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fabricposneg
Figure 1 (a) Input data. (b)
Estimated dips with LBFGSB without bound constraints. (c)
Estimated positive dips only. (d) Estimated negative dips
only.
sfbayposneg
Figure 2 (a) Input data. This topographic map
comes from the bay area. (b)
Estimated dips with LBFGSB without bound constraints. (c)
Estimated positive dips only. (d) Estimated negative dips only.
aliasposneg
Figure 3 (a) Input data. (b)
Estimated dips with LBFGSB without bound constraints. (c)
Estimated positive dips only. (d) Estimated negative dips only.
Positive and negative dips are found beyond aliasing.
dipposneg
Figure 4 (a) Input data. The lines cross
at a location where the dips should be positive. (b)
Estimated dips with LBFGSB without bound constraints. Negative
dips are actually found at the crossing of the lines. (c)
Estimated positive dips only. The bound constrained optimization is
capable of identifying the true positive dips. (d) Estimated negative dips only.
gmdip
Figure 5 (a) Input data. (b)
Estimated dips with LBFGSB without bound constraints. At the
cross, negative dips are given although only positive dips should
be found. (c) Estimated positive dips only. Positive dips
are recovered everywhere. (d) Estimated negative dips only.
idaposneg
Figure 6 (a) Input data. The cross shows a
location where two events overlap. (b)
Estimated dips with LBFGSB without bound constraints. (c)
Estimated positive dips only. At the cross, no positive dips are
estimated because the overlapping event that goes in the other
direction is too strong. (d) Estimated negative dips only.
gmdecomp
Figure 7 The fist panel on the top
left corner shows the dips without the bounds. Then, from left to
right, top to bottom, the range of dip increases by 0.25 between
each panel, starting at and finishing at
. The lower and upper clip values are
equal to the bounds for each panel.
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Up: Guitton: Bound constrained optimization
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Stanford Exploration Project
10/23/2004