Rapid identification of high-quality marine shale gas reservoirs based on the…
The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage. However, due to the serious nonlinear relationship between the lo…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.091 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Random forest for spatial prediction of censored response variables
The spatial prediction of a continuous response variable when spatially exhaustive predictor variables are available within the region under study has become ubiquitous in many geoscience fields. T…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 5.490 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Near-surface velocity inversion from Rayleigh wave dispersion curves based on…
The utilization of urban underground space in a smart city requires an accurate understanding of the underground structure. As an effective technique, Rayleigh wave exploration can accurately obtai…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 2.687 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
Hydrocarbon detections using multi-attributes based quantum neural networks i…
A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields. The proposed multi-attributes based quantum neural networks for hydrocarbon dete…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 3.472 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Flood susceptibility assessment using artificial neural networks in Indonesia
Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, gove…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 1.503 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Enhancing lithofacies machine learning predictions with gamma-ray attributes …
Derivative and volatility attributes can be usefully calculated from recorded gamma ray (GR) data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such attribute…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 17 hlm PDF, 5.728 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Deep convolutional autoencoders as generic feature extractors in seismologica…
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this id…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 1.991 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Data-driven approaches for time series prediction of daily production in the …
The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has e…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.029 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Classification random forest with exact conditioning for spatial prediction o…
Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region. Even thoug…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 7.744 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Capsule network-based approach for estimating grassland coverage using time s…
The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Moni…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 3,657 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Arriving at estimates of a rate and state fault friction model parameter usin…
The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the crit…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 1.956 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Application of neural network to speed-up equilibrium calculations in composi…
Injecting carbon dioxide (CO2) into reservoirs is a widely recognized method for enhanced oil recovery (EOR) and carbon storage. This study introduces an innovative Artificial neural network (ANN)-…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 1.456 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
ShakeDaDO: A data collection combining earthquake building damage and ShakeMa…
In this article, we present a new data collection that combines information about earthquake damage with seismic shaking. Starting from the Da.D.O. database, which provides information on the damag…
- Edisi
- -
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 9.708 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Seismic labeled data expansion using variational autoencoders
Supervised machine learning algorithms have been widely used in seismic exploration processing, but the lack of labeled examples complicates its application. Therefore, we propose a seismic labeled…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 7 hlm PDF, 3.230 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Local earthquakes detection: A benchmark dataset of 3-component seismograms b…
Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publi…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 3.299 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Exact Conditioning of Regression Random Forest for Spatial Prediction
Regression random forest is becoming a widely-used machine learning technique for spatial prediction that shows competitive prediction performance in various geoscience fields. Like other popular m…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 6.201 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Automatic fault instance segmentation based on mask propagation neural networ…
Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil. Recently, significant advances have been made toward…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 5 hlm PDF, 1.456 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
X-ray Micro-CT based characterization of rock cuttings with deep learning
Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually …
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 6.037 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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