Multiple fault modelling combining seismic and geodetic data: the importance of simultaneous subevent inversions

SUMMARY We present a new inversion method for modelling multiple fault sources combining seismic and geodetic data. The technique takes into account 3-D earth structure in the modelling and uses a Monte Carlo inversion scheme that extensively explores the parameter space, which enables the assessment of source parameter uncertainties. Eleven parameters are determined for each subfault: centroid latitude, longitude, depth, time-shift, strike, dip, rake, non-double couple component, average slip, fault length and width. Since multiple fault inversions in the literature are often carried out in an iterative way, we perform synthetic tests comparing simultaneous and iterative synthetic multiple fault inversions for realistic two-fault models including: (i) downdip listricity and (ii) along-strike branching. While the iterative approach leads to substantial errors in the source parameters obtained for the fault model with downdip listricity (e.g. errors of ≈22○ for fault strike, ≈2○ for dip, ≈41○ for rake and ≈0.1 in Mw for joint data inversions), the simultaneous inversions recover the input parameters well. This shows that erroneous source models can be obtained if the inversions do not determine all fault parameters simultaneously. Synthetic inversion tests including different levels of real data noise highlight the strong complementarity of five different data types: regional seismic data, teleseismic P and Swaves, teleseismic surface waves and interferometric synthetic aperture radar (InSAR). The joint data inversions substantially reduce well-known trade-offs between the fault dimensions, fault depth and slip compared to inversions of InSAR or seismic data alone. The synthetic inversions show that local/regional seismic data are key to constrain the lowest magnitude subevents, notably for the two-fault configuration with downdip listricity, which cannot be well constrained by the InSAR or teleseismic data alone. This suggests that downdip segmentation can be difficult to detect in the absence of local/regional seismic data.