Yu-Sheng Sun

Earth Sciences PhD Candidate @ University of Oregon

Now, I am working on the source characteristics by the join inversion, including high-rate GNSS, InSAR and strong motion, and the long duration tsunami modeling for the 2020 Samos earthquake in the Aegean Sea. I also focus on slow slip events (SSEs) in the Cascadia subduction zone with machine learning and SSE simulation.

The 2020 Mw 7.0 Samos (Eastern Aegean Sea) Earthquake: joint source inversion of multi-type data, and tsunami modeling 

Yu-Sheng Sun1, Diego Melgar1, Angel Ruiz-Angulo2, Athanassios Ganas3, Tuncay Taymaz4, Brendan Crowell5, Xiaohua Xu6,7, Varvara Tsironi3, Ilektra Karasante3, Seda Yolsal-Çevikbilen4, Ceyhun Erman4, Tahir Serkan Irmak8, Yeşim Çubuk-Sabuncu9, Tuna Eken4

We present a kinematic slip model and a simulation of the ensuing tsunami for the 2020 Mw 7.0 Néon Karlovásion (Samos, Eastern Aegean Sea) earthquake, generated from a joint inversion of high-rate GNSS, strong ground motion and InSAR data. From the inversion, we find that the source time function has a total duration of ∼20 s with three peaks at ∼4, 7.5 and 15 s corresponding to the development of three asperities. Most of the slip occurs at the west of the hypocenter and ends at the northwest down-dip edge. The peak slip is ∼3.3 m, and the inverted rake angles indicate predominantly normal faulting motion. Compared with previous studies, these slip patterns have essentially similar asperity location, rupture dimension and anti-correlation with aftershocks. Consistent with our study, most published papers show the source duration of ∼20 s with three episodes of increased moment releases. For the ensuing tsunami, the eight available gauge records indicate that the tsunami waves last ∼18-30 hours depending on location, and the response period of tsunami is ∼10-35 min. The initial waves in the observed records and synthetic simulations show good agreement, which indirectly validates the performance of the inverted slip model. However, the synthetic waveforms struggle to generate long-duration tsunami behavior in simulations. Our tests suggest that the resolution of the bathymetry may be a potential factor affecting the simulated tsunami duration and amplitude. It should be noted that the maximum wave height in the records may occur after the decay of synthetic wave amplitudes. This implies that the inability to model long-duration tsunamis could result in underestimation in future tsunami hazard assessments.

The source time functions of the Samos earthquake.

Comparison with previous studies: Taymaz et al. (2022) (Model A, B and C), Kiratzi et al. (2021), Chousianitis and Konca (2021) and Plicka et al. (2022) (model: LinSlipInv). Our inversion result is shown by the six contour lines (slip = 0.5 to 3 m with 0.5 m interval), which are enclosed within the fault boundary, the gray dot rectangle. The black dots are relocated aftershocks (Mw ≥ 3 within 30 days) (Kiratzi et al. 2021). The yellow star is the epicenter used in this study; the white star is the epicenter used in other studies.

Tsunami waveforms of the bathymetry resolution test. Comparison between observation and synthetic waveforms of 3-L and 2-L. The waveforms are band-pass filtered.

1 Department of Earth Sciences, University of Oregon, Eugene, Oregon, U.S.A.2 Institute of Earth Sciences, University of Iceland, Reykjavik, Iceland3 National Observatory of Athens, Institute of Geodynamics, Lofos Nymfon, Thission, 11810 Athens, Greece4 Department of Geophysical Engineering, The Faculty of Mines, Istanbul Technical University, Maslak 34467, Sarıyer, Istanbul, Türkiye5 Department of Earth and Space Sciences, University of Washington, Seattle, Washington, U.S.A.6 University of Science and Technology of China, Hefei, Anhui 230026, CN7 University of Texas at Austin, Austin TX 78758, US8 Department of Geophysical Engineering, Kocaeli University, 41001 Izmit, Kocaeli, Türkiye9 Icelandic Meteorological Office, Iceland

The southernmost portion of the Ryukyu Trench near the island of Taiwan potentially generates tsunamigenic earthquakes with magnitudes from 7.5 to 8.7 through shallow rupture. The fault model for this potential region dips 10∘ northward with a rupture length of 120 km and a width of 70 km. An earthquake magnitude of Mw 8.15 is estimated by the fault geometry with an average slip of 8.25 m as a constraint on the earthquake scenario. Heterogeneous slip distributions over the rupture surface are generated by a stochastic slip model, which represents the decaying slip spectrum according to k−2 in the wave number domain. These synthetic slip distributions are consistent with the abovementioned identical seismic conditions. The results from tsunami simulations illustrate that the propagation of tsunami waves and the peak wave heights largely vary in response to the slip distribution. Changes in the wave phase are possible as the waves propagate, even under the same seismic conditions. The tsunami energy path not only follows the bathymetry but also depends on the slip distribution. The probabilistic distributions of the peak tsunami amplitude calculated by 100 different slip patterns from 30 recording stations reveal that the uncertainty decreases with increasing distance from the tsunami source. The highest wave amplitude for 30 recording points is 7.32 m at Hualien for 100 different slips. Compared with the stochastic-slip distributions, the uniform slip distribution will be highly underestimated, especially in the near field. In general, the uniform slip assumption only represents the average phenomenon and will consequently ignore the possibility of tsunami waves. These results indicate that considering the effects of heterogeneous slip distributions is necessary for assessing tsunami hazards to provide additional information about tsunami uncertainties and facilitate a more comprehensive estimation.


  1. Department of Earth Sciences, National Central University, Taoyuan City 32001, Taiwan, R.O.C.
  2. Earthquake-Disaster & Risk Evaluation and Management Center, National Central University, Taoyuan City 32001, Taiwan, R.O.C.
  3. Graduate Institute of Hydrological and Oceanic Sciences, National Central University, Taoyuan City 32001, Taiwan, R.O.C.
  4. Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung, Taiwan

The map of Taiwan shows the fault model and recording stations used in this study.

The probabilities of the peak tsunami amplitude (PTA) along the coast of Taiwan (blue: stations 1 ∼ 19, red: stations 20 ∼ 30). The histograms display the PTAs derived from 100 different slip simulations. The black lines represent the results from another 100 simulations, and the orange lines represent the PTA obtained using a uniform slip distribution. The PTA probability distribution gives a clear PTA range and its occurring probability. The map of Taiwan shows the station locations and the sites of four nuclear power plants (NPPs, yellow squares).

Real-time probabilistic seismic hazard assessment based on seismicity anomaly

Yu-Sheng Sun1, Hsien-Chi Li1, Ling-Yun Chang1, Zheng-Kai Ye1, and Chien-Chih Chen1,2

Real-time probabilistic seismic hazard assessment (PSHA) was developed in consideration of its practicability for daily life and the rate of seismic activity with time. Real-time PSHA follows the traditional PSHA framework, but the statistic occurrence rate is substituted by time-dependent seismic source probability. Over the last decade, the pattern informatics (PI) method has been developed as a time-dependent probability model of seismic source. We employed this method as a function of time-dependent seismic source probability, and we selected two major earthquakes in Taiwan as examples to explore real-time PSHA. These are the Meinong earthquake (ML 6.6) of 5 February 2016 and the Hualien earthquake (ML 6.2) of 6 February 2018. The seismic intensity maps produced by the real-time PSHA method facilitated the forecast of the maximum expected seismic intensity for the following 90 days. Compared with real ground motion data from the P-alert network, our seismic intensity forecasting maps showed considerable effectiveness (~70% accuracy). This result indicated that real-time PSHA is practicable and provides useful information that could be employed in the prevention of earthquake disasters.


  1.  Department of Earth Sciences, National Central University, Taoyuan City 32001, Taiwan, R.O.C.
  2.  Earthquake-Disaster and Risk Evaluation and Management Center, National Central University, Taoyuan City 32001, Taiwan, R.O.C.

Panels (a) and (b) are the P-alert station distributions indicating “hit” and “not hit”. The red and blue triangles represent “hit” and “not hit”, respectively. Panels (c) and (d) are the distributions of the hit percentage for the 2016 Meinong and 2018 Hualien earthquakes, respectively. The red line area represents the acceptable prediction range.