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axis, with a spectral estimate based on a window w n + m ( t ) of the data centred at
b n + m on the time axis,
E S gg, n ( t ) S gg, n + m ( f )
I 2 E H n ( f ) H n ( f ) H n + m ( f ) H n + m ( f ) ,
1
=
(2.366)
where H n ( f )and H n + m ( f ) are Fourier transforms of the respective windowed data
segments. Then,
E S gg, n ( f ) S gg, n + m ( f )
I 2
1
e i f ( s t + u v)
=
−∞
−∞
−∞
−∞
· w n ( s )w n ( t )w n + m ( u )w n + m (v)
·
E g( s )g ( t )g( u )g (v) ds dt dud v.
(2.367)
The evaluation of this expected value requires the expected value of a fourth moment
of the process generatingg( t ). The realisations of the process are complex numbers.
If they are taken to have real and imaginary parts that jointly are normally distrib-
uted with zero mean, at a number of points N along the time axis, the probability
density has been given by Wooding (1956) as
exp
jk g k ,
1
g j c 1
p (g 1 ,g 2 ,...,g N )
=
(2.368)
N
π
|
C
|
where summation is implied over the repeated subscripts j , k , while C is the Her-
mitian variance-covariance matrix,
is its determinant and c 1
jk are the elements
of its inverse. Wooding (1956) also gives the characteristic function for this distri-
bution as
|
C
|
exp
4 ω j c jk ω k
1
Φ=
(2.369)
exp i ω j g j
+ ω j g j /2 p (g 1 ,g 2 ,...,g N ) d
=
V ,
(2.370)
with the integral over all volume elements d
of the complex realisation space.
Again, summation is implied over repeated subscripts, and c jk are the elements of
the variance-covariance matrix C . Labelling the realisations at locations s , t , u , von
the time axis g 1 , g 2 , g 3 , g 4 , respectively, power series expansion of the exponential
in the integral (2.370) for the characteristic function, and integration term by term,
yield
V
4 pd
1
4!
1
16
···+ ω 1 g 1
+ ω 2 g 2 + ω 3 g 3
+ ω 4 g 4 +···
Φ=
1
+···+
V+···
1
16
ω 1 g 1 ω 2 g 2 ω 3 g 3 ω 4 g 4 pd
=
1
+···+
V+···
16 ω 1 ω 2 ω 3 ω 4 E g 1 g 2 g 3 g 4
1
=
1
+···+
+··· ,
(2.371)
 
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