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fW . j / .0/;
W . j / .1/;
W . j / .2/; :::::::
W . j / .
u . j / .
/ g be the sequence of
backward states obtained by running the backward model ( 2.108 ).
The new initial condition, u . jC 1/ .0/
Let
N
/ D
N
)th run of the forward model is
set to be equal to the initial rate, W . j / (0) of the backward run just completed.
To start the overall iterative process at the 0th run of the forward model, the initial
condition u .0/ .0/ D u
for the (j C 1
, an arbitrary choice.
Our goal is to characterize the limiting behavior of the sequence
fu
.0/
u .p/ .0/ ::::::: g of initial state of
the forward run induced by the feed-back process between the forward and the
backward runs described above.
We consider two cases.
CASE A: Observations are Noise - free
Under this assumption, V
u .0/ .0/;
u .1/ .0/;
u .2/ .0/; :::::::
.0/ D
in ( 2.112 ). Consequently, the stochastic part
SPF in ( 2.116 ) and SPB in ( 2.121 ) are identically zero.
We now derive a recurrence relation that relates the evolution of the required
initial conditions u . j / .0/
.
k
/ 0
.Thefinalrateu . j / (N) starting from u . j / (0) is given by
( 2.115 )as
N u . j / .0/ u T
.0/
u . j / .
/ D u T
N
.0/ C .
1 g
/
(2.123)
Similarly, referring to ( 2.120 ) the initial rate W . j / .0/
of the jth backward run is given
by
N W . j / .
.0/
W . j / .0/ D u T
/ u T
.0/ C .
1 ˛/
N
(2.124)
Since W . j / .
/ D u . j / .
N
N
/
, substituting ( 2.123 )into( 2.124 ) and simplifying we get,
u . jC1 / .0/ D W . j / .0/
(2.125)
N u . j / .0/ u T
.0/
D u T
N
.0/ C .
1 ˛/
.
1 g
/
That is,
N u . j / .0/ u T
.0/
u . jC1 / .0/ u T
N
.0/ D .
1 ˛/
.
/
1 g
(2.126)
0<
<1
˛
Thus, if
g
,thensois
and ( 2.126 ) becomes
ju . jC 1/ .0/ u
.0/ jD ˇ ju . j / u T .0/ j
(2.127)
when
ˇ D .1 ˛/ N .1 g/ N and
0<ˇ<1
for any fixed numbers N
.> 0/
of
observations.
Iterating ( 2.127 ), we obtain
ju . p / .0/ u
p
ju .0/ u
.0/ jD ˇ
.0/ j
(2.128)
That is, u . p / .0/
converges to the true but unknown initial state exponentially, That is,
u . p / .0/ D u . T / .0/
lim
p !1
(2.129)
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