List of resources for mineral exploration and machine learning, generally with useful code and examples.
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Updated
Nov 14, 2024
List of resources for mineral exploration and machine learning, generally with useful code and examples.
Basin and Landscape Dynamics model
Stratigraphic pick prediction via supervised machine-learning
python port of the USGS bedforms software tool
GebPy is a Python-based, open source tool for the generation of geological data of minerals, rocks and complete lithological sequences. The data can be generated randomly or with respect to user-defined constraints, for example a specific element concentration within minerals and rocks or the order of units within a complete lithological profile.
R package for archaeological stratigraphy and chronological sequences
Badlands workshop & examples
Stratrigraphy simulation solver for the GeoStats.jl framework
Badlands pre & post-processing
Repository of upcoming abstract submission deadlines for geoscience conferences
Python package to simulate wave-dominated shallow-marine environments using Storms (2003)'s BarSim
Markov chain simulator in a sequence stratigraphic framework
Well logs correlation using dynamic time warping
A simple reactjs module for creating graphical representations of stratigraphies
Plot stratigraphic columns with python.
Docker image for badlands
a paleocurrent plotter for plotting a histogram and a rose diagram out of paleocurrent direction values given in azimuth degrees
An R-function that makes plotting geological depth/age data easy
Estimate Age-Depth Models and Transform Data
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