Semblance
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| Series | Geophysical References Series |
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| Title | Problems in Exploration Seismology and their Solutions |
| Author | Lloyd P. Geldart and Robert E. Sheriff |
| Chapter | 9 |
| Pages | 295 - 366 |
| DOI | http://dx.doi.org/10.1190/1.9781560801733 |
| ISBN | ISBN 9781560801153 |
| Store | SEG Online Store |
Problem
Semblance $ S_{T} $ is the ratio of the energy of a stack of $ N $ traces to $ N $ times the sum of the energies of the $ N $ component traces, all summed over some time interval. Show that $ S_{T}=1 $ when the traces are identical.
Background
While crosscorrelation is a quantitative measure of the similarity of two traces, semblance $ S_{T} $ is a measure of the similarity of a number of traces. Assuming $ N $ traces, we sum (stack) the traces at time $ t $, square the sum to get the total energy, and sum the values over a time interval $ {m}\Delta $. The equation for $ S_{T} $ is
$ {\begin{aligned}S_{T}=\sum \limits _{t=t_{0}}^{t_{0}+m\Delta }\left(\sum \limits _{i=1}^{N}g_{ti}\right)^{2}/N\left[\sum \limits _{t=t_{0}}^{t_{0}+m\Delta }\sum \limits _{i=1}^{N}(g_{ti})^{2}\right].\end{aligned}} $ ()
Solution
The sum of $ N $ traces is $ (\sum {_{i=1}^{N}g_{ti}}) $ and its energy is $ (\sum {_{i=1}^{N}}g_{ti})^{2} $. If the traces are identical, the numerator of equation (9.10a) becomes $ N^{2}[\sum {^{t_{0}+m\Delta }_{t=t_{0}}}g_{t}^{2}] $. The denominator becomes $ N\sum {^{t_{0}+m\Delta }_{t=t_{0}}}(\sum {^{N}_{i=1}}g_{t}^{2})=N^{2}\sum {}_{t=t_{0}}^{t_{0}+m\Delta }g_{t}^{2} $. Since numerator and denominator are equal, $ S_{T}=1 $.
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Also in this chapter
- Fourier series
- Space-domain convolution and vibroseis acquisition
- Fourier transforms of the unit impulse and boxcar
- Extension of the sampling theorem
- Alias filters
- The convolutional model
- Water reverberation filter
- Calculating crosscorrelation and autocorrelation
- Digital calculations
- Semblance
- Convolution and correlation calculations
- Properties of minimum-phase wavelets
- Phase of composite wavelets
- Tuning and waveshape
- Making a wavelet minimum-phase
- Zero-phase filtering of a minimum-phase wavelet
- Deconvolution methods
- Calculation of inverse filters
- Inverse filter to remove ghosting and recursive filtering
- Ghosting as a notch filter
- Autocorrelation
- Wiener (least-squares) inverse filters
- Interpreting stacking velocity
- Effect of local high-velocity body
- Apparent-velocity filtering
- Complex-trace analysis
- Kirchhoff migration
- Using an upward-traveling coordinate system
- Finite-difference migration
- Effect of migration on fault interpretation
- Derivative and integral operators
- Effects of normal-moveout (NMO) removal
- Weighted least-squares