a 3GB dataframe after splitting it made it use 16GB RAM.
• Taking the first 20 million elements from a dataframe took ages and RAM usage blew up.

Outcomes

• Most if not all code runs in Python, mostly using numpy and gdal.
• Code runs in minutes instead of days.
• Can run on a machine with 32GB RAM (16GB should be enough too).
• Can run with larger input datasets.
• Can create finer resolutions than 100m.
• Results easily viewed/interrogated in QGIS project. Easily shared.

Currently all processing elements have had Python algorithms/process found/investigated except MVN.

Steps

1. Terrain model interpolation given values (core completed).
2. Geology model polygon interpolation (algorithm found 10,000s times faster).
3. Rest of geology model datasets, eg store slope as TIFF.
4. Rest of geology model processing rules.
5. Synchronise outputs between geology and terrain (run with same parameters).
6. Combination of geology/terrain.
7. Store all outputs.
8. Argument version for running complete workflow.
9. Create QGIS project including outputs as layers, open street map.
10. Convert VSPR to Python (loading and pre-processing measured sites).
11. Posterior model modification to Python using already available clustering code.
12. Look into converting MVN code to Python (find main algorithms/ processes required).
13. Convert if algorithms/processes found and reasonable in Python.
14. Implement MVN in Python.
15. Run at 50m, 10m resolution.
16. Compare results.