为了解决人工智能（Artificial Intelligence）应用在地球物理参数反演中的“黑箱”问题，使得人工智能应用具有物理意义和可解释性及普适性，深度学习耦合物理方法和统计方法的理论和技术在各学科领域正在陆续展开。本研究通过梳理作者20余年的相关研究，在前面归纳和演绎得到人工智能地球物理参数反演范式理论和判定条件基础上，分别给出了遥感参数人工智能反演范式和判定条件。目前大家研究普遍遇到一个问题，很多人工智能参数反演理论模拟数据反演精度非常高，但实际应用反演精度不理想，因此深度学习如何耦合物理方法和统计方法成为当前亟须解决的工程与技术难题。我们以被动微波土壤水分和地表温度反演为例进行阐述，分析表明物理模型本身的精度还要很大的提升空间或者模拟数据只代表现实情况中的少部分情况。因此只利用物理模型模拟数据直接进行反演还存在很大的局限，必须补充大量高精度的多源统计观测数据。同时可以通过利用模拟数据对深度学习训练和用实际数据检验物理模型的误差。统计方法是人类最直观的描述，物理方法是对统计方法的归纳演绎总结，但真实世界的信息或能量传输是按量子形式传递，物理模型只是当前人们认识世界的最高形式，大部分模型并没有刻画好真实信息流。深度学习中的不同神经元更适合描述和表达量子信息的传输方式，以微积分量子能量信息流认识真实世界需要提高人类的思维认知方式，这才是最高模式。如何采集满足真实情况（量子信息或能量传输）的数据显得非常重要，当前可以充分利用物理逻辑推理构建物理方法和统计方法，并在范式理论和判定条件框架指导下利用大数据思维模式提高地球物理参数反演精度。通过物理逻辑推理证明输入变量能唯一确定输出变量是形成具有物理意义和可解释及通用的反演或分类或预测范式的基本条件，从量子信息（能量）传输真实角度控制采集数据质量是地球物理参数高精度反演工程与技术实现的关键，提高微积分量子信息流思维认知和甄辨物理模型的局限对实现人工智能高精度反演具有里程碑意义。

In order to solve the "black box" problem of artificial intelligence application in geophysical parameter retrieval, and make artificial intelligence applications have physical significance, interpretability, and universality, the theory and technology of deep learning coupling physical and statistical methods are gradually being developed in various disciplinary fields. This study summarizes the author's more than 20 years of relevant research, and presents the artificial intelligence inversion paradigms and judgment conditions for remote sensing parameters based on the induction and deduction of the theory and judgment conditions of artificial intelligence geophysical parameter inversion paradigms. At present, a common problem encountered in many studies is that many artificial intelligence parameter retrieval uses theoretical simulation data to achieve high retrieval analysis accuracy, but the actual application retrieval accuracy is not ideal. Therefore, deep learning how to couple physical and statistical methods has become an urgent engineering and technical challenge that needs to be addressed. We will take passive microwave soil moisture and surface temperature retrieval as an example to illustrate that the accuracy of the physical model itself still needs to be greatly improved, or the simulated data only represents a small portion of the actual situation. We believe that there are significant limitations in using only physical models to simulate data for direct retrieval, and high-precision multi-source statistical data must be supplemented. At the same time, we can also improve the physical model by directly using deep learning to simulate data training and testing with actual data to verify the gap between the physical model and the actual situation, determine the errors of the physical model, and thus improve the physical model. Statistical methods are the most intuitive description of human beings, while physical methods summarize and generalize statistical methods. However, information or energy transmission in the real world is transmitted in quantum form, and many physical models have made many simplifications without depicting real physical phenomena well. Different neurons in deep learning are more suitable for describing and expressing the transmission methods of quantum information. Understanding the real world through calculus quantum information flow requires improving our cognitive thinking. How to collect data that meets the real situation (quantum information or energy transmission) is very important. We can fully utilize physical logic reasoning to construct physical formulas and statistical methods, and use big data thinking mode to improve the accuracy of geophysical parameter inversion under the guidance of paradigm theory and judgment condition framework. Proving through physical logic reasoning that the input variable can uniquely determine the output variable is a fundamental condition for forming a physically meaningful, interpretable, and universal retrieval or classification or prediction paradigm. Controlling the quality of collected data from the perspective of quantum information (energy) transmission is the key to achieving high-precision inversion engineering and technology for geophysical parameters. Improving the cognitive understanding of quantum information flow in calculus and identifying the limitations of physical models are of milestone significance for achieving high-precision inversion in artificial intelligence.