Recognition of small faults in coal fields based on multi-scale seismic curvature attributes fusion

Abstract

In order to meet the needs of intelligent development of coal mines in China for transparency of geological conditions, identifying small faults with a drop of about 3 m has become one of the important geological tasks in structural interpretation. However, the accuracy of conventional 3D seismic exploration data interpretation methods for detecting small faults is still low. On the basis of introducing the basic principles of S-transform time-frequency analysis, principal component analysis, and RGB fusion, this paper proposes a method for identifying small faults in coal fields using multi-scale seismic curvature attribute fusion. The method uses S-transform to perform time frequency analysis to obtain seismic data volumes with multiple frequencies and seismic data volumes at different frequencies correspond to different scales of underground geological information portrayal. Perform spectral analysis on seismic data, determine parameters such as the dominant frequency and frequency bandwidth of seismic signals, and extract the maximum positive curvature attributes of seismic data volumes at different frequencies. Then the principal component analysis (PCA) method is used to analyze the seismic attributes of different frequency seismic data, the GRB fusion method is used to fuse the first three principal components. The application results of actual seismic data show that the results of multi-scale seismic curvature attribute fusion have obvious advantages in identifying small faults, and can improve the accuracy and interpretation accuracy of small faults in seismic data.

Keywords

• Multi-scale seismic attribute
• Maximum positive curvature attributes
• Fusion
• Small fault
• RGB