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
Do more with less: Exploring semi-supervised learning for geological image cl…
Labelled datasets within geoscience can often be small, with data acquisition both costly and challenging, and their interpretation and downstream use in machine learning difficult due to data scarcity. Deep learning algorithms require large datasets to learn a robust relationship between the data and its label and avoid overfitting. To overcome the paucity of data, transfer learning has been e…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 20 hlm PDF, 10.680 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Developing ground motion prediction models for West Java: A machine learning …
Indonesia, one of the most earthquake-prone countries in the world, is currently developing an Earthquake Early Warning (EEW) system. A key component of this system, the Regional EEW, relies on Ground Motion Prediction models (GMPMs) to issue end-user alerts. However, in West Java, one of the pilot regions for this project, there is a lack of region-specific GMPMs essential for accurate early w…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 10,819 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Deformation analysis by an improved similarity transformation
In this contribution, deformation analysis is rigorously performed by a non-linear 3-D similarity transformation. In contrast to traditional methods based on linear least-squares (LLS), here we solve a non-linear problem without any linearization. To achieve this goal, a new weighted total least-squares (WTLS) approach with general dispersion matrix is implemented to deformation analysis proble…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 705 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Classification of geological borehole descriptions using a domain adapted lar…
Geological borehole descriptions contain detailed textual information about the composition of the subsurface. However, their unstructured format presents significant challenges for extracting relevant features into a structured format. This paper introduces GEOBERTje: a domain adapted large language model trained on geological borehole descriptions from Flanders (Belgium) in the Dutch language…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 15 hlm PDF, 2.295 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Chemical map classification in XMapTools
Chemical mapping using electron beam or laser instruments is an important analytical technique that allows the study of the compositional variability of materials in two dimensions. While quantitative compositional mapping of minerals has received considerable attention over the last two decades, pixel misclassification in commonly used software solutions remains a fundamental limitation affect…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 17 hlm PDF, 30.775 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Automatic variogram inference using pre-trained Convolutional Neural Networks
A novel approach is presented for inferring covariance functions from sparse data using Convolutional Neural Networks (CNNs). Two workflows are proposed: (1) direct prediction of variogram model parameters, and (2) prediction of experimental variogram values at specified lag distances, which are smooth and easily autofit. Workflow 1 achieves an r-squared of 0.80, while Workflow 2 attains a high…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 6.887 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Advanced AI techniques for landslide susceptibility mapping and spatial predi…
Landslides, a global phenomenon, significantly impact economies and societies, especially in densely populated areas. Effective mitigation requires awareness of landslide risks, yet temporal links between occurrences are often neglected, challenging model performance due to non-stationary triggering and predisposing factors. This study presents a novel landslide susceptibility model that incorp…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 11 hlm PDF, 3.550 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A new inversion algorithm (PyMDS) based on the Pyro library to use chlorine 3…
Paleoseismology (study of earthquakes that occurred before records were kept and before instruments can record them) provides useful information such as recurrence periods and slip rate to assess seismic hazard and better understand fault mechanisms. Chlorine 36 is one of the paleoseismological tools that can be used to date scarp exhumation associated with earthquakes events. We propose an al…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 7 hlm PDF, 3.302 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Revolutionizing the future of hydrological science: Impact of machine learnin…
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, w…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 16 hlm PDF, 2.174 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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