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ASEG_Hobart_Workshop_2024/00_notebooks/01_gradientbased_synthetic/gradientbased_synth.ipynb
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# [HiQGA.jl](https://github.com/GeoscienceAustralia/HiQGA.jl) workshop at ASEG Discover 2024 conference, Hobart: Airborne electromagnetic (AEM) surveying from farm to table -- an interactive overview of acquisition, processing and inversion of AEM data | ||
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Jupyter notebooks are in the directory | ||
``` | ||
00_notebooks/ | ||
``` | ||
and will only require the use of a computer with at least 7 CPUs. Most laptops can run these in 2024. Remember to change any file paths to your local disk if running the notebooks yourself. | ||
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To view the pre-run notebooks, simply click the `.ipynb` file in GitHub on your browser. | ||
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Julia scripts (not notebooks) on which the notebooks are based, are in the directories | ||
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``` | ||
synthetic/ | ||
UDF_data/ | ||
UDF_deterministic/ | ||
UDF_probabilistic/ | ||
``` | ||
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Simply run the files in numbered order for `synthetic/`. Remember to change any file paths to your local disk! | ||
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Please note that the `UDF_deterministic/` and `UDF_probabilistic/` directories use MPI and 1,000s of cores. The submit scripts for `qsub`/PBS on the Gadi cluster, as well as diagnostic output from HiQGA is included. These two directories only require you run the 01, 02 and 04 scripts to plot pre-run results. However, for the probabilistic inversions, the full posteriors are too large to provide here. Again, remember to change any file paths to your local disk. | ||
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The directory | ||
``` | ||
viz | ||
``` | ||
contains a Julia script that uses the accompanying [RDP.jl](https://github.com/GeoscienceAustralia/HiQGA.jl/blob/55400fa97c57f576701e239e651ccce83557cf46/zz_portalcurtains/RDP.jl) module in commit `55400fa` for [VTK](https://vtk.org/) visualization in [ParaView](https://www.paraview.org/) or another VTK viewer such as [VisIt](https://visit-dav.github.io/visit-website/). | ||
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## Installation of HiQGA version used in the workshop | ||
To install the workshop stable release, use Julia's `Pkg` REPL by hitting `]` to enter `pkg>` mode. Then enter the following, at the `pkg>` prompt: | ||
``` | ||
pkg> add HiQGA @0.4.8 | ||
``` | ||
For the latest HiQGA version (not used in the workshop), drop the `@0.4.8` | ||
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## Docs | ||
References, detailed instructions for installation, running examples and setting your environment on a cluster are ☞ [<img src="https://img.shields.io/badge/docs-stable-steelblue.svg">](https://geoscienceaustralia.github.io/HiQGA.jl/) | ||
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## References | ||
Please see the accompanying conference abstract for mathmatical details and further references | ||
https://doi.org/10.5281/zenodo.13918176 | ||
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# Tx dimensions | ||
rTx = 10.4 | ||
# Low Pass filter corner frequencies | ||
lowpassfcs = [203600.0, 300000.0] | ||
# High alt noise in same units as data in ASEG GDF | ||
include("halt_combined.jl") | ||
# LM settings | ||
LM_times = 1e-6*[ | ||
16.415 | ||
20.915 | ||
26.415 | ||
33.415 | ||
42.415 | ||
53.915 | ||
68.415 | ||
86.415 | ||
108.915 | ||
136.915 | ||
177.915 | ||
228.915 | ||
279.915 | ||
355.915 | ||
456.915 | ||
557.915 | ||
684.415 | ||
861.415 | ||
] | ||
LM_ramp = [ | ||
-0.0008 0.0 | ||
-0.0007396 0.099783 | ||
-0.00062783 0.25193 | ||
0.0 1.0 | ||
3.288e-7 0.98807 | ||
1.0648e-6 0.93826 | ||
1.9688e-6 0.84188 | ||
2.3048e-6 0.79337 | ||
2.6488e-6 0.75004 | ||
3.1448e-6 0.67073 | ||
4.0648e-6 0.55438 | ||
4.6328e-6 0.46349 | ||
6.0648e-6 0.28483 | ||
7.2888e-6 0.16799 | ||
8.6408e-6 0.082725 | ||
9.6328e-6 0.044079 | ||
1.1041e-5 0.012779 | ||
1.2217e-5 0.0 | ||
] | ||
# HM settings | ||
HM_times = 1e-6*[ | ||
436.415 | ||
458.915 | ||
486.915 | ||
527.915 | ||
578.915 | ||
629.915 | ||
705.915 | ||
806.915 | ||
907.915 | ||
1034.415 | ||
1211.415 | ||
1438.915 | ||
1742.415 | ||
2121.415 | ||
2575.915 | ||
3182.415 | ||
3940.415 | ||
4849.415 | ||
5960.415 | ||
7274.415 | ||
8892.415 | ||
10812.415 | ||
13187.415 | ||
] | ||
HM_ramp = [ | ||
-0.005 0.0 | ||
-0.0049025 0.32775 | ||
-0.0048002 0.65055 | ||
-0.0047155 0.89397 | ||
-0.0047064 0.9167 | ||
-0.004692 0.93776 | ||
-0.0043829 0.95989 | ||
-0.0040505 0.96678 | ||
-0.0017826 0.97902 | ||
0.0 1.0 | ||
4.8566e-5 0.8511 | ||
0.00010945 0.65471 | ||
0.00018545 0.40016 | ||
0.00029847 0.011948 | ||
0.00030379 0.0 | ||
] |
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LM_noise = vec([11.777056632828693 28.212360499657112 11.65896012978298 1.7136639003344751 1.4010830630340292 1.0170369261453802 0.8526108635907393 0.6492591656663358 0.5147578711895792 0.4121822015501488 0.26331150699980094 0.21630923911162836 0.18878284405855764 0.12437710482582202 0.10532977709154645 0.09568181515903044 0.08233908793065307 0.07248677367932307]) | ||
HM_noise = vec([0.034555088411955655 0.024422716771949595 0.015625005924072354 0.007526446864085733 0.006977558693116579 0.006408817906669136 0.004061450614873088 0.003648690900613066 0.0032897340686849938 0.0023924606235784505 0.0019473594127964113 0.001684974081805395 0.001398361878093558 0.0012038901607596788 0.0010018837170084481 0.0009016526328880699 0.0007904028897939201 0.0007196501908164429 0.0006046245760704596 0.0005404764935287137 0.0004936775818543692 0.0004446231599090786 0.00041513501203845743]) |
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