Learning on the correctness class for domain inverse problems of gravimetry

• Published in: Machine learning: science and technology Vol. 5; no. 3; pp. 35072 - 35082
• Main Authors: Chen, Yihang, Li, Wenbin
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.09.2024
• Subjects: Density; domain inverse problems; Gravimetry; inverse gravimetry; Inverse problems; Learning; Neural networks; on the correctness class
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/ad72cc

We consider end-to-end learning approaches for inverse problems of gravimetry. Due to ill-posedness of the inverse gravimetry, the reliability of learning approaches is questionable. To deal with this problem, we propose the strategy of learning on the correctness class. The well-posedness theorems are employed when designing the neural-network architecture and constructing the training set. Given the density-contrast function as a priori information, the domain of mass can be uniquely determined under certain constrains, and the domain inverse problem is a correctness class of the inverse gravimetry. Under this correctness class, we design the neural network for learning by mimicking the level-set formulation for the inverse gravimetry. Numerical examples illustrate that the method is able to recover mass models with non-constant density contrast.