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Sparse-spike seismic inversion with semismooth newton algorithm solver.

Ronghuo Dai1

  • 1School of Mathematics and Information, China West Normal University, Nanchong, 637009, China. daironghuo@yeah.net.

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|August 28, 2024
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Summary
This summary is machine-generated.

This study introduces a faster Semismooth Newton algorithm (SNA) for sparse-spike seismic inversion, crucial for accurately estimating subsurface seismic impedance. The new method significantly reduces computation time for 3D seismic data, improving resource exploration.

Keywords:
Seismic impedanceSemismooth Newton algorithmSparse regularizationSparse-spike inversion

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Area of Science:

  • Geophysics
  • Seismic Exploration
  • Inversion Methods

Background:

  • Seismic prospecting is vital for exploring underground resources like oil, gas, and minerals.
  • Seismic impedance estimation is key for geological characterization and resource prediction.
  • Sparse-spike seismic inversion is commonly used for high-resolution reflectivity series.

Purpose of the Study:

  • To address the challenge of fast optimization algorithms for sparse-spike seismic inversion, especially with large 3D seismic data.
  • To introduce and evaluate the Semismooth Newton algorithm (SNA) for seismic impedance estimation.
  • To demonstrate the efficiency of SNA compared to existing methods.

Main Methods:

  • Sparse-spike seismic inversion using sparse regularization.
  • Application of the Semismooth Newton algorithm (SNA), a second-order method.
  • Comparison of SNA with common algorithms using synthetic and real 3D seismic data.

Main Results:

  • The proposed SNA demonstrates a faster convergence rate.
  • SNA requires less computation time for seismic inversion.
  • The algorithm effectively handles large-scale 3D seismic data volumes.

Conclusions:

  • The Semismooth Newton algorithm offers an effective and efficient solution for sparse-spike seismic inversion.
  • This advancement is critical for real-world applications in mineral and energy resource exploration.
  • The SNA provides a valuable new tool for geoscientists dealing with massive seismic datasets.