S53C-06
Can earthquake source inversion benefit from rotational ground motion observations?

Friday, 18 December 2015: 14:55
305 (Moscone South)
Heiner Igel1, Stefanie Donner2, Michael Reinwald3, Moritz Bernauer3, Joachim M Wassermann3 and Andreas Fichtner4, (1)Ludwig Maximilians University of Munich, Munich, Germany, (2)University of Potsdam, Potsdam, Germany, (3)Ludwig Maximilian University of Munich, Munich, Germany, (4)ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
Abstract:
With the prospects of instruments to observe rotational ground motions in a wide frequency and amplitude range in the near future we engage in the question how this type of ground motion observation can be used to solve seismic inverse problems. Here, we focus on the question, whether point or finite source inversions can benefit from additional observations of rotational motions. In an attempt to be fair we compare observations from a surface seismic network with N 3-component translational sensors (classic seismometers) with those obtained with N/2 6-component sensors (with additional colocated 3-component rotational motions). Thus we keep the overall number of traces constant. Synthetic seismograms are calculated for known point- or finite-source properties. The corresponding inverse problem is posed in a probabilistic way using the Shannon information content as a measure how the observations constrain the seismic source properties. The results show that with the 6-C subnetworks the source properties are not only equally well recovered (even that would be benefitial because of the substantially reduced logistics installing N/2 sensors) but statistically significant some source properties are almost always better resolved. We assume that this can be attributed to the fact the (in particular vertical) gradient information is contained in the additional rotational motion components. We compare these effects for strike-slip and normal-faulting type sources. Thus the answer to the question raised is a definite “yes”. The challenge now is to demonstrate these effects on real data.