Loaming: "A method of geochemical prospecting in which samples of soil or other surficial material are tested for traces of the metal desired, its presence presumably indicating a near-surface orebody." - The American Geosciences Institute
This is a Julia tool that employs Monte Carlo methods to simulate the spread of geochemical anomalies mineral deposits could present on mountain sides. It is desgined with the gold prospector in mind - whereby these 'geochemical anomalies' take the form of gold-specks. It is able to simulate loaming (samples to likely deposit) and reverse-loaming (deposit to likely samples), maximising the theoretical chances of finding a 'patch' using what very limited information may be avaliable. It is currently able to model 'point' outcrops and lode/vein outcrops (including dip, strike and length).
This tool could be of use in arid areas with a relatively thin overburden layer. The user is cautioned against using the tool in areas with significant overburden, glacial activity and/or landslides.
Functionality is being added to, suggestions are welcome.
An example of the log-anomaly presented by an auriferous reef on the slopes of the Jukes-Darwin Mining Field from a variety of perspectives:
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To make (useless and computationally expensive) colourful diagrams like this simply adjust the configurations in the config.toml file (instructions are in comments in the file) and run the sim.jl file. Elevation data is widely avaliable (in Australia) from Elivs.
PlotlyJS, Images, ImageView, ArchGDAL, StatsBase and DelimitedFiles will need to be installed prior to running.
- Keep either res small (less than ~1000 if possible) or use smaller TIF files as PlotlyJS struggles to display larger images and simulation time also drastically increases with larger values of res
- It is possible to produce very clean images with not that many simulations (~1000 per sample), unless display.log is true, in which case ~10,000 simulations per sample is needed. Simulating deposits with display.log true can require in the order of 100,000s of simulations to produce a crisp images. Simulating deposits also tends to demand a higher res value