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The estimation of petrophysical parameters is key in the identification of underground reservoirs. Current petrophysical parameter estimation methods are typically constrained by the choice of particular rock-physics models, necessitating the use of distinct models for various regions. Furthermore, the inherent pronounced nonlinearity of these models presents significant challenges to the solution process in reservoir parameter inversion. Although linearized petrophysical inversion methods can simplify the solving process, they can introduce errors due to linearization. To address these limitations, we propose a petrophysical parameter estimation method driven by rock-physics model and collaborative sparse representation(RCSR). In this approach, the rock-physics model governs the fundamental pattern of the petrophysical parameters, with the collaborative sparse representation introducing perturbations to minimize model errors. Our method maximizes the utilization of well-log data and the rock-physics model, thereby reducing errors associated with linearized rock-physics models and enhancing the methods adaptability. We employ the joint dictionary method to learn features and correlations among multiple parameters from existing well-log data. This learned dictionary is then used as a collaborative sparse representation regularization constraint, and applied to refine the linearized rock physics inversion objective function. Finally, the objective function is minimized using the block coordinate descent method to predict petrophysical parameters. The effectiveness of this method in enhancing the adaptability and accuracy of petrophysical parameter estimation is confirmed through synthetic and field data tests.
The estimation of petrophysical parameters is key in the identification of underground reservoirs. Current petrophysical parameter estimation methods are typically constrained by the choice of particular rock-physics models, necessitating the use of distinct models for various regions. Furthermore, the inherent pronounced nonlinearity of these models presents significant challenges to the solution process in reservoir parameter inversion. Although linearized petrophysical inversion methods can simplify the solving process, they can introduce errors due to linearization. To address these limitations, we propose a petrophysical parameter estimation method driven by rock-physics model and collaborative sparse representation(RCSR). In this approach, the rock-physics model governs the fundamental pattern of the petrophysical parameters, with the collaborative sparse representation introducing perturbations to minimize model errors. Our method maximizes the utilization of well-log data and the rock-physics model, thereby reducing errors associated with linearized rock-physics models and enhancing the methods adaptability. We employ the joint dictionary method to learn features and correlations among multiple parameters from existing well-log data. This learned dictionary is then used as a collaborative sparse representation regularization constraint, and applied to refine the linearized rock physics inversion objective function. Finally, the objective function is minimized using the block coordinate descent method to predict petrophysical parameters. The effectiveness of this method in enhancing the adaptability and accuracy of petrophysical parameter estimation is confirmed through synthetic and field data tests.
Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications.
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