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Related Experiment Video

Updated: Jan 20, 2026

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

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Published on: August 7, 2017

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Robust Seismic Image Interpolation with Mathematical Morphological Constraint.

Weilin Huang, Jianxin Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 5, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel mathematical morphology-based interpolation technique for seismic imaging. This method effectively addresses challenges with regularly missing traces and erratic noise, improving seismic data recovery.

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    Related Experiment Videos

    Last Updated: Jan 20, 2026

    Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
    07:58

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    Published on: August 7, 2017

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    Reconstruction of Signal using Interpolation
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    Area of Science:

    • Geophysics
    • Reflection Seismology
    • Signal Processing

    Background:

    • Seismic image interpolation is crucial in reflection seismology, often treated as an inversion problem.
    • Existing compressed sensing methods excel with irregular missing traces but struggle with regular gaps and noise.
    • Spatial aliasing and erratic noise significantly degrade interpolation results from traditional sparse and low-rank methods.

    Purpose of the Study:

    • To develop a robust seismic interpolation technique for regularly missing traces.
    • To overcome limitations of current methods in handling spatial aliasing and erratic noise.
    • To introduce a mathematical morphology-based approach for improved seismic data recovery.

    Main Methods:

    • A novel mathematical morphology-based interpolation (MMC) technique is proposed.
    • The method constrains the morphological scale of the model within the inversion process.
    • Shaping regularization is employed to solve the inversion problem, enhancing robustness.

    Main Results:

    • The MMC technique demonstrates satisfactory robustness against spatial aliasing and erratic energies.
    • Numerical examples confirm the successful performance of the proposed interpolation method.
    • The study provides a detailed algorithmic framework and discusses higher-dimensional extensions.

    Conclusions:

    • The proposed mathematical morphology-based interpolation technique offers a significant advancement for seismic data processing.
    • This method provides a robust solution for interpolating regularly missing seismic traces in the presence of noise.
    • The technique shows promise for improving the accuracy and reliability of seismic imaging.