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PEF spectrum equals the inverse of the data spectrum

PEFs have the important property that they whiten the data they are designed on. Since time-domain convolution is frequency-domain multiplication, this implies that the PEF has a spectrum which is inverse of the input data. This property of PEFs is what makes the interpolation scheme described in this thesis function. There are various ways to prove the whitening property of PEFs; here I follow Jain 1989 and Leon-Garcia 1994.

The minimum mean square error prediction of the $n\mathrm{th}$ value in a stationary zero-mean data series u(n), based on the previous p data values, is  
\hat{u}(n) = \sum^p_{k=1} a(k)u(n-k)\end{displaymath} (1)
The coefficients a(n) make up the bulk of the prediction error filter for u(n), which is defined in the Z-transform domain by  
A_p(z) = 1 - \sum^{p}_{n=1}a(n)z^{-n}\end{displaymath} (2)

The coefficients a(n) generate a prediction of the data. Convolving the entire prediction error filter pefdef on the input yields the prediction error $\epsilon (n)$,which is then just the difference between the estimate $\hat{u}(n)$and the known data u(n).  
u(n) - \hat{u}(n) = \epsilon(n)\end{displaymath} (3)
The filter coefficients a(n) are determined from the input data u(n) by minimizing the mean square of the prediction error $\epsilon (n)$.

A fundamental principle from estimation theory says that the minimum mean square prediction error is orthogonal to the known data and to the prediction. It turns out that this implies the most important property of PEFs, and their utility in finding missing data.

First, to develop the orthogonality condition, we consider the data u(n) and the estimate $\hat{u}(n)$as random variables. The estimate is the expectation of the true data u(n) based on the rest of the data, the random sequence $\bold u = (u(1),u(2),u(3),...)$, not including u(n). Taking $f(\bold u)$ to be any function of $\bold u$,and using $E\left[\right]$ to denote expectation, we can write
E\left [ \hat{u}(n) f(\bold u) \right] & = & E\left [ E(u(n)\ve...
 ...)\vert\bold u) \right ]\\  & = & E\left [ u(n) f(\bold u) \right ]\end{eqnarray} (4)
Since $\hat{u}(m)$ and u(m) are functions of $\bold u$,that implies the orthogonality conditions  
E \left [ (u(n) - \hat{u}(n) ) u(m) \right ] = E\left [ \epsilon(n) u(m) \right ] = 0 , \quad \quad m \neq n\end{displaymath} (7)
E \left [ (u(n) - \hat{u}(n) ) \hat{u}(m) \right ] = E\left [ \epsilon(n) \hat{u}(m) \right ] = 0, \quad \quad m \neq n.\end{displaymath} (8)

With certain provisos, these orthogonality conditions imply that the prediction error is white:  
E\left[\epsilon(n)\epsilon(m)\right] = \sigma_\epsilon^2\delta(n-m)\end{displaymath} (9)
where $\sigma_\epsilon^2$ is the variance of $\epsilon (n)$.

For proof, replace n with m-k, and
E \left [ \epsilon(m) \epsilon(m-k) \right ] & = & E \left ( \l...
 ...(m-k) \right ] - E \left [ \epsilon(m) \sum_i a(i)u(n-k-i) \right]\end{eqnarray} (10)

The first term on the right side of equation thingy is a delta function with amplitude equal to the variance of the prediction error, because
E\left[\epsilon(m)u(m)\right] & = & E\left[\epsilon(m)\epsilon(...
 ...(m)\sum_i a(i)u(m-i)\right] \\  & = & \sigma_\epsilon^2 , i \neq 0\end{eqnarray} (12)
The second term in the RHS of equation thingy is basically the same, but the delta function appears inside the sum. Rewriting the RHS, equation thingy turns into  
E \left [ \epsilon(m) \epsilon(m-k) \right ] = \sigma_\epsilon^2\delta(k) - \sigma_\epsilon^2\sum_i a(i)\delta(k+i)\end{displaymath} (15)
If the filter Ap is causal, then i = 1, 2, 3,...,p. The sum in the RHS of equation thingy2 is zero, because k is an autocorrelation lag, meaning $k \geq 0$.This means the prediction error is white:
E \left [ \epsilon(m)\epsilon(m-k) \right ] = \sigma_\epsilon^2\delta(k).\end{displaymath} (16)
If the filter Ap is not causal but has i =-p,...,0,...,p, then the sum does not vanish, and the prediction error is not white. In this case, using a(0) = -1 to fit with equation pefdef,
E \left [ \epsilon(m)\epsilon(m-k) \right ] = - \sigma_\epsilon^2\sum_{i=-p}^{p}a(i)\delta(k+i)\end{displaymath} (17)

The prediction error is the output of the convolution of PEF and data, so if the prediction error is white, then the PEF spectrum tends to the inverse of the data spectrum. This is the most important thing about PEFs; they give an estimate of the inverse data spectrum. This is only true for a causal prediction.

The development above uses one-dimensional data series. In this thesis I deal with predicting missing trace data from other known traces, so two and more dimensions are necessary. Thinking in helical coordinates Claerbout (1998) allows extension to arbitrarily many dimensions. Jain 1989 develops the same arguments with more dimensions. Also, Claerbout 1997 gives alternative whiteness proofs in one dimension and two dimensions, attributed to John Burg.

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