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Seismic Facies Analysis Using the Multiattribute SOM-K-Means Clustering.

Zhaolin Zhu1, Xin Chen2, Haoran Ren1,3

  • 1Hainan Institute of Zhejiang University, Yongyou Industrial Park, 11# Building, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, Hainan, China.

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|October 20, 2022
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Summary

This study introduces a novel multiattribute SOM-K-means clustering algorithm for seismic facies analysis (SFA). The method enhances geological exploration by improving seismic facies recognition and clustering accuracy.

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Area of Science:

  • Geophysics
  • Machine Learning
  • Geological Exploration

Background:

  • Seismic facies analysis (SFA) is crucial for understanding subsurface geology and guiding exploration.
  • Existing machine learning methods for SFA face challenges due to complex geological and seismic data.
  • Accurate SFA is vital for effective resource identification and geological interpretation.

Purpose of the Study:

  • To propose and evaluate a new multiattribute SOM-K-means clustering algorithm for seismic facies analysis.
  • To enhance the accuracy and effectiveness of seismic facies classification.
  • To improve the extraction of complementary features from seismic attribute volumes.

Main Methods:

  • A two-stage clustering approach using the Self-Organizing Map (SOM) and K-means algorithms.
  • Integration of multiple geological attributes to capture comprehensive subsurface information.
  • Application of the proposed algorithm to seismic data for facies identification.

Main Results:

  • The multiattribute SOM-K-means algorithm effectively extracts complementary features from seismic attributes.
  • The proposed method significantly improves the accuracy of seismic facies classification.
  • Experimental results demonstrate the algorithm's power as a tool for SFA.

Conclusions:

  • The developed multiattribute SOM-K-means clustering algorithm offers a robust solution for SFA.
  • This approach enhances the recognition of seismic facies by leveraging multiple attributes.
  • The algorithm represents a powerful advancement for geological exploration and subsurface analysis.