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• W4288784920 abstract "As oil and gas exploration moves towards complicated geological environments, high-resolution and true-amplitude seismic imaging becomes increasingly important for detecting and evaluating hydrocarbon reservoirs. Traditional ray-based and wave-equation imaging methods can be considered as the adjoint operator of seismic forward modeling, which are difficult to produce high-quality images in complicated structures because of limited frequency band, unbalanced illumination and irregular acquisition. Least-squares migration (LSM) generates an inverse solution for subsurface reflectivity model with high image resolution and balanced amplitudes. Previous studies on LSM mainly focused on the developments of theoretical and practical strategies, but few on error and uncertainty analysis. We present a quantitative analysis method to evaluate the errors of LSM results. The &#x03D5;_<i>data</i> and &#x03C8;_<i>data</i> functions are first computed based on the local similarity and <i>L</i>₂-norm misfit between observed and synthetic data. They are used as data-domain kinematic and dynamic errors, respectively. Then, these local functions are mapped to the subsurface using a Kirchhoff-integral relation to calculate image-domain errors. Numerical examples for synthetic and field data demonstrate that as the iteration of LSM increases, the total kinematic and dynamic errors are reduced, and they vary in different locations. For low signal-to-noise-ratio field data, LSM might enlarge image errors at large iterations because of the overfitting issue, and proper regularization is very important to facilitate the convergence of LSM to a good solution." @default.
• W4288784920 created "2022-07-30" @default.
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• W4288784920 date "2022-01-01" @default.
• W4288784920 modified "2023-10-17" @default.
• W4288784920 title "Quantitative Error Analysis for the Least-Squares Imaging" @default.
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• W4288784920 doi "https://doi.org/10.1109/tgrs.2022.3194895" @default.
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