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.

Badlands workshop & examples

R package for archaeological stratigraphy and chronological sequences

stratigraphic machine-learning - active work moved to Predictatops

Stratrigraphy simulation solver for the GeoStats.jl framework

Badlands pre & post-processing

Python package to simulate wave-dominated shallow-marine environments using Storms (2003)'s BarSim

Repository of upcoming abstract submission deadlines for geoscience conferences

Markov chain simulator in a sequence stratigraphic framework

Well logs correlation using dynamic time warping

Docker image for badlands

A simple reactjs module for creating graphical representations of stratigraphies

Plot stratigraphic columns with python.

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

An R package for wavelet based cyclostratigraphic analyses