Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems  

作　　者：Huanfeng SHEN Liangpei ZHANG 

机构地区：[1]School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China [2]Key Laboratory of Geographic Information System(Ministry of Education),Wuhan University,Wuhan 430079,China [3]State Key Laboratory of Information Engineering,Survey Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China

出　　处：《Science China Earth Sciences》2023年第3期568-582,共15页中国科学（地球科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.42130108)。

摘　　要：Building the physics-driven mechanism model has always been the core scientific paradigm for parameter estimation in Earth surface systems,and developing the data-driven machine learning model is a crucial way for paradigm transformation in geoscience research.The coupling of mechanism and learning models can realize the combination of“rationalism”and“empiricism”,which is one of the most concerned research hotspots.In this paper,for remote sensing inversion and dynamic simulation,we deeply analyze the internal bottleneck and complementarity of mechanism and learning models and build a coupling paradigm framework with mechanism-learning cascading model,learning-embedded mechanism model,and mechanism-infused learning model.We systematically summarize ten specific coupling methods,including preprocessing and initialization,intermediate variable transfer,post-refinement processing,model substitution,model adjustment,model solution,input variable constraints,objective function constraints,model structure constraints,hybrid,etc.,and analyze the main existing problems and future challenges.The research aims to provide a new perspective for in-depth understanding and application of the mechanism-learning coupling model and provide theoretical and technical support for improving the inversion and simulation capabilities of parameters in Earth surface systems and serving the development of Earth system science.

关 键 词：Mechanism model Machine learning Model coupling Remote sensing inversion Numerical simulation 

分 类 号：P3[天文地球—地球物理学] TP181[自动化与计算机技术—控制理论与控制工程]