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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. Various well-known deconvolution techniques already exist for one dimension, such as predictive deconvolution, Kalman filtering and ...
Deconvolution maps to division in the Fourier co-domain. This allows deconvolution to be easily applied with experimental data that are subject to a Fourier transform. An example is NMR spectroscopy where the data are recorded in the time domain, but analyzed in the frequency domain. Division of the time-domain data by an exponential function ...
For land acquisition, different types of sources may be used depending on the acquisition settings. Explosive sources such as dynamite are the preferred seismic sources in rough terrains, in areas with high topographic variability or in environmentally sensitive areas e.g. marshes, farming fields, mountainous regions etc. [4] Such type of sources needs to be buried (coupled) into the ground in ...
In the case of deconvolution of seismic data, the original unknown signal is made of spikes hence is possible to characterize with sparsity constraints [4] or regularizations such as l 1 norm/l 2 norm norm ratios, [5] suggested by W. C. Gray in 1978. [6]
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 ]
This data alone can be useful in obtaining information about a specific location. [6] But when receiver function data from one seismic station is combined with data from many other stations, it is possible to build a detailed map of the Moho depth and of seismic velocity across a large geographic area.
Raytheon 704 used as an onsite seismic data processing system in Mogadishu, Somalia in 1974. Like most minicomputers of the late 1960s, the 704 was designed to be mounted in a rack mount case, with the CPU being 9U (15.75 in, 40 cm) tall. [2] [11]
Bispectra fall in the category of higher-order spectra, or polyspectra and provide supplementary information to the power spectrum. The third order polyspectrum (bispectrum) is the easiest to compute, and hence the most popular.