A Review of Modeling and Waveform Inversion for Marine Seismic Data

Abstract

Marine seismic exploration is a core technology supporting marine resource exploration, seabed detection, carbon sequestration monitoring, and offshore engineering safety. The integration of full-waveform inversion (FWI), elastic inversion, numerical modeling, and artificial intelligence is driving a paradigm shift from physics-driven to physics-constrained and data-driven hybrid mode. Based on the JMSE special issue Modeling and Waveform Inversion of Marine Seismic Data, this paper systematically reviews 11 papers across six areas: data preprocessing, forward modeling, FWI, elastic inversion, reservoir characterization, and migration imaging. Results show that intelligent interpolation, multi-source joint inversion, low-frequency recovery and cycle-skipping suppression, physics-guided deep learning inversion, and wide-band velocity modeling are key solutions to industrial bottlenecks in OBN/OBC, streamer, and passive-source scenarios. These achievements form a complete system from theory to engineering application, supporting deep-water exploration, seabed hazard detection, and carbon sequestration monitoring. This paper also introduces the new JMSE special issue Marine Geophysical Exploration in the Era of Artificial Intelligence, summarizes recent AI-based advances, and prospects future trends of AI and marine seismic integration.