الفهرس 
Thesis ObjectivesOnly 14 pages are availabe for public view
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Abstract
ABSTRACT
Seismic signals are defined as transient signals that spread from a certain source
through the layers of the earth. This source may be either natural or man-made. They are
characterized by low frequency nature. So, it is difficult to interpret them in the presence of
noise. Thesis objective to introduce a powerful noise reduction techniques to transmit these
signals through the channel, they require large channel bandwidth. Therefore, coding and
compression are considered as the solution for this problem. Coding process has an important
role in the signal processing area. It aims to convert the analog signal into a compressed
binary form. The goal of the conversion process is to reduce the number of bits needed for
transmission. Thus; the cost is decreased. In addition, efficient coding and compression of
seismic signals are considered. In this thesis, noise can be defined as undesirable and
unpredictable signals that interfere with the seismic signals. So, different noise reduction
techniques such as spectral subtraction, Wiener filtering, adaptive Wiener filtering and
wavelet denoising are used to reduce the seismic noise. The different noise reduction
techniques are compared and the quality of the recovered signal is evaluated using Dynamic
Time Warping (DTW), Signal-to-Noise Ratio (SNR) and correlation coefficient. The results
prove that both hard and soft thresholding are better than other techniques of noise reduction.
In addition, in this thesis, coding and two compression techniques are investigated. The
Linear Predictive Coding (LPC) is the applied coding technique due to its simplicity and
popularity. The first compression technique for seismic signals depends on the decimation
process for compression, and thus the original seismic signal is reconstructed using inverse
techniques. Inverse techniques include maximum entropy and regularized solutions. On the
other hand, the second compression technique is Compressive Sensing (CS). The coding and
compression techniques are compared and the quality of the recovered signal is evaluated
using DTW. The results prove that the CS technique works efficiently in the absence of noise,
but in the presence of noise, maximum entropy and regularized solutions are better compared
with other techniques. Finally, after applying noise reduction techniques before coding or
compression techniques. The results proved that hard thresholding is the best technique for
LPC. On the other hand, the adaptive Wiener filtering is the best technique for different
compression techniques.