• ISSN 2097-1893
  • CN 10-1855/P
路书鹏,徐亚,张倩文,褚伟. 2024. 岩性预测综合地球物理解释方法综述. 地球与行星物理论评(中英文),55(0):1-12. doi: 10.19975/j.dqyxx.2023-047
引用本文: 路书鹏,徐亚,张倩文,褚伟. 2024. 岩性预测综合地球物理解释方法综述. 地球与行星物理论评(中英文),55(0):1-12. doi: 10.19975/j.dqyxx.2023-047
Lu S P, Xu Y, Zhang Q W, Chu W. 2024. Review of lithology prediction and comprehensive geophysical interpretation methods. Reviews of Geophysics and Planetary Physics, 55(0): 1-12 (in Chinese). doi: 10.19975/j.dqyxx.2023-047
Citation: Lu S P, Xu Y, Zhang Q W, Chu W. 2024. Review of lithology prediction and comprehensive geophysical interpretation methods. Reviews of Geophysics and Planetary Physics, 55(0): 1-12 (in Chinese). doi: 10.19975/j.dqyxx.2023-047

岩性预测综合地球物理解释方法综述

Review of lithology prediction and comprehensive geophysical interpretation methods

  • 摘要: 探测地下结构并进行地质解释是地球物理研究的主要目标,根据地球物理数据反映的地下物质的物理属性,如密度、速度、磁化率、电阻率等特征可确立地层结构及其性质. 由于单一地球物理方法的多解性等局限,采用多种方法综合开展地球物理解释是目前可行的有效手段. 本文针对地下岩性预测这一目标,总结了开展岩性预测的综合地球物理解释方法基本原则及步骤,并按照知识驱动和数据驱动两类技术路线对岩性综合预测的主要技术方法进行了总结. 知识驱动方法利用先验信息,简单直接易于理解,但对复杂及高维数据适应能力弱;数据驱动方法使用数理统计等策略可有效挖掘各类数据间的关系,适应复杂应用场景能力强. 在解决实际问题过程中,有监督机器学习方法以充分的岩石物理性质研究为基础,不仅引入了先验知识而且充分发挥了自身的数据挖掘能力,提高岩性预测解释的准确性,更好地建立地球物理与地质信息的对应关系,支撑资源能源等勘探需求.

     

    Abstract: The primary objective of geophysical research is the exploration of underground structures and to serve as a valuable tool for geological interpretation. The formation structure and properties can be determined by analyzing the physical properties of the underground medium reflected by geophysical data, such as density, velocity, magnetic susceptibility, resistivity, and more. Given the numerous solutions of a single geophysical method, comprehensive geophysical interpretation is currently a feasible and effective approach. This study explores lithology prediction, providing a summary of the basic principles and steps of comprehensive geophysical interpretation methods for lithology prediction. Additionally, it outlines the main technical methods of comprehensive lithology prediction involving two kinds of technical routes: knowledge-driven and data-driven. The knowledge-driven method uses prior information. It is simple, direct, and easy to understand, but has weak adaptability to the complexity and high dimension data. The data-driven method employs a mathematical statistics strategy to explore the relationship between data and has a robust capacity to adapt to complex scenarios. In solving practical problems, the supervised machine learning method, based on sufficient rock physical properties research, not only incorporates prior knowledge but also maximizes its internal data exploration ability. It can enhance the accuracy of lithology prediction and interpretation, better establish the corresponding relationship between geophysical and geological information, and support the exploration needs of resources and energy.

     

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