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 continu…
- 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 usa…
- 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
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 …
- 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
Biplots for compositional data derived from generalized joint diagonalization…
Biplots constructed from principal components of a compositional data set are an established means to explore its features. Principal Component Analysis (PCA) is also used to transform a set of spa…
- Edisi
- Vol.8, December 2020
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 8 hlm PDF, 3.744 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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