Water resource forecasting with machine learning and deep learning: A sciento…
Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and eme…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 4.935 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Reservoir evaluation using petrophysics informed machine learning: A case study
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical inf…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 19.964 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Research on the prediction method for fluvial-phase sandbody connectivity bas…
The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while …
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 6.809 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Reconstruction of lithofacies using a supervised Self-Organizing Map: Applica…
Recently, machine learning (ML) has been considered a powerful technological element of different society areas. To transform the computer into a decision maker, several sophisticated methods and a…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 3.426 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Prediction of seismic-induced bending moment and lateral displacement in clos…
Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerica…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 8.793 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Locally varying geostatistical machine learning for spatial prediction
Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these method…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 9.301 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Exploring emerald global geochemical provenance through fingerprinting and ma…
Emeralds – the green colored variety of beryl – occur as gem-quality specimens in over fifty deposits globally. While digital traceability methods for emerald have limitations, sample-based app…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 23.257 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Benchmarking data handling strategies for landslide susceptibility modeling u…
Machine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 33.426 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Machine learning elucidates the anatomy of buried carbonate reef from seismic…
A carbonate build-up or reef is a thick carbonate deposit consisting of mainly skeletal remains of organisms that can be large enough to develop a favourable topography. Delineation of such geologi…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 9.753 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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
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