The server is under maintenance between 08:00 to 12:00 (GMT+08:00), and please visit later.
We apologize for any inconvenience caused
Login  | Sign Up  |  Oriprobe Inc. Feed
China/Asia On Demand
Journal Articles
Bookmark and Share
Goaf Identification Method Based on Seismic Data Gradient Structure Tensor (GST) Attributes
Pages: 42-47
Year: Issue:  7
Journal: Coal Geology of China

Keyword:  GST3D seismic prospectingchaotic reflectionbended reflectiongoaf;
Abstract:  The structure tensor matrix organization and its algorithmic principles of eigenvector and eigenvalue are introduced in this pa-per. Using GST matrix eigenvalue to calculate chaos and edge attributes;the technical ideas to promote different types of goaf 3D seis-mic data interpretation accuracy put forward and discussed chaos in seismic data structure tensor, key links in edge detection attribute calculation. The example analysis has demonstrated that chaos and edge attributes can describe different types of goaf seismic reflective configuration characteristic variation pattern precisely. Working face goaf can form obvious chaotic reflective configuration and present continuous, tight, massive or broad banded anomalies on plane. While roadway goaf presents wavelet typed bended reflective configura-tion with beaded, short and bended linear, narrow banded characteristics on plane. Thus the attribute analysis method based on seismic data GST algorithm is an effective tool in goaf precise identification and interpretation.
Related Articles
No related articles found