模型、数据与知识驱动的地球物理反问题及智能计算

Key Points

• Geophysical inverse problems aim to determine subsurface properties from surface observation data, which can be complex and unstable.
• Using statistical analysis and machine learning, data-driven methods can stabilize inversion results and reveal patterns within large datasets.
• Knowledge-driven approaches integrate physical models with data analysis, enhancing computational efficiency of solving inverse problems.
• Quantum computing's potential to accelerate processing large-scale datasets may lead to faster and more precise inversion outcomes.

Abstract