Ditapis dengan
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 learning and deep learning, convolutional neural networks (CNNs) are applied for interpolating irregularly sampled seismic data. CNN based approaches can address the apparent defects of traditional inter…
- 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), random forest (RF), and multivariate adaptive regression splines (MARS); novel ensemble approaches i.e. MLP-Bagging, KLR-Bagging, RF-Bagging and MARS-Bagging in the Kurseong-Himalayan region. For th…
- 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 network (ANN) demonstrates great advantages for seismic inversion because of its powerful feature extraction and parameter learning ability. Hence, ANN method could provide a high resolution inversio…
- 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 variable is observed at a few sampling locations due to the relatively time-consuming and costly process of obtaining measurements. In contrast, auxiliary variables are often exhaustively observed within th…
- 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
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 sub-space (RSS) and rotation forest (RTF) ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility (GES) in Hinglo river basin. 120 gullies were marked and grouped into four-f…
- 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 the kinematics of reflection and migration smiles. Several ‘conventional’ filtering methods exist and recently deep learning based workflows have been proposed. A deep learning workflow can be a …
- 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 geochemical and geophysical surveys, and/or remote sensing. Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials, prompting e…
- 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 improve confidence in geochemical data and gain more insights into potentially mineralized areas. While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data b…
- 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 coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more ro…
- 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 belt. At present, there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt. One of the reasons is that the genetic tectonic setting of the Carboniferous vo…
- 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
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