# Translations:Summary/2/en

Both deconvolution methods have much in common; the difference is in the fundamental assumptions that determine how we obtain the deconvolution operator. The common goal of both predictive deconvolution and Einstein deconvolution is to obtain the reflection-coefficient series as the deconvolved signal. Both predictive (spiking) deconvolution and Einstein deconvolution conduct the deconvolution process on the upgoing signal. Both predictive deconvolution and Einstein deconvolution share the same deconvolution operator: the inverse of the downgoing signal. The difference between these two methods is in the fundamental assumptions that determine the way the deconvolution operator is obtained. The small white reflectivity hypothesis allows the predictive deconvolution operator to be computed by least squares from the upgoing signal. In addition, the small white reflectivity hypothesis often eliminates the need for a final dynamic deconvolution step. Predictive deconvolution has the advantage of many years of successful use. It is robust and stable in the presence of noise. Einstein deconvolution enjoys the advantage that it is not based on the small white reflectivity hypothesis. In this sense, Einstein deconvolution is more general. However, Einstein deconvolution also is more sensitive to noise. Ideally, then, the two methods should be used together to yield the best results.