Nonlinearities are often encountered in the analysis and processing of real-world signals. Most existing approaches to nonlinear signal processing characterize the nonlinearity in the time domain or frequency domain. In fact, there are good reasons for characterizing nonlinearity using more general signal representations like the wavelet expansion. Wavelet expansions often provide very concise signal representation and thereby can simplify subsequent nonlinear analysis and processing. Wavelets also enable local nonlinear analysis and processing in both time and frequency, which can be advantageous in non-stationary signals. The Wavelet Transform technique is particularly suitable for non-stationary signals like Earth Quake. In contrast to the Fourier Transform, the wavelet transform allows exceptional localization, both in the time domain via translation t of the wavelet, and in the frequency domain via dilations scales b, which can be changed from minimum to maximum, chosen by the user. The Wavelet Transform technique is able to detect the complex variability of Earth Quake signals in time–frequency space.