Ditapis dengan
Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coa…
This study aims to establish a scientific and methodological basis for predicting shoreline positions using modern data analysis and machine learning techniques. The focus area is a 5 km section of the Ural coast along Baydaratskaya Bay in the Kara Sea. This region was selected due to its diverse geomorphological features, varied lithological composition, and significant presence of permafrost …
- Edisi
- Vol.15, Issue 1, January 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 19 hlm PDF, 2.124 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
Accounting for the Compositional Nature of Geochemical Data to Improve the In…
This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements.…
- Edisi
- Vol.15, Issue 1, January 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 16 hlm PDF, 6,837 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
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
Streamlining geoscience data analysis with an LLM-driven workflow
Large Language Models (LLMs) have made significant advancements in natural language processing and human-like response generation. However, training and fine-tuning an LLM to fit the strict requirements in the scope of academic research, such as geoscience, still requires significant computational resources and human expert alignment to ensure the quality and reliability of the generated conten…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 6.546 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
GeoCoDA: Recognizing and validating structural processes in geochemical data.…
Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-sum constraint, that is, the simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice struct…
- Edisi
- Vol.22, June 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 15 hlm PDF, 1.985 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Using a 3D heat map to explore the diverse correlations among elements and mi…
This paper presents an enhanced 3D heat map for exploratory data analysis (EDA) of open mineral data, addressing the challenges caused by rapidly evolving datasets and ensuring scientifically meaningful data exploration. The Mindat website, a crowd-sourced database of mineral species, provides a constantly updated open data source via its newly established application programming interface (API…
- Edisi
- Vol.21, March 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 5.984 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Considerations in the application of machine learning to aqueous geochemistry…
Since the advent of modern computing, geochemists have increasingly relied on computers to garner efficiencies in calculations, data analysis, and data presentation. Entirely new fields, such as Monte Carlo-based simulation and geochemical modeling, have developed under this paradigm. With continued growth in computing power, machine learning has become an increasingly popular tool in aqueous g…
- Edisi
- Vol.3-4, December 2019
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 2.751 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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