Laporan akhir kegiatan 2007: pengelolaan basis data spasial kelautan dan kedi…
Dengan masih sendirinya pengembangan sistem interoperabilitas, maka pengembangan sistem ini lambat ditambah lagi dengan belum adanya fasilitas online web, maka sistem ini belum beroperasi secara pe…
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- ISBN/ISSN
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- Deskripsi Fisik
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- Judul Seri
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- Lap.Kegiatan 2007/25 BAD l
Transfer learning for well logging formation evaluation using similarity weights
Machine learning has been widely applied in well logging formation evaluation studies. However, several challenges negatively impacted the generalization capabilities of machine learning models in …
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 22.2488 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Robust high frequency seismic bandwidth extension with a deep neural network …
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various determini…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 23.132 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
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
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
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
Synthetic shear sonic log generation utilizing hybrid machine learning techni…
Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability m…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 19.380 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Irregularly sampled seismic data interpolation via wavelet-based convolutiona…
Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine lea…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 5.141 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Geostatistical semi-supervised learning for spatial prediction
Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms. Typically, the target variab…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 17 hlm PDF, 10.115 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Attenuation of seismic migration smile artifacts with deep learning
Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 12.044 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
A study on geological structure prediction based on random forest method
The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt, which is one of the key areas to study the formation and evolution of the Central Asian orogenic bel…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 7.913 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Unilateral Alignment: An interpretable machine learning method for geophysica…
Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue, so the trained model cannot well generalize to the unseen data without …
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 3.656 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA
Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysica…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.490 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 magmati…
- 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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