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<p>Geophysical inverse problems involve inferring the physical properties and structures of subsurface media using surface observation data, and they are widely applied in resource and energy exploration, geological surveys, and environmental monitoring. Traditional geophysical inversion relies on the construction of mathematical and physical models and efficient numerical computation. However, due to the ill-posed nature of mathematical problems and the complexity of subsurface media, computational methods driven solely by models often lead to issues such as non-uniqueness and computational instability. To address these challenges, data-driven methods utilize large volumes of observational data, employing statistical analysis and machine learning techniques to recognize underlying patterns in the data, thus enhancing the stability and accuracy of inversion results. With advancements in computational technology, knowledge-driven intelligent computing methods have emerged, integrating domain knowledge and prior information to combine physical models with data analysis, thereby improving the stability of problem-solving and enhancing the physical reasonability and computational efficiency of inversion results. In addition, the rise of quantum computing (QC) offers new solutions for geophysical inverse problems. QC, employing the principles of quantum mechanics, can achieve parallel computation and exponential acceleration in handling complex computational tasks. For geophysical inverse problems, the potential of QC lies in its ability to enhance computational efficiency and handle large-scale datasets, enabling faster and more precise inversion. Model, data, and knowledge-driven geophysical inverse problems and intelligent computing represent a comprehensive and innovative research paradigm, fully utilizing physical models, observational data, and domain knowledge to achieve precise subsurface exploration through intelligent computing technologies. This not only advances the research of geophysical inverse problems but also provides more reliable and efficient solutions for practical applications.</p>

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Model, data, and knowledge-driven geophysical inverse problems and intelligent computing

  • 期刊论文

  • 发表日期：

    2026年5月26日

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  • Yanfei Wang

摘要

Geophysical inverse problems involve inferring the physical properties and structures of subsurface media using surface observation data, and they are widely applied in resource and energy exploration, geological surveys, and environmental monitoring. Traditional geophysical inversion relies on the construction of mathematical and physical models and efficient numerical computation. However, due to the ill-posed nature of mathematical problems and the complexity of subsurface media, computational methods driven solely by models often lead to issues such as non-uniqueness and computational instability. To address these challenges, data-driven methods utilize large volumes of observational data, employing statistical analysis and machine learning techniques to recognize underlying patterns in the data, thus enhancing the stability and accuracy of inversion results. With advancements in computational technology, knowledge-driven intelligent computing methods have emerged, integrating domain knowledge and prior information to combine physical models with data analysis, thereby improving the stability of problem-solving and enhancing the physical reasonability and computational efficiency of inversion results. In addition, the rise of quantum computing (QC) offers new solutions for geophysical inverse problems. QC, employing the principles of quantum mechanics, can achieve parallel computation and exponential acceleration in handling complex computational tasks. For geophysical inverse problems, the potential of QC lies in its ability to enhance computational efficiency and handle large-scale datasets, enabling faster and more precise inversion. Model, data, and knowledge-driven geophysical inverse problems and intelligent computing represent a comprehensive and innovative research paradigm, fully utilizing physical models, observational data, and domain knowledge to achieve precise subsurface exploration through intelligent computing technologies. This not only advances the research of geophysical inverse problems but also provides more reliable and efficient solutions for practical applications.