Source rupture processes of the foreshock and mainshock in the 2016 Kumamoto earthquake sequence estimated from the kinematic waveform inversion of strong motion data

By K. Asano, T. Iwata. Earth, Planets and Space, 2016

177 citations recorded by the source corpus.

The 2016 Kumamoto earthquake sequence started with an MJMA 6.5 foreshock occurring along the northern part of the Hinagu fault, central Kyushu, Japan, and the MJMA 7.3 mainshock occurred just 28 h after the foreshock. We analyzed the source rupture processes of the foreshock and mainshock by using the kinematic waveform inversion technique on strong motion data. The foreshock was characterized by right-lateral strike-slip occurring on a nearly vertical fault plane along the northern part of the Hinagu fault, and it had two large-slip areas: one near the hypocenter and another at a shallow depth. The rupture of the mainshock started from the deep portion of a northwest-dipping fault plane along the northern part of the Hinagu fault, then continued to transfer to the Futagawa fault. Most of the significant slip occurred on the Futagawa fault, and the shallow portion of the Hinagu fault also had a relatively large slip. The slip amount on the shallowest subfaults along the Futagawa fault was approximately 1–4 m, which is consistent with the emergence of surface breaks associated with this earthquake. Right-lateral strike-slip dominated on the fault segment along the Hinagu fault, but normal-slip components were estimated to make a significant contribution on the fault segment along the Futagawa fault. The large fault-parallel displacements recorded at two near-fault strong motion stations coincided with the spatiotemporal pattern of the fault slip history during the mainshock. The spatial relationship between the rupture areas of the foreshock and mainshock implies a complex fault structure in this region.

Subfields: Geophysics

View Paper: https://www.semanticscholar.org/paper/aa2c9083fc92f92e19e0e7d49517698375bc8043

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In Geophysics

Citations in the corpus

Generalized Seismic Phase Detection with Deep Learning (2018, methodology): “We demonstrate the viability of GPD for detecting large earthquakes reliably by applying it to the 2016 Mw 7.0 Kumamoto earthquake [3].”