Ditapis dengan
A Machine Learning Classification Approach to Geotechnical Characterization U…
Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock format…
- Edisi
- Vol.15, Issue 3, March 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 17 hlm PDF, 1.559 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Tools for Predicting Long Runout Landslides
One of the most important issues in landslide hazard management is predicting the runout of a landslide event. Current technology and modeling help to analyze landslides in terms of overall stability, triggers, and sensitivity to environmental changes, but the length of the runout remains a difficult variable to predict. In this study, we review how runout is measured and conclude that the land…
- Edisi
- Vol.15, Issue 2, February 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 17 hlm PDF, 3.078 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Foundations for an Operational Earthquake Prediction System
Earthquake prediction is one of the most challenging enterprises of science. Any prediction system must be based on the search for a precursor appearing during the preparation phase of an earthquake in the ground, atmosphere, or ionosphere that can anticipate its occurrence. We present methods to detect potential pre-earthquake anomalies. In particular, we show the analysis of lithospheric, atm…
- Edisi
- Vol.15, Issue 2, February 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 22 hlm PDF, 2.855 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
The role of artificial intelligence and IoT in prediction of earthquakes: Review
Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. There has been an increasing interest in the prediction of earthquakes and in gaining a comprehensive understanding of the mechanisms that underlie their generation, yet earthquakes are the least predictable natural disaster. Satellite data, g…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 19 hlm PDF, 5.965 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Research on the prediction method for fluvial-phase sandbody connectivity bas…
The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes. Aiming at the above problem in the Bohai…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 6.809 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Enhanced permeability prediction in porous media using particle swarm optimiz…
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness of individual prediction methods. This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model (PSO-PIP), which incorporates a particle swarm o…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 9.874 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Optimization of shale gas fracturing parameters based on artificial intellige…
Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of shale gas wells. In this paper, according to the shale gas reservoir characteristic of the Fuling marine Longmaxi Formation, a single-well geological model was established using the reservoir numerica…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 13.443 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
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
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
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