Ditapis dengan
Deriving big geochemical data from high-resolution remote sensing data via ma…
Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. Th…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 10.726 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Big geochemical data through remote sensing for dynamic mineral resource moni…
Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This direction is more significant where traditional geochemical data are not ideal, which is the case for evaluating unconventional resources, such as tailing…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 13.437 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Artificial intelligence-based anomaly detection of the Assen iron deposit in …
Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical and geophysical surveys, and/or remote sensing. Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials, prompting e…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 15 hlm PDF, 14.623 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Advanced geochemical exploration knowledge using machine learning: Prediction…
In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas. While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data b…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 15 hlm PDF, 28.166 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Towards a fully data-driven prospectivity mapping methodology: A case study o…
Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limit…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 20 hlm PDF, 16.632 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Machine learning-based prediction of trace element concentrations using data …
In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwana Supercontinent). Wedemonstrate that a variety of trace elements, including most of the lanthanides, chalcophile, lithophile, and siderophile ele…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 3.494 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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