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
Pore size classification and prediction based on distribution of reservoir fl…
Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids. Traditional methods for predicting pore size distribution (PSD), relyin…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 23 hlm PDF, 24.240 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
High-resolution seismic inversion method based on joint data-driven in the ti…
Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains. Time-domain inversion has stronger stability and noise resistance co…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 15.975 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Fully invertible hyperbolic neural networks for segmenting large-scale surfac…
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentation. Recently dev…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 3.537 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Forecast future disasters using hydro-meteorological datasets in the Yamuna r…
This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. Th…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 23 hlm PDF, 18,684 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
EQGraphNet: Advancing single-station earthquake magnitude estimation via deep…
Magnitude estimation is a critical task in seismology, and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution. In this con…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 4.434 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Enhancing economic sustainability in mature oil fields: Insights from the clu…
Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, compa…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 1.767 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Enhanced permeability prediction in porous media using particle swarm optimiz…
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the e…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 9.874 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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