M.Sc Student | Ram Idan |
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Subject | Wavelets for Graphs and their Deployment to Image Processing |

Department | Department of Electrical Engineering |

Supervisors | Professor Israel Cohen |

Professor Shalom Raz | |

Full Thesis text |

Multichannel seismic deconvolution is employed when the
structure of the earth is
estimated by transmitting an acoustic wave into the ground and measuring
the reflected energy resulting from impedance discontinuities. The observed
seismic data can be modeled
as a convolution between a two-dimensional (2D) reflectivity section and
a one-dimensional (1D) seismic
pulse (wavelet), which have been further degraded
by additive noise. Deconvolution is
used to minimize the effect of the wavelet and
produce an increased resolution estimate of the
reflectivity, where closely spaced
reflectors can be identified. In this thesis we propose two
multichannel blind deconvolution algorithms for the restoration of 2D seismic
data. Both algorithms are
based on a 2D reflectivity prior model, and use iterative multichannel
deconvolution procedures which deconvolve the seismic data, while taking into
account the spatial
dependency between neighboring traces. The first algorithm employs
in each iteration a modified maximum
posterior mode (MPM) algorithm which
estimates a reflectivity column from the corresponding
observed trace using the estimate
of the preceding reflectivity column. The second algorithm takes into
account estimates of both the
preceding and subsequent columns in the estimation process. Both algorithms are
applied to synthetic and real data and demonstrate better results compared to
those obtained by a single-channel deconvolution
method. Expectedly, the second algorithm which
utilizes more information in the estimation
process of each reflectivity column is shown to produce
better results than the first
algorithm.