Agentic Full-Waveform Inversion Using Local and Global Optimizers

doi:10.1007/s11004-026-10342-5

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Agentic Full-Waveform Inversion Using Local and Global Optimizers

2026-09-03

PRE

Daniele Colombo *

Runhai Feng

Erşan Türkoğlu

Ernesto Sandoval-Curiel

DOI：10.1007/s11004-026-10342-5

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Abstract

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A novel machine learning approach to seismic full waveform inversion is developed by adopting agentic artificial intelligence (AI) concepts. The method involves the concatenation of multiple learning algorithms to generate an autonomous system that efficiently explores and learns the parameter space to perform high-resolution full-waveform inversion. Physics-based exploration agents are used to generate high-quality supervisory signals in the form of pseudo labels to reinforce the learning of the local data domain. Reinforcement learning agents, rooted in either L2 local or stochastic global optimizers, are coupled to active learning sampling techniques to select the most informative data samples and enhance the learning process. Physics-based agents are subject to a long-term cumulative reward policy of data misfit reduction. The agentic system autonomously learns from the unlabeled pool of data and provides domain adaptation for effective self-supervised learning. The system performs full-waveform inversion in an autonomous and adaptive manner, using local or global optimizers to guide the inversion process. The surrogate machine learning inversion matches or outperforms the resolution of conventional optimization techniques and significantly reduces computing resource utilization. On the realistic test, the agentic framework achieves speed-ups of up to 10× for deterministic schemes and 37× for stochastic Markov chain Monte Carlo (MCMC) runs compared with standard inversion workflows. The developed self-supervised agentic approach effectively performs domain adaptation on any dataset, providing a highly efficient, autonomous, and adaptive framework for general physics-based inversion applications.

Keywords:

Agentic

Full-waveform inversion

Self-supervised

Optimization

Physics-driven