Geostatistical Modeling with gstlearn: A High-Performance Solution for R and Python
<p id="p1"><em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn</italic></em> is a modern, high-performance geostatistical modeling library that provides an open-source alternative to trad...
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| Autores principales: | , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/89840 |
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| Sumario: | <p id="p1"><em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn</italic></em> is a modern, high-performance geostatistical modeling library that provides an open-source alternative to traditional geostatistical software. Designed for both R and Python, <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn</italic></em> leverages an optimized C++ backend to improve computational efficiency, reproducibility, and scalability. As the field of geostatistics continues to embrace open-source software, solutions like <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn</italic></em> enable greater flexibility in data science workflows. This chapter explores the advantages of <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn’s</italic></em> object-oriented framework, which enhances computational performance and cross-platform compatibility. We present structured workflows for variogram modeling, kriging, and conditional simulation using <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn</italic></em>, demonstrating its capability to handle large geospatial datasets efficiently. As high-performance distributed computing (HPDC) and machine learning (ML) continue to influence geostatistical modeling, <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">gstlearn</italic></em> offers a flexible, scalable solution for modern applications in geoscience, environmental studies, and resource management.</p> |
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| ISBN: | 978-1-83968-501-9 |
| DOI: | 10.5772/intechopen.115675 |