A Review of Modeling and Waveform Inversion for Marine Seismic Data

TL;DR

This review discusses recent advances in marine seismic data modeling and waveform inversion, emphasizing AI integration, and highlights solutions addressing industrial challenges in offshore exploration.

Contribution

It systematically reviews 11 key papers, summarizing recent AI-driven methods and proposing future trends in marine seismic inversion and modeling.

Findings

AI-based inversion techniques improve accuracy and efficiency.

Key solutions include intelligent interpolation and physics-guided deep learning.

Systematic review covers theory to engineering applications.

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.