الفهرس | Only 14 pages are availabe for public view |
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. |