Abstract
This paper outlines the implementation details and enhancements of a previously described new concept in blind seismic deconvolution that is referred to as principle phase decomposition (PPD). A requirement of the PPD algorithm is for the investigator to determine the seismogram's dominant frequency (DF) and corresponding principle phase components (PPCs). Once these parameters are estimated, a hybrid Rao-Blackwellized particle filter and a hidden Markov model (HMM) filter are utilized to separate the potentially time-variant overlapping source wavelets. A variation of the PPD algorithm that is referred to as the PPD wavelet extraction (PPD-WE) technique addresses the requirement of estimating the seismogram's DF and PPCs. This paper describes in detail the PPD-WE algorithm where the overlapping source wavelets are sequentially and chronologically extracted from the seismogram under analysis. A HMM filter is described which facilitates in the simultaneous estimation of the DF and the corresponding phase of the source wavelet to be extracted within the PPD-WE algorithm. In addition, the utilization of the PPD-WE algorithm within standard frequency-domain deconvolution techniques is outlined
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 callMore From: IEEE Transactions on Geoscience and Remote Sensing
Disclaimer: 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.
