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Deconvolution negates these effects to an extent and thus increases the resolution of the seismic data. Seismic data, or a seismogram, may be considered as a convolution of the source wavelet, the reflectivity and noise. [5] Its deconvolution is usually implemented as a convolution with an inverse filter.
Blind deconvolution is a well-established image restoration technique in astronomy, where the point nature of the objects photographed exposes the PSF thus making it more feasible. It is also used in fluorescence microscopy for image restoration, and in fluorescence spectral imaging for spectral separation of multiple unknown fluorophores .
There are three main processes in seismic data processing: deconvolution, common-midpoint (CMP) stacking and migration. [44] Deconvolution is a process that tries to extract the reflectivity series of the Earth, under the assumption that a seismic trace is just the reflectivity series of the Earth convolved with distorting filters. [45]
[1] [2] This is done by deconvolution of the incoming vertical and longitudinal components of the seismogram, which removes the common part of the components - namely, the source and travel path information. [3] The resulting waveform is the receiver function. Similarly, a teleseismic S-wave will generate an S-to-P conversion beneath the ...
VSPs are used to measure a seismic signal at depth and with that measurement the wavelength at the source of the seismic activity is easily found. With the measurement of the source wavelet, geophysicists can carry out deconvolution on the VSP and decrease the reports of all seismic activity and limit the reports to just abnormal or extreme ...
The scientists’ prior work on core rotation helped them interpret variations in the height of seismic waves, defining them as indicators of changes to the surface of the inner core, said Dr ...
In image processing, blind deconvolution is a deconvolution technique that permits recovery of the target scene from a single or set of "blurred" images in the presence of a poorly determined or unknown point spread function (PSF). [2] Regular linear and non-linear deconvolution techniques utilize a known PSF.
In mathematics, Wiener deconvolution is an application of the Wiener filter to the noise problems inherent in deconvolution. It works in the frequency domain , attempting to minimize the impact of deconvolved noise at frequencies which have a poor signal-to-noise ratio .