Our mission is to learn as much as possible about the processes governing earthquakes and volcanic eruptions… and to enjoy the journey along the way! Our ultimate hope is to learn something that can help mitigate earthquake and volcano-related hazards.

Machine learning applications in geophysics

Screenshot 2021-08-19 at 11.00.02 AM

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 catalog which improves our ability to map fault structures and study earthquake interactions [Tan et al., TSR 2021]. These studies are part of a long-lasting effort to improve earthquake and eruption 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].

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.

Slow 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].