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<p>The integration of big data analytics, artificial intelligence (AI), and machine learning (ML) with traditional geostatistical and Bayesian methodologies presents opportunities and challenges in spatial data analysis and uncertainty quantification. This special issue, emerging from the International Association for Mathematical Geosciences (IAMG) conference 2023 in Trondheim, Norway, comprises seven papers that investigate the theoretical and practical implications of this methodological convergence. Throughout these papers there is a consistent message: AI and ML adapted to address the fundamental questions of geostatistics such as data conditioning of spatial data in a geologically realistic context, accounting for scale and model uncertainty and validating predictive performance. In this way, AI-based approaches are used and extended in the hope of becoming a mainstay among established methods in the toolbox of geostatisticians. While progress in AI and ML has relied considerably on experimentation, there is also a need to develop more abstract thoughts for gaining fundamental insight which can substantially strengthen interpretability and trust in AI-based models. Following the discussion at the IAMG 2023 conference and the topic of this special issue, this is a chance to reflect on the current opportunities and challenges in the geostatistics field with the tremendous activity in AI.</p>

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Introduction to the Special Issue: How do Big Data, AI, and ML Challenge Geostatistical and Bayesian Formalisms?

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Richard Sinding-Larsen,
  • Jo Eidsvik,
  • Steinar Ellefmo,
  • Michael J. Pyrcz

摘要

The integration of big data analytics, artificial intelligence (AI), and machine learning (ML) with traditional geostatistical and Bayesian methodologies presents opportunities and challenges in spatial data analysis and uncertainty quantification. This special issue, emerging from the International Association for Mathematical Geosciences (IAMG) conference 2023 in Trondheim, Norway, comprises seven papers that investigate the theoretical and practical implications of this methodological convergence. Throughout these papers there is a consistent message: AI and ML adapted to address the fundamental questions of geostatistics such as data conditioning of spatial data in a geologically realistic context, accounting for scale and model uncertainty and validating predictive performance. In this way, AI-based approaches are used and extended in the hope of becoming a mainstay among established methods in the toolbox of geostatisticians. While progress in AI and ML has relied considerably on experimentation, there is also a need to develop more abstract thoughts for gaining fundamental insight which can substantially strengthen interpretability and trust in AI-based models. Following the discussion at the IAMG 2023 conference and the topic of this special issue, this is a chance to reflect on the current opportunities and challenges in the geostatistics field with the tremendous activity in AI.