IEEE Geoscience and Remote Sensing Magazine - March 2016 - 19

Length (km) Width (km) Top Deptha (km) Strike (deg) Dip (deg)
12.2 (0.4)
aVertical
bEast

14.1 (0.7)

1.9 (0.2)

133 (2)

47 (1)

Eastb (km)

Northb (km)

373.83 (1.38) 4,691.29 (1.58)

Rake (deg)

Slip (cm)

-103 (2)

56 (2)

depth of the fault top edge.

and north coordinates are in UTM-WGS84, zone 33, and refers to the center of the fault trace.
(a)
13°12′

13°24′

13°36′ 13°12′

T1-Slip Distribution

13°24′

13°36′

T2-Slip Distribution

42°36′

42°24′

42°12′
5 km

5 km
Postseismic Slip (cm)

Coseismic Slip (cm)
0

25

50
(b)

75

100

0

5

10
(c)

15

20

FIgURE 9. (a) Geometrical parameters of the Paganica fault estimated in [112]. (b) Coseismic and (c) postseismic slip distributions of the

Paganica fault and the Campotosto fault using the Paganica fault geometry in part (a) and the Campotosto fault geometry derived from
geological mapping and InSAR, GPS, and leveling data. The white star indicates the 6 April 2009 M w 6.3 L'Aquila mainshock, while the green
stars are the three Mw > 5 aftershocks on the Campotosto fault. The Paganica fault is in green and the Campotosto fault is in red. The gray
arrows show the slip direction. (Figure used with permission from [15].)

in the individual displacement measurement, the prefusion
can be a good choice for both linear and nonlinear inversions. An appropriate fusion step before the inversion allows reducing most the systematic uncertainty. In the case
of complementarity, because each individual measurement
brings nonreplaceable information, all the measurements
should be used. In both cases of redundancy and complementarity, data fusion can provide optimal results only if the
specification of the displacement uncertainty is appropriate.
In practice, both redundancy and complementarity
exist in most studies. Moreover, the uncertainty associated with each measurement is not always reliable, and it is
even unavailable in some cases, which makes the judgement of the agreement between different measurements
difficult. Currently, the main topic on displacement measurement fusion consists of using as many measurements
as possible. Therefore, joint inversion is the most used
march 2016

ieee Geoscience and remote sensing magazine

fusion strategy with the expectation that we can obtain
new information using more measurements. With good
data quality (i.e., a random uncertainty of small amplitude), this strategy can give satisfactory results; with
moderate or poor data quality (i.e., a random uncertainty
of large magnitude of systematic uncertainty), this strategy can fail since it is difficult, on one hand, to adjust a
model among many noisy data, and, on the other hand,
to determine the appropriate relative contribution of each
measurement, even though numerous studies have been
focused on the search of the optimal weighting of heterogeneous measurements. In this case, prefusion can
be considered a good choice. In the case of complex geophysical model inversion where the computational time
is the main concern, a step of prefusion between redundant measurements before the inversion is also preferred
so not to burden the inversion system.
19



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