Abstract
Based on the processing and analysis of seismic signals originating from underground nuclear explosions and natural earthquakes, it is illustrated that the seismic signals in the time domain possess the characteristics of statistical self-affine fractals, whilst the fractal dimension D yielded from logarithmic power does not serve as an effective feature for seismic pattern recognition. Moreover, it is found that the signal at each scale of the wavelet decomposition relates closely to the scale, and that an apex appeared on the energy spectrum of the detail signal, hence, the two kinds of features advocated are very likely to be utilized in seismic pattern recognition applications. The provided recognition results show the improvement and performance achieved by the proposed feature extraction and selection methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
