Abstract
While single point source is an oversimplified representation of medium to large earthquakes, finite fault models in many cases over parameterize the inversion due to the lack of sufficient near field data. Multiple point source solutions can fill in the gap in-between these two representations. Here, we propose a Markov-Chain-Monte-Carlo multiple point source inversion scheme, in combination with the advantage of the cut-and-paste technique, which cuts the seismogram into Pnl and surface portions and allows different time-shifts for each segment to align data with the synthetics. We apply the approach to the Mw 6.2 foreshock in the 2016 Kumamoto earthquake sequence by using the strong-motion observations within 100 km. We are able to perform the inversion at relatively high-frequency ranges (0.02–0.28 Hz for Pnl and 0.02–0.20 Hz for surface waves) with confidence on the velocity model built on the Mw 5.4 path calibration event. Our results show that the rupture was mainly composed of three subevents, with total duration of about 12 s and total Mw of 6.2. The strikes of three subevents agree well with surface fault mapping where the Futagawa Fault intersects the Hinagu Fault with ∼30° difference in strike. Our solution shows that the first subevent dips to the southeast, while the second and the third subevents, located ∼3 km the north and ∼4 km to the southwest of the first subevent, dip to the northwest. The focal mechanism of the first subevent shows remarkable agreement with the first-motion solution. The fault geometry also shows well consistence with the relocated aftershocks, which delineate a SE-dipping fault around the first subevent and two NW-dipping faults to the north and south, respectively, corresponding to the second and the third subevent. The sum of moment tensor of subevents that have not only different geometry but also rake angles shows strong Compensated Linear Vector Dipole (CLVD) component (48–50 per cent). With a local 1D crustal model, a full-moment-tensor inversion using regional long-period waveform data also detects strong CLVD component of this earthquake. In contrast, using the PREM model results in an almost pure double-couple solution. In short, we have precisely resolved the rupture process, the intricate fault geometry, and the strong CLVD component with strong-motion data. This highlights the importance of extracting the relatively high-frequency information from the waveform data and with accurate velocity model in seismic source analyses of large earthquakes.