@article {adimah_shear-wave_2024,
	title = {Shear-wave velocity structure of the Blanco oceanic transform fault zone},
	journal = {Geophysical Journal International},
	volume = {239},
	number = {2},
	year = {2024},
	month = {11/2024},
	pages = {1287{\textendash}1312},
	abstract = {Oceanic transform faults (OTFs) facilitate hydrothermal circulation which can modify the fault zone materials and affect their rheological evolution. However, the depth extent and variability of fluid infiltration, degree of mineral alteration and their relationship with earthquake behaviour has only been characterized along a few OTFs globally. Here, we use first-overtone Rayleigh waves extracted from seismic ambient noise to estimate the shear-wave velocity structure beneath the Blanco Transform Fault Zone (BTFZ). Compared to the adjoining normal oceanic plates, relatively variable and slow velocities reduced by at least \$\textbackslashsim\$0.2{\textendash}0.4~km s-1 (\$\textbackslashsim\$4{\textendash}8 per cent) are observed from the crust down to \$\textbackslashsim\$22~km depth along some segments of the BTFZ. The crustal slow velocities can be explained by enhanced fluid-filled porosity of \$\textbackslashsim\$0.4{\textendash}10.9 per cent caused by intense fracturing associated with abundant seismicity. Slow uppermost mantle velocities are predominantly consistent with \$\textbackslashsim\$1.2{\textendash}37 per cent serpentinization and \$\textbackslashsim\$9 per cent hydration, indicating variable and deep fluid infiltration that exceeds 15~km depth. For instance, shear-wave velocities (\$\textbackslashsim\$4.3{\textendash}4.4~km s-1) in the uppermost mantle beneath the Blanco Ridge suggest extensive serpentinization (\$\textbackslashsim\$13{\textendash}25 per cent), which might explain the recently documented earthquake swarms linked with aseismic creep. In comparison, within the vicinity of the ridge-transform intersections at depths \$\textbackslashsim\$\$\textbackslashgt \$16~km, low velocities (\$\textbackslashsim\$4.1{\textendash}4.2~km s-1) that are consistent with the presence of up to \$\textbackslashsim\$1.6 per cent partial melt suggest intratransform magmatism which would contradict the long-held simple conservative strike-slip characterization of OTFs.},
	issn = {1365-246X},
	doi = {10.1093/gji/ggae318},
	url = {https://doi.org/10.1093/gji/ggae318},
	author = {Adimah, Nicholas Irabor and Tan, Yen Joe and Russell, Joshua Berryman}
}
@article {cheng_efficient_2023,
	title = {An Efficient Transfer Learning-Based OBS Seismic Phase Picker (OBSPD) Trained on Cascadia Subduction Zone Earthquake Dataset},
	journal = {Seismological Research Letters},
	volume = {95},
	number = {2A},
	year = {2023},
	month = {11/2024},
	pages = {834{\textendash}847},
	abstract = {Earthquake monitoring and many seismological studies depend on seismic phase arrivals. Thus, detecting seismic events and picking the phase arrival times are fundamentally important. In the recent years, seismic phase picking models based on deep learning approaches have been widely developed. These deep learning models can achieve better performances than traditional phase picking methods and improve the quality of phase picking for land-based earthquake monitoring. However, these models might not perform well on data from ocean-bottom seismometers (OBSs), because they are trained exclusively using onshore seismic data and have limited out-of-distribution generalization ability. Nevertheless, there are insufficient labeled OBS phase arrivals dataset to train a deep learning model from scratch. In this study, we developed an automatic phase detection model for OBS data (OBS phase detection [OBSPD]) using the transfer learning approach based on an existing U-GPD model with pretrained weights from a generalized phase detection model feature extraction system. We developed OBSPD with a limited amount of training data (2784 three-component event waveforms) from the Cascadia subduction zone (CSZ) OBS deployments. Our results show that transfer learning can achieve lower model loss with less overfitting compared to when training a model from scratch. Our new OBSPD model outperforms four existing deep learning pickers in terms of phase detection accuracy with smaller arrival time residuals on a test OBS dataset at CSZ, especially for P phases.},
	issn = {0895-0695},
	doi = {10.1785/0220230161},
	url = {https://doi.org/10.1785/0220230161},
	author = {Cheng, Zhangbao and Tan, Yen Joe and Zhang, Fan and Zhou, Pengcheng and Lin, Jian and Tian, Jinyu and Zhang, Xubo and Zha, Caicai}
}
