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Related Experiment Video

Updated: Nov 29, 2025

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

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Adaptive Multiple Subtraction Based on an Accelerating Iterative Curvelet Thresholding Method.

Bowu Jiang, Wenkai Lu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 23, 2020

Summary

This summary is machine-generated.

This study introduces a faster adaptive multiple subtraction method for seismic exploration. The new technique improves the accuracy of seismic imaging by efficiently removing unwanted multiples without distorting primary signals.

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Area of Science:

Geophysics
Seismic Exploration
Signal Processing

Background:

Seismic data contain primary reflections (useful for imaging) and multiples (interfering signals).
Surface-related multiple elimination (SRME) is crucial for accurate subsurface imaging.
Adaptive multiple subtraction (AMS) is key to SRME, but traditional methods like iterative curvelet thresholding (ICT) are computationally intensive.

Purpose of the Study:

To develop a computationally efficient and effective adaptive multiple subtraction method for seismic data.
To improve the accuracy and reduce the computational burden of surface-related multiple elimination.
To enhance signal-to-noise ratio (SNR) in seismic data processing.

Main Methods:

Proposed an accelerated iterative curvelet thresholding (ICT) method by directly utilizing the signal misfit in the curvelet domain.
Implemented a single forward/inverse curvelet transform pair per iteration, reducing computational cost compared to traditional ICT.
Introduced joint constraints across different frequency bands to stabilize and enhance multiple attenuation, addressing frequency-dependent errors.

Main Results:

The proposed accelerated ICT method significantly reduces computational cost compared to traditional ICT.
Demonstrated superior performance in both synthetic and field seismic data examples.
Achieved a signal-to-noise ratio (SNR) improvement of 0.5-2.8 dB when refining results from other AMS methods.

Conclusions:

The accelerated ICT method offers a faster and more efficient approach to adaptive multiple subtraction in seismic exploration.
The frequency-band constrained approach enhances the stability and effectiveness of multiple attenuation.
This method is a valuable tool for improving seismic data quality and subsurface imaging accuracy.