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
Integrating the artificial intelligence and hybrid machine learning algorithm…
The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR)…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 5.975 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
High resolution pre-stack seismic inversion using few-shot learning
We propose to use a Few-Shot Learning (FSL) method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data. Recently, artificial neural…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 5.168 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
Estimation of the effectiveness of multi-criteria decision analysis and machi…
Land suitability analysis (LSA) is an evaluation method that measures the degree to which land is suitable for certain land use. The primary aims of this study are to identify potentially viable ag…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 7.032 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Ensemble hybrid machine learning methods for gully erosion susceptibility map…
Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random …
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 7.028 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
Artificial intelligence-based anomaly detection of the Assen iron deposit in …
Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geoch…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 15 hlm PDF, 14.623 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 small magnitude seismic phase identification using 1D deep residua…
Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global covera…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 2.201 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
A new correlation for calculating wellhead oil flow rate using artificial neu…
A separator and multiphase flow meters are considered the most accurate tools used to measure the surface oil flow rates. However, these tools are expensive and time consuming. Thus, this study aim…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 7 hlm PDF, 1.225 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
A convolutional recurrent neural network for strong convective rainfall nowca…
Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, bei…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.368 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Wavefield solutions from machine learned functions constrained by the Helmhol…
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation provides wavefield …
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 3.131 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
Towards a fully data-driven prospectivity mapping methodology: A case study o…
Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using princip…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 20 hlm PDF, 16.632 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
The potential of self-supervised networks for random noise suppression in sei…
Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural network…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 7.813 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
The benefits and dangers of using artificial intelligence in petrophysics
Artificial Intelligence, or AI, is a method of data analysis that learns from data, identify patterns and makes predictions with the minimal human intervention. AI is bringing many benefits to petr…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 4.034 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Sparse inversion-based seismic random noise attenuation via self-paced learning
Seismic random noise reduction is an important task in seismic data processing at the Chinese loess plateau area, which benefits the geologic structure interpretation and further reservoir predicti…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 3.665 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and…
The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D. Block using the Analytic Hierarchy Process (AHP) and Machine learning algorithms along wi…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 6.223 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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