Ditapis dengan
Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic …
The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Sal…
- Edisi
- Vol.15, Issue 1, January 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 29 hlm PDF, 13.887 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
When linear inversion fails: Neural-network optimization for sparse-ray trave…
In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice, and an inverse modeling algorithm that uses an ANN to estimate the velocity structure from the “observed” travel-time …
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 11.246 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 developed fully reversible or fully invertible networks can mostly avoid memory limitations by recomputing the states during the backward pass through the network. This results in a low and fixed memory …
- 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
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 context, we propose the Earthquake Graph Network (EQGraphNet) to enhance the performance of single-station magnitude estimation. The backbone of the proposed model consists of eleven convolutional neural…
- 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
Improved frost forecast using machine learning methods
Frosts are one of the atmospheric phenomena with one of the larger negative effects on the agricultural sector in the southern region of Brazil, therefore, an earlier forecast can minimize their impacts. In the present work, artificial neural networks (ANNs) techniques were applied in order to improve the predicting capabilities of frost events in southern Brazil. In the study, two multilayer p…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 19.643 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Estimation of dusk time F-region electron density vertical profiles using LST…
The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System (GNSS). Accurate estimation of ionospheric delays through vertical electron density profiles is…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 9.300 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Estimating relative diffusion from 3D micro-CT images using CNNs
In recent years, convolutional neural networks (CNNs) have demonstrated their effectiveness in predicting bulk parameters, such as effective diffusion, directly from pore-space geometries. CNNs offer significant computational advantages over traditional methods, making them particularly appealing. However, the current literature primarily focuses on fully saturated porous media, while the parti…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 2.839 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Determination of future land use changes using remote sensing imagery and art…
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource extraction, as well as natural phenomena, for example, erosion and climate change. LULC changes significantly impact ecosystem services, biodiversity, and human welfare. In this study, LULC changes i…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 6.696 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 mainly due to high acquisition costs. This study introduces a hybrid machine learning approach to generating synthetic DTS logs. Five wireline logs such as gamma ray (GR), density (RHOB), neutron poros…
- 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
ResGraphNet: GraphSAGE with embedded residual module for prediction of global…
Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded resi…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 2.716 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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