Lithospheric Structure of the Yamato Basin Inferred from Trans-dimensional Inversion of Receiver Functions

The Yamato Basin, located at the southeast of the Japan Sea, has been formed by the back-arc opening of the Japan Sea. Wide-angle reflection surveys have revealed that the basin has anomalously thickened crust compared with a normal oceanic crust [e.g., Nakahigashi et al., 2013] while deeper lithospheric structure has not known so far. Revealing the lithospheric structure of the Yamato Basin will lead to better understanding of the formation process of the Japan Sea and thus the Japanese island. In this study, as a first step toward understanding the lithospheric structure, we aim to detect the lithosphere-asthenosphere boundary (LAB) using receiver functions (RFs). We use teleseismic P waveforms recorded by broad-band ocean-bottom seismometers (BBOBS) deployed at the Yamato Basin. We calculated radial-component RFs using the data with the removal of water reverberations from the vertical-component records [Akuhara et al., 2016]. The resultant RFs are more complicated than those calculated at an on-land station, most likely due to sediment-related reverberations. This complexity does not allow either direct detection of a Ps conversion from the LAB or forward modeling by a simple structure composed of a handful number of layers. To overcome this difficulty, we conducted trans-dimensional Markov Chain Monte Carlo inversion of RFs, where we do not need to assume the number of layers in advance [e.g., Bodin et al., 2012; Sambridge et al., 2014]. Our preliminary results show abrupt velocity reduction at 70 km depth, far greater depth than the expected LAB depth from the age of the lithosphere ( 20 Ma, although still debated). If this low-velocity jump truly reflects the LAB, the anomalously thickened lithosphere will provide a new constraint on the complex formation history of the Japan Sea. Further study, however, is required to deny the possibility that the obtained velocity jump is an artificial brought by the overfitting of noisy data.