Research Statement

I have been fortunate to be part of the seismology group at ANU since my Ph.D., which has allowed me to immerse in a wide breadth of research topics covering the study of the Earth’s structures, from the crust to the cores, seismic source study, and inversion theory. Consequently, my past research has expanded into (i) the P-wave coda autocorrelation method for shallow imaging, (ii) theoretical and methodological developments of the earthquake coda correlation wavefield, (iii) the study of the Earth’s deep structure, particularly the inner core near the Earth’s center, and (iv) seismic source full-waveform inversion. In my current and future studies, I am determined to expand my technical expertise into machine learning to explore its full potential in structural seismology. In particular, I want to use it as a tool to aid exploratory seismic data analysis with applications in (i) the study of the deep Earth interiors, focusing on the Earth’s core, and (ii) environmental seismology, focusing on glacial seismology in polar ice sheets and the ocean.

1. Autocorrelation Method for Shallow Earth Imaging

One of the classical problems in passive seismology is using seismic waveforms to characterize shallow structures approximated as stratified layers beneath a recording station. We developed the P-wave coda autocorrelation method (Phạm & Tkalčić 2017, 2018, 2021a), which utilizes steep arrival from a teleseismic earthquake and subsequent reverberation. The JGR Editor highlighted the related method paper as a promising alternative to receiver function and ambient noise autocorrelation. This method has been applied to imaging various environments, such as polar ice caps, sedimentary basins, and crustal structures on Earth and Mars. I was invited to speak on this method at the Seismological Society of America’s Seismic Tomography Webinars: Cutting-edge Methods for Seismic Imaging II, 2020.

2. Coda-correlation Wavefield and Deep Earth Seismology

The lack of spatial coverage and sampling sensitivity remains challenging for advancing the study of the Earth’s deep interior. To circumvent the challenge, I am one of the key contributors to several theoretical and methodological breakthroughs that help decipher the architecture of the late earthquake coda and give rise to the coda correlation wavefield (Phạm et al. 2018; Tkalčić et al. 2020). This created the topics for two additional Ph.D. students and has proved a powerful research tool underpinning several discoveries of the Earth’s and other planets’ deep interior structures.

In 2018, we reported a robust detection of shear waves (i.e., J-waves) transversing the Earth’s inner core (Tkalčić & Phạm 2018) utilizing the emerging concepts of the correlation wavefield. J-waves provide unique insights into the solidity of the Earth’s inner core but have been elusive since the IC was discovered more than 85 years ago. The estimate of shear wave speed in the inner core has also been refined (Costa de Lima et al. 2023) thanks to recent advances to understand further the expression of the correlation wavefield (Tkalčić & Phạm 2020; Wang & Tkalčić 2022). Inspired by the correlation wavefield, we also reported unprecedented observation of compressional waves ricocheting along the Earth’s diameters multiple times (Phạm & Tkalčić 2023). The observation strengthens the hypothesis for the existence of the Earth’s innermost inner core (Ishii & Dziewoński 2002). This work created a lot of public interest with the Altmetric attention score of 1880+ and was featured on the front page of the New York Times.

Furthermore, the method has been extended to a single receiver and multiple sources and applied for scanning the Martian core (Wang & Tkalčić 2022). It is envisioned to play an instrumental role in future exploration of the planets and moons of the Solar System. Thanks to these contributions, I have been invited to speak at top conferences in the Study of Earth’s Deep Interior community, including the 2023 IUGG General Assembly in Berlin and the 2024 SEDI meeting in Great Barrington, MA.

3. Seismic Moment Tensor Inversion for Forensic Seismology

Understanding the physics of a seismic source is critical to deciphering its nature, which can be of tectonic or volcanic origins or manmade. The main practical challenge is the incomplete knowledge of Earth’s structures to explain seismological records adequately. My recent work features a class of inversion methods that incorporate the Earth’s structural model uncertainty in the mathematical solution of source mechanisms, which is at the forefront of research efforts to characterize the earthquake source better. The technique has been deployed to study the volcanic earthquakes in Long Valley Caldera (Phạm & Tkalčić 2021b), nuclear tests in the Democratic People of Republic Korea (Hu, Pham, et al. 2023), and the volcanic explosion at Hunga Tonga–Hunga Haʻapai (Hu, Phạm, et al. 2023). The results were presented at various international meetings.

4. Environmental and Antarctic Seismology

Last year, I was awarded the 2023 Australian Research Council’s Discovery Early Career Researcher Award, the most prestigious Australian Government award for an early career researcher. My project is based on the work in Antarctic seismology and correlation methods I started as a Ph.D. student. Previous cryoseismic study has primarily focused on characterizing seismic activities in icy environments, such as basal sliding, hydraulic activities, or fractures at ice shelf termini (Podolskiy & Walter 2016; Aster & Winberry 2017). However, studies into ice sheets’ internal structures and possible changes over time remain in infancy (Podolskiy & Walter 2016; Aster & Winberry 2017). Thus, this project aims to leverage the monitoring capacity in cryoseismic settings, especially in polar ice sheets, with an arsenal of seismological methods to explore the dataset.

In addition, I am interested in further ocean thermometry ideas where Wu et al. (2020) inventively demonstrated the possibility of monitoring the deep ocean temperature with repeating submarine earthquakes. This raises an intriguing question: “Can it be done without earthquakes?” Indeed, it has been demonstrated feasible to sense seasonal fluctuation in the frozen ground temperature (Lindner et al. 2021) and daily air temperatures on Earth (Ortiz et al. 2021) and Mars (Ortiz et al. 2022) by analyzing the noise records. Therefore, I am keen to adopt the noise-based monitoring framework to oceanic seismic data to unveil the trend of deep ocean temperatures. If proven successful, this technique will profoundly impact global climate change research because the ocean temperature trend is the most reliable indicator of global warming (State of the Climate 2022).

5. Future research concepts

Seismologists are facing an unprecedented expansion of seismic data from terrestrial seismic stations available for analysis, which requires industrious data processing methods. In the past, using ambient noise via correlation methods revolutionalized studies of the Earth’s interior. At a smaller scale, coda correlation wavefield has made a breakthrough in the deep Earth study community. The next decade is exciting, with several missions to the Moon collecting data, where the methods I have developed are readily applicable to gain insights into the planetary interior.

With my deep Earth expertise, I am excited to look for opportunities to utilize DAS data, which have been widely used for shallow Earth seismology, for studying the deep Earth’s interior, which will pave the way for technology in future space missions. Furthermore, machine learning, a multi-purpose technology, has found a way in earthquake seismology, mainly facilitating automatic data processing. The application of the new technology has been gradually but slowly finding its way into studies of the deep Earth’s interior, probably due to the community’s small size. With a solid background in applied mathematics, I am determined to expand research into machine learning, particularly supervised learning, to expand the usable data sets to set the stage for unsupervised learning to facilitate exploratory data analysis. In my perspective, these could hold the key for future discovery in deep Earth studies and environmental seismology in years to come.

References

Aster, R.C. & Winberry, J.P., 2017. Glacial seismology. Rep. Prog. Phys., 80, 126801. doi:10.1088/1361-6633/aa8473

Costa de Lima, T., Phạm, T.-S., Ma, X. & Tkalčić, H., 2023. An estimate of absolute shear-wave speed in the Earth’s inner core. Nat. Commun., 14, 1–10, Nature Publishing Group. doi:10.1038/s41467-023-40307-9

Hu, J., Pham, T.-S. & Tkalčić, H., 2023. Seismic moment tensor inversion with theory errors from 2D Earth structure: Implications for the 2009-2017 DPRK nuclear blasts. Geophys. J. Int.

Hu, J., Phạm, T.-S. & Tkalčić, H., 2023. Joint moment tensor and single force inversion with uncertainty estimate: Implications for the 2022 Hunga Tonga-Hunga Haʻapai volcanic eruption. Be Submitt.

Ishii, M. & Dziewoński, A.M., 2002. The innermost inner core of the earth: Evidence for a change in anisotropic behavior at the radius of about 300 km. Proc. Natl. Acad. Sci., 99, 14026–14030. doi:10.1073/pnas.172508499

Lindner, F., Wassermann, J. & Igel, H., 2021. Seasonal Freeze-Thaw Cycles and Permafrost Degradation on Mt. Zugspitze (German/Austrian Alps) Revealed by Single-Station Seismic Monitoring. Geophys. Res. Lett., 48, 2021GL094659. doi:10.1029/2021GL094659

Ortiz, H.D., Matoza, R.S., Johnson, J.B., Hernandez, S., Anzieta, J.C. & Ruiz, M.C., 2021. Autocorrelation Infrasound Interferometry. J. Geophys. Res. Solid Earth, 126, 2020JB020513. doi:10.1029/2020JB020513

Ortiz, H.D., Matoza, R.S. & Tanimoto, T., 2022. Autocorrelation Infrasound Interferometry on Mars. Geophys. Res. Lett., 49, 2021GL096225. doi:10.1029/2021GL096225

Phạm, T.-S. & Tkalčić, H., 2017. On the feasibility and use of teleseismic P wave coda autocorrelation for mapping shallow seismic discontinuities. J. Geophys. Res. Solid Earth, 122, 3776–3791. doi:10.1002/2017JB013975

Phạm, T.-S. & Tkalčić, H., 2018. Antarctic Ice Properties Revealed From Teleseismic P Wave Coda Autocorrelation. J. Geophys. Res. Solid Earth, 123, 7896–7912. doi:10.1029/2018JB016115

Phạm, T.-S. & Tkalčić, H., 2021. Constraining Floating Ice Shelf Structures by Spectral Response of Teleseismic P-Wave Coda: Ross Ice Shelf, Antarctica. J. Geophys. Res. Solid Earth, 126, 2020JB021082. doi:https://doi.org/10.1029/2020JB021082

Phạm, T.-S. & Tkalčić, H., 2021. Toward Improving Point-Source Moment-Tensor Inference by Incorporating 1D Earth Model’s Uncertainty: Implications for the Long Valley Caldera Earthquakes. J. Geophys. Res. Solid Earth, 126, 2021JB022477. doi:10.1029/2021JB022477

Phạm, T.-S. & Tkalčić, H., 2023. Up-to-fivefold reverberating waves through the Earth’s center and distinctly anisotropic innermost inner core. Nat. Commun., 14, 754, Nature Publishing Group. doi:10.1038/s41467-023-36074-2

Phạm, T.-S., Tkalčić, H., Sambridge, M. & Kennett, B.L.N., 2018. Earth’s Correlation Wavefield: Late Coda Correlation. Geophys. Res. Lett., 45, 3035–3042. doi:10.1002/2018GL077244

Podolskiy, E.A. & Walter, F., 2016. Cryoseismology. Rev. Geophys., 54, 708–758. doi:10.1002/2016RG000526

State of the Climate, 2022, CSIRO and The Bureau of Meteorology.

Tkalčić, H. & Phạm, T.-S., 2018. Shear properties of Earth’s inner core constrained by a detection of J waves in global correlation wavefield. Science, 362, 329–332. doi:10.1126/science.aau7649

Tkalčić, H. & Phạm, T.-S., 2020. Excitation of the global correlation wavefield by large earthquakes. Geophys. J. Int., 223, 1769–1779, Oxford Academic. doi:10.1093/gji/ggaa369

Tkalčić, H., Phạm, T.-S. & Wang, S., 2020. The Earth’s coda correlation wavefield: Rise of the new paradigm and recent advances. Earth-Sci. Rev., 208, 103285. doi:10.1016/j.earscirev.2020.103285

Wang, S. & Tkalčić, H., 2022. Scanning for planetary cores with single-receiver intersource correlations. Nat. Astron., 1–8, Nature Publishing Group. doi:10.1038/s41550-022-01796-8

Wu, W., Zhan, Z., Peng, S., Ni, S. & Callies, J., 2020. Seismic ocean thermometry. Science, 369, 1510–1515, American Association for the Advancement of Science. doi:10.1126/science.abb9519