an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery
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Updated
May 31, 2024 - Python
an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery
List of resources for mineral exploration and machine learning, generally with useful code and examples.
Thermodynamic calculations and diagrams for geochemistry
Geochemistry pipeline for processing and visualising data from IRMS, IC and ICP-MS
A set of tools for getting the most from your geochemical data.
In this notebook, I built gradient boosting classifier and LSTM model to classify and predict the mineral scaling potential of formation water in US shale basins.
Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome
An application based on Python and designed as a solution for geology related daily work.
The Virtual Planet Simulator
Easily calling PhreeqcRM from Matlab
Wrapper scripts useful for studies involving the EQ3/6 geochemistry code
Repository for williams et al. (2024) Geochemical proxies for deep-sea temperature and nutrient content in cold-water bamboo corals
Package for ApThermo (melt hygrometry), ApREE2O3 (magma oxybarometry), and more (to be added).
Geosciences reference data sets for R
Data and code for Lago Pato, Torres del Paine in Roberts SJ, McCulloch RDM, Emmings J, Davies SJ, Van Nieuwenhuyze W, Sterken M, et al. (2022). Late glacial and Holocene palaeolake history of the Última Esperanza region of Southern Patagonia. Frontiers in Earth Science.
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.
MC_Diffusion - a C++ program to calculate multi-component diffusion
Analysis and Visualization toolkit for plaNetary Inferences
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