The transformation may comprise a combination with the original seismic image or else a convolution with a wavelet or another convolution kernel.

The method comprises calculating (101) a non-cyclic convolution of the information signal with a watermark signal (v(n)) and combining (102) the convolution with the information signal.

There are only convolution layers with 1×1 convolution kernels and a full connection table.

The first and second differential correlations are convolved with a previous set of differential correlations.

The estimation process may be implemented by convolution-like operations with impulse responses.

Below is an example of a convolution with a 6 x 6 size matrix:

These two inputs are convolved with the response spectra reflecting the local seismic settings.

These primitives are then blurred by convolution with the point spread function of the aperture of the imager.

A positive order corresponds to convolution with that derivative of a Gaussian.

An interpolator with similar properties can be obtained by applying convolution with the following kernel in both dimensions:

An interpolator with similar properties can be obtained by applying a convolution with the following kernel in both dimensions:

Using FFT filtering or a convolution with a large kernel, pits can be detected.

A signal may be lost due to having been convoluted with other signals.

An image descreening process first smoothes the image, where smoothing is accomplished by applying a convolution with a low pass filter (LPF) kernel, which is a parameter to the descreening function.

The matrix (in the present case of size 7 × 7) undergoes a convolution with the image to calculate approximations from horizontal and vertical derivatives.