AN INVERSE METHOD FOR DETERMINING 3-DIMENSIONAL FAULT GEOMETRY WITH THREAD CRITERION - APPLICATION TO STRIKE-SLIP AND THRUST FAULTS (WESTERN ALPS AND CALIFORNIA)
M. Thibaut et al., AN INVERSE METHOD FOR DETERMINING 3-DIMENSIONAL FAULT GEOMETRY WITH THREAD CRITERION - APPLICATION TO STRIKE-SLIP AND THRUST FAULTS (WESTERN ALPS AND CALIFORNIA), Journal of structural geology, 18(9), 1996, pp. 1127-1138
In order to draw geological structures such as faults, interpolation i
s generally needed between scattered data. The use of an approximation
criterion integrating the kinematic properties of the faults helps to
document the fault surfaces by adding a compatibility criterion to th
e data set. Assuming that two jointed blocks of rocks slipping on each
other generate a thread surface, an approximation method has been dev
eloped which integrates a thread criterion. This approximation method
is used to solve an inverse problem with least-squares criteria includ
ing proximity to data points, smoothness and thread criteria. The aim
is to find a smooth surface which is as close as possible to a thread
and as close as possible to the observed data set. Applications to two
corrugated fault surfaces with a dense data set, located in the Weste
rn Alps (France) and in the Transverse Ranges (California), confirm th
e validity of the thread assumption. Despite their difference in mean
corrugation wavelength (5 m and 10 km respectively), in the type of fa
ult (strike-slip and thrust fault respectively), and in the nature of
the faulted rocks (limestones and sandstones respectively), very simil
ar results are obtained. In both cases the observed data fit well with
a thread surface and the computed fault displacement fits well with t
he measured displacement on the fault (striae, seismic focal mechanism
, geodetic data, restoration). The conclusion is that treating a fault
as a thread is a valid physical description which gives the slip dire
ction independently of other kinematic indicators. The advantage of us
ing a thread criterion in addition to classical proximity and smoothne
ss criteria is that this physical insight allows information from area
s where data are relatively dense to help constrain areas where data a
re relatively sparse, these last areas being those that are usually no
t well constrained by proximity and smoothness criteria. Copyright (C)
1996 Elsevier Science Ltd