Understanding the intricate internal structure and dynamic processes of the Earth has consistently been a focal point in modern Earth science research. The inaccessibility of the Earth’s interior presents a significant challenge; scientists are often confined to indirect detection methods to glean insights into what lies beneath our feet. Seismic waves emanating from natural and artificial sources serve as the primary tool for researchers to probe the depths of the Earth. By analyzing these waves, scientists attempt to create high-resolution subsurface structural models that accurately depict the dynamic processes occurring within the Earth’s interior. However, the goal of achieving high precision in this modeling is fraught with challenges, primarily due to the complexities of the seismic data.
To address these challenges, recent advancements in Full Waveform Inversion (FWI) technology offer pioneering approaches in high-resolution seismic imaging. Unlike traditional seismic methods that strictly rely on ray-theoretical approaches, FWI utilizes the entire seismic waveform to provide a more comprehensive representation of the subsurface structures. This method allows scientists to tap into detailed information, including variations in amplitude, phase, and the intricate waveform patterns, effectively dismantling the resolution barriers that have historically plagued seismic studies.
A significant contribution to this evolving field has been made by a research team led by Professor Dinghui Yang of Tsinghua University. In collaboration with institutions such as the China Earthquake Administration and multiple other universities, the team rigorously explored the theoretical foundation of nonlinear FWI methods, shedding light on its evolutionary history while engaging in a critical analysis of existing technical barriers and practical application challenges. Their collective insights provide a roadmap for understanding the trajectory of FWI advancements and highlight areas ripe for future inquiry.
Traditional seismic tomography methods, although beneficial in identifying broad subsurface structures, often falter when it comes to resolving small-scale anomalies due to their reliance on simplified ray-theory constructs. In contrast, FWI’s holistic approach facilitates the efficient extraction of high-definition images of subterranean environments. By employing an array of data types, FWI has successfully enhanced the characterization of complex subsurface structures, revealing finer details and offering broader insights than its predecessors.
The adoption of numerical methods to simulate wavefields stands at the forefront of FWI advancements. With a suite of algorithms—ranging from finite difference methods to spectral element approaches and including discontinuous Galerkin methods—researchers are now equipped to deliver accurate physical models that underpin seismic data analysis. This foundational work not only supports the FWI framework but also establishes robust mechanisms for interpreting complex seismic environments more effectively.
Recent progress also encompasses the development of new objective functions that substantially improve FWI’s reliability. By shifting away from traditional L2 norm-based functions prone to local minima issues, researchers have embraced methods rooted in the Wasserstein metric. Such innovations have proven invaluable in addressing challenges linked to local minima, thus promoting more accurate and dependable imaging outcomes.
In addition to theoretical progression, revolutionary optimization algorithms play an integral role in enhancing the efficiency of FWI techniques. These advancements, including the implementation of L-BFGS and conjugate gradient methods, have significantly expedited convergence times in FWI calculations. The recent amalgamation of machine learning and stochastic optimization techniques forming new optimization pathways further empowers FWI, breaking through computational barriers and paving the way for sophisticated data handling.
Multiscale imaging has emerged as a pivotal methodological advancement within FWI, allowing for the simultaneous analysis of various seismic wave types. By integrating body waves, surface waves, and converted waves, scientists can now obtain more nuanced and comprehensive insights into the subsurface structures across different depth scales. This multiscale approach not only enhances resolution but also contributes to a more complete understanding of the dynamic processes at play beneath the Earth’s surface.
Today, FWI technology has found applications across myriad fields, reflecting its versatility and effectiveness in subsurface imaging. In the domain of oil and gas exploration, for example, FWI has facilitated remarkable advancements in locating and characterizing complex reservoirs, as demonstrated in the Valhall oil field in the North Sea. By refining imaging techniques, FWI effectively enhances exploration efficiency, offering improved insights into hydrocarbon resources and their potential recovery.
FWI’s relevance extends beyond resource exploration; it significantly contributes to our understanding of deep underground geological structures. Regions such as the Tibetan Plateau and Southern California have benefited from high-resolution imaging, revealing key geological features including subducting slabs and interactions between the crust and mantle. These discoveries contribute to enhanced geodynamic models, offering essential constraints for understanding tectonic activities and earth processes over time.
Moreover, FWI has evolved into an instrumental tool in unraveling the mechanisms contributing to earthquake preparation. By meticulously imaging fault zones and regions associated with seismic sources, researchers have garnered critical insights into the processes that lead to earthquakes. Investigations into events such as the 2022 Luding earthquake underscore the importance of understanding melt and fluid migration processes in the mechanisms that govern seismic occurrences.
The technology has also made notable inroads into the realm of engineering geophysics, with applications in the imaging of near-surface structures that are vital for infrastructure integrity. FWI is making strides in bridge pile foundation assessments, railway tunnel inspections, and various other engineering applications. Its capability to provide high-resolution assessments positions FWI as a valuable asset in ensuring the safety and durability of infrastructural developments.
Interestingly, the influence of FWI has transcended traditional geophysical boundaries, finding relevance in the medical field as a promising tool for high-resolution imaging of brain structures. FWI’s application in medical imaging suggests potential avenues for enhancing diagnostic precision and reliability, introducing the promise of non-invasive, detailed brain assessments.
Despite its broadly acknowledged advantages and the strides made in theoretical frameworks, FWI technology continues to confront several formidable challenges. Chief among these is the high computational cost associated with the iterative solving of wave equations that FWI necessitates. The immense resource demands for extensive three-dimensional imaging tasks underscore the continued need for optimization in terms of computational methods and efficiency.
Additionally, issues surrounding the non-uniqueness of solutions within the FWI framework cannot be overlooked. Given that FWI often represents underdetermined nonlinear optimization problems, researchers grapple with the complexities arising from non-unique or ambiguous imaging results. This challenge is exacerbated when traditional objective functions induce local minima, posing barriers to achieving satisfactory imaging fidelity.
The final hurdle lies in the challenges presented by seismic phase extraction and matching. Discrepancies arise from the inherent complexity of the Earth’s internal structures, as well as limitations in modeling the propagation of seismic waves through diverse geologies. Such complexities frequently lead to mismatches between observed seismic phases and theoretical waveforms, complicating the matching of data to derived models.
In light of these challenges, the trajectory of FWI technology calls for focused research and innovation to bolster the reliability and efficacy of imaging results. The continued evolution of methodologies promises to unlock new potential across disciplines such as Earth sciences, engineering, medical imaging, and disaster management. As advancements in FWI progress unabated, its role in seismology and geophysics will undoubtedly solidify, solidifying FWI’s status as a cornerstone tool in the quest for transparent insights into the Earth’s mysteries.
With a commitment to addressing the outstanding challenges and harnessing the technology’s capabilities, FWI stands poised to transform how we investigate and understand the dynamic Earth, driving forward our exploration of both terrestrial and planetary environments alike.
Subject of Research: Full Waveform Inversion Technology in Seismic Imaging
Article Title: Advancements and Applications of Full Waveform Inversion in Seismic Imaging
News Publication Date: October 2023
Web References: (Not applicable)
References: Yang D, Dong X, Huang J, Fang Z, Huang X, Liu S, Liu M, Meng W. 2025. High-resolution full waveform seismic imaging: Progresses, challenges, and prospects. Science China Earth Sciences, 68(2): 315‒342. DOI: 10.1007/s11430-024-1498-0
Image Credits: (Not applicable)
Keywords: Full Waveform Inversion, seismic imaging, Earth sciences, numerical methods, optimization algorithms, multiscale imaging, oil and gas exploration, earthquake mechanics, engineering geophysics.
