Ditapis dengan
Automatic variogram inference using pre-trained Convolutional Neural Networks
A novel approach is presented for inferring covariance functions from sparse data using Convolutional Neural Networks (CNNs). Two workflows are proposed: (1) direct prediction of variogram model parameters, and (2) prediction of experimental variogram values at specified lag distances, which are smooth and easily autofit. Workflow 1 achieves an r-squared of 0.80, while Workflow 2 attains a high…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 6.887 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Near surface sediments introduce low frequency noise into gravity models
3D geologic modeling and mapping often relies on gravity modeling to identify key geologic structures, such as basin depth, fault offset, or fault dip. Such gravity models generally assume either homogeneous or spatially uncorrelated densities within modeled rock bodies and overlying sediments, with average densities typically derived from surface and drill-hole sampling. The noise contributed …
- Edisi
- Vol.19, September 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 18 hlm PDF, 18,577 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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 spatial variables into spatially decorrelated factors. However, because no spatial structures are accounted for in the transformation the application of PCA is limited. In geostatistics and blind source …
- 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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