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ADVANCEMENTS IN FULL WAVEFORM TOMOGRAPHY FOR NEAR SURFACE GEOTECHNICAL APPLICATIONS: INVESTIGATING THE EFFECTS OF PARAMETERIZATION AND WORKFLOW ON ANOMALY DETECTION
Alidoust Golroudbari, Pourya
Alidoust Golroudbari, Pourya
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2023-12
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Civil Engineering
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http://dx.doi.org/10.34944/dspace/10244
Abstract
Full Waveform Inversion (FWI) is a powerful seismic imaging technique used to reconstruct high-resolution velocity models of the subsurface. It relies on the inversion of seismic data acquired from multiple sources and receivers to estimate the mechanical properties of geologic materials and can be used to detect anomalous subsurface conditions. The accuracy of FWI results is influenced by various factors related to the workflow used for its implementation. This includes the survey parameters, the mathematical framework of the inversion, and the complexity of the subsurface conditions modeled during the inversion process. Therefore, it is crucial to have a fundamental understanding of the interplay between these factors and their impact on the accuracy of the reconstructed model, particularly given the effects of these factors on computational costs. This is an area that has been understudied within the context of near-surface geotechnical applications for anomaly detection, which is an application that presents unique challenges relative to seismic exploration for hydrocarbons where FWI has been more fully developed. One key aspect that has not received sufficient attention is the impact of survey parameters on the accuracy of FWI results. The lack of formal research in this topic may lead to near-surface FWI studies that use more seismic sources than required for subsurface feature reconstruction, which results in data collection and computational inefficiencies. The selection of misfit function and starting model are also essential factors influencing the reliability of the reconstructed model. The physics employed for forward modeling can also affect the ability to simulate wave propagation in the domain of interest. These factors have significant implications for near-surface applications of FWI, and further research is required to explore their interplay and improve FWI workflow.Given the gaps in the current implementation of FWI for geotechnical applications, this research will explore the role of parameterization and workflow on FWI results when applied to anomaly detection in karst conditions. This will include selection of an FWI workflow that can improve the feasibility of fieldwork and reduce the processing time. The research will investigate four key factors of the FWI workflow (i.e., survey design, initial model, misfit function, and forward modeling physics) for detection of sinkholes using numerical and field testing in different subsurface conditions. Overall, the outcomes of this research will help practitioners with more appropriate choices in the FWI process and consequently promote its high potential in near-surface applications.
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