GEOROC and GeoReM—Linking the information of two Geological databases

Schramm, B.; Jochum, K. P.; Sarbas, Bärbel; Nohl, Uwe

doi:10.1016/j.gca.2006.06.1045

Cited by 3 publications

(2 citation statements)

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“…In the Earth sciences there is expanding appreciation of the value of readily accessible online databases of experimental and analytical data [ Staudigel et al , 2003]. In recent years, databases have been constructed or are being constructed to compile chemical compositions of rocks and sediments, including PetDB [ Lehnert et al , 2000], GEOROC [ Schramm et al , 2006], SedDB [ Lehnert et al , 2007], and NAVDAT [ Carlson et al , 2001], as well as magnetic rock and mineral properties (MAGIC [ Koppers et al , 2005]), geochronologic data (CHRONOS [ Cox and Richard , 2005]), and others. In many respects, these database efforts are modeled after the large databases of seismic data managed by IRIS [ Ahern , 2003], which have proved so important to geophysical studies.…”

Section: Introductionmentioning

confidence: 99%

Library of Experimental Phase Relations (LEPR): A database and Web portal for experimental magmatic phase equilibria data

Hirschmann

Ghiorso

Davis

et al. 2008

Geochem Geophys Geosyst

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[1] We describe the Library of Experimental Phase Relations (LEPR), which is a new Web-based database of experiments pertaining to magmatic phase equilibria. The database includes information from experiments related to natural or near-natural magmatic silicate phase equilibria. At present, the database includes more than 6,600 experiments with liquids ranging in composition from komatiite to rhyolite performed at temperatures between 500 and 2500°C and at pressures up to 27.5 GPa. Here we describe the organization of data and metadata that are included in the database as well as the database structure and the implementation of its Web interface. We discuss a number of challenges and considerations related to construction of a database of experimental data that influence both the selection and presentation of the information incorporated in LEPR. Finally, we survey durations of experiments in LEPR and discuss the relationship between duration and the probability of crystal-liquid equilibrium in petrologic experiments.

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Section: Introductionmentioning

confidence: 99%

Library of Experimental Phase Relations (LEPR): A database and Web portal for experimental magmatic phase equilibria data

Hirschmann

Ghiorso

Davis

et al. 2008

Geochem Geophys Geosyst

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“…The original data used to train the Qmin algorithm are derived from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, supported by the Max Planck Institute for Chemistry (Sarbas, 2021;Schramm et al, 2006). The GEOROC initiative collects and organizes several standardized spreadsheets with geoscienti c data in tables and other supplementary materials.…”

Section: Data Sourcementioning

confidence: 99%

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

Silva

Ferreira

Costa

et al. 2021

Preprint

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Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.

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Qmin – A machine learning-based application for processing and analysis of mineral chemistry data

Silva

Ferreira

Costa

et al. 2021

Computers & Geosciences

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GEOROC and GeoReM—Linking the information of two Geological databases

Cited by 3 publications

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Library of Experimental Phase Relations (LEPR): A database and Web portal for experimental magmatic phase equilibria data

Library of Experimental Phase Relations (LEPR): A database and Web portal for experimental magmatic phase equilibria data

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

Qmin – A machine learning-based application for processing and analysis of mineral chemistry data

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