Seismic data are often represented by the well known convolutional model:  
\[\ s(t)\ =\ w(t)\ {\otimes}\ r(t)\ {+}\ n(t),\]
(1)
where w(t) denotes the seismic wavelet, r(t) the reflectivity series, and n(t) additive noise. The goal of seismic deconvolution is to design a filter f(t) capable of removing or compressing the wavelet. To understand the effect of removing the wavelet from the seismogram, convolve both sides of equation (1) with the filter  
\[\ f(t)\ {\otimes}\ s(t)\ =\ f(t)\ {\otimes}\ w(t)\ {\otimes}\ r(t)\ {+}\ f(t)\ {\otimes}\ n(t).\]
(2)
...
The last equation says that deconvolution can be successfully carried out if and...

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