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We can apply the approximation in equation (10) to the objective
functions. For example, from equations (3)
and (7) we can derive
| |
(11) |

The optimal estimate of is the minimizer of this function.
Because the objective function is a quadratic function of the unknown
, one can use the standard least-squares techniques to find
the solution of this linear optimization problem:
| |
(12) |

Once is found for each *t*, the pick can be improved
by using equation (9).

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** Up:** LINEAR OPTIMIZATION
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Stanford Exploration Project

12/18/1997