Wavelet Transform is a new tool for signal analysis which can perform a simultaneous signal time and frequency representations. Under Multi Resolution Analysis (MRA), one can quickly determine details for signals and their properties using Fast Wavelet Transform (FWT) Algorithms.
In this paper, for a better physical understanding of a signal and its basic algorithms, Multi Resolution Analysis together with wavelet transforms in a form of Digital Signal Processing (DSP) will be discussed. For a Seismic Signal Processing (SSP), sets of Orthonormal Daubechies Wavelets (ODW) are suggested. When dealing with the application of wavelets in SSP, one may discuss about denoising from the signal and Data Compression existed in the signal, which is important in seismic signal data processing. Using these techniques, El-Centro and Nagan signals were remodeled with a 25% of total points, resulted in a satisfactory results with an acceptable error drift. Thus a total of 1559 and 2500 points for El_Centro and Nagan seismic curves each, were reduced to 389 and 625 points respectively, with a very reasonable error drift, details of which are recorded in the paper. Finally, the future progress in signal processing, based on wavelet theory will be appointed
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