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Interference and Superposition of Waves01:07

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Related Experiment Video

Updated: Jul 11, 2026

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

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

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A blended wavefield separation method for seismic exploration based on improved GoogLeNet.

ZhiQiang Gan1, XiangE Sun1

  • 1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, HuBei, China.

Plos One
|June 25, 2024
PubMed
Summary

Simultaneous acquisition in seismic exploration generates blended noise. This study introduces a Convolutional Neural Network (CNN) method, an improved GoogLeNet, to effectively separate useful seismic signals from this noise, enhancing data quality.

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Area of Science:

  • Geophysics
  • Seismic Exploration Technology

Background:

  • Simultaneous acquisition is crucial for ultra-large-scale, high-precision seismic exploration, reducing costs and improving efficiency.
  • Data deblending is a critical step in simultaneous acquisition, directly impacting data quality and exploration success.
  • Traditional filtering methods are insufficient for deblending due to the similar kinematic and dynamical characteristics of blended noise and useful signals.

Purpose of the Study:

  • To introduce simultaneous acquisition methods in seismic exploration.
  • To analyze the principles of data deblending using Convolutional Neural Networks (CNNs).
  • To develop an improved GoogLeNet-based CNN for effective seismic data deblending.

Main Methods:

  • Introduced simultaneous acquisition techniques.
  • Analyzed the application of Convolutional Neural Networks (CNNs) for seismic data deblending.
  • Developed and implemented an improved GoogLeNet architecture for the deblending method.

Main Results:

  • The developed CNN model effectively separated mixed wavefields from blended seismic data.
  • The method demonstrated quick and efficient deblending capabilities.
  • Achieved the desired separation of useful seismic signals from noise.

Conclusions:

  • The improved GoogLeNet-based CNN is a viable and effective solution for seismic data deblending in simultaneous acquisition.
  • This method significantly enhances the quality of acquired seismic data.
  • Successful deblending is key to the success of oil and gas exploration projects utilizing simultaneous acquisition.