Ditapis dengan
Geological object recognition in legacy maps through data augmentation and tr…
Maps are crucial tools in geosciences, providing detailed representations of the spatial distribution and relationships among geological features. Accurate recognition and classification of geological objects within these maps are essential for applications in resource exploration, environmental management, and geological hazard assessment. Along the years, many legacy geological maps have been…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 8 hlm PDF, 4.999 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS
Digital geological mapping has experienced significant growth over the past three decades due to the advent of commercial geographical information systems, advances in global positioning systems, and the availability of portable hand-held devices, such as mobile personal computers (PCs), smartphones, and tablets. Numerous software packages have been developed to collect, combine, organise, visu…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 11.716 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Improved reservoir characterization of thin beds by advanced deep learning ap…
Targeting reservoirs below seismic resolution presents a major challenge in reservoir characterization. High-resolution seismic data is critical for imaging the thin gas-bearing Khadro sand facies in several fields within the Lower Indus Basin (LIB). To truly characterize thin beds below tuning thickness, we showcase an optimally developed deep learning technique that can save up to 75% turn-ar…
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 15.238 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Advancing geological image segmentation: Deep learning approaches for rock ty…
This study aims to tackle the obstacles linked with geological image segmentation by employing sophisticated deep learning techniques. Geological formations, characterized by diverse forms, sizes, textures, and colors, present a complex landscape for traditional image processing techniques. Drawing inspiration from recent advancements in image segmentation, particularly in medical imaging and o…
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 16 hlm PDF, 17.044 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Semantically triggered qualitative simulation of a geological process
The field of geology has been the subject of a range of research efforts aiming to formalize geological domain knowledge, notably through geological domain ontologies. The main focus of existing geological ontologies primarily lies in describing static geological objects and their properties, paying less attention to the knowledge concerning geological processes and events. Meanwhile, the geolo…
- Edisi
- Vol.21, March 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 2.077 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Construction and application of a multilevel geohazard domain ontology: A cas…
The occurrence of geohazards entails sudden, unpredictable, and cascading effects, with numerous conceptual frameworks and intricate spatiotemporal relationships existing between hazard events. Presently, the absence of a unified mechanism for describing and expressing geohazard knowledge poses substantial challenges in terms of sharing and reusing domain-specific knowledge pertaining to geohaz…
- Edisi
- Vol.20, December 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 3.645 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Uncertainties in 3-D stochastic geological modeling of fictive grain size dis…
Geological 3-D models are very useful tools to predict subsurface properties. However, they are always subject to uncertainties, starting from the primary data. To ensure the reliability of the model outputs and, thus, to support the decision-making process, the incorporation and quantification of uncertainties have to be integrated into the geo-modeling strategies. Among all modeling approache…
- Edisi
- Vol.19, September 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 20 hlm PDF, 35.441 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Subsurface geometry of the Revell Batholith by constrained geophysical modell…
The Revell batholith is located within the Western Wabigoon terrane of the Superior Province, Northwestern Ontario, Canada, and is a potential site for a deep geological repository (DGR). This batholith is considered to have favourable geoscientific characteristics for hosting a DGR, including a sufficient volume of relatively homogenous rock. The subsurface geometry of the batholith plays an i…
- Edisi
- Vol.19, September 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 13.525 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learn…
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed f…
- Edisi
- Vol.18 June 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 11 hlm PDF, 9.277 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Applying machine learning methods to predict geology using soil sample geoche…
In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE and Edited Nearest Neighbor (SMOTEENN), and SMOTE and Tomek links (SMOTETomek)). SMOTE performed best with ADASYN and…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 13.315 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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