Our mission is to learn as much as possible about earthquake, volcanic, and earth surface processes while enjoying the journey along the way. Our ultimate hope is to learn things that can help mitigate these potential hazards and contribute towards sustainable development of society.
Machine learning applications in geophysics
Catalog produced using traditional (left) vs machine-learning-based procedure (right).
We have been applying machine-learning-based methods to identify seismic signals precursory to eruptions [Yuan et al., SRL 2019] as well as produce high-resolution earthquake catalogs which improve our ability to map fault structures and study earthquake interactions [Tan et al., TSR 2021] and foreshock sequences [Zhu et al., EPSL 2022]. These studies are part of a long-lasting effort to improve earthquake and eruption monitoring and forecasting.
How faults respond to stress changes
Number of earthquakes per hour in comparison with ocean tides.
We have been examining how earthquake rate and frequency-magnitude distribution vary with tidal stress. This sheds light on how earthquakes nucleate, as well as the frictional property and stress state of natural faults [Wilcock et al., Science 2016; Tan et al., GRL 2018; Tan et al., EPSL 2019; Scholz et al., Nature Communications 2019]. We have also combined geodetic and seismic data to study reservoir-induced earthquakes in Pakistan [Barkat et al., SRL 2022].
Dynamics of volcanic eruptions
Ocean-bottom seismometer trapped in freshly-erupted lava flow. Watch how a remotely operated vehicle rescued one of them here [Image courtesy of WHOI/NSF].
≥ 80% of Earth’s volcanism occurs on the seafloor, yet most submarine eruptions go undetected. Through analyzing ocean-bottom seismic data, we have used the spatiotemporal evolution of microearthquakes, tremor, and lava-related seismic events to characterize the dynamics of submarine eruptions. This advances our understanding of the processes governing volcanic eruptions, as well as the interaction between caldera ring faults and the underlying magma chambers [Tan et al., Nature 2016; Wilcock et al., Science 2016; Tolstoy et al., Oceanography 2018; Wilcock et al., Oceanography 2018; Waldhauser et al., JGR 2020].
Moment-duration scaling: Comparison between San Andreas slow events (dots) and proposed scaling for regular (green bar) and slow (blue bar) earthquakes.
Low-frequency earthquakes
Through statistical modeling, we have quantified the clustering properties of low-frequency earthquakes and inferred the scaling properties of slow-slip events. We seek to understand why faults can slip over a wide range of velocities and reveal the dynamics governing transient fault slips [Tan and Marsan, Science Advances 2020]. We are also investigating the source processes underlying low-frequency earthquakes at volcanic regions and their utility for tracking magma movement and forecasting eruptions.
Yen Joe Tan (陈衍佐) 