This paper deals with obtaining the reflectivity white series as a stochastic process using the method of<br>Kalman; this means, deconvolution of non stationary processes. The method of Kalman is considered as<br>another technique in the time domain, supplementary to the theory of Wiener. The deduction of the filter<br>coeficients highlights the relationship between the Kalman and Wiener filters. The mathematics are based on<br>the representation of the system by state variables and random process models. All this representations seek<br>the transformation of integral equations of Wiener-Kolmogorov type in linear and non-linear differential<br>equations adapted to numerical calculation. We developed a methodology capable of estimating the initial<br>information in the process, motivated in that the geological noise, the local noise, the simple refletivity function<br>and the source function are not known. The valorization and the importance of the filter of Kalman, as well as<br>the essence of its concepts, are fundamental to the geophysical data.


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