![Peidong Shi](https://profiles-cardiff.imgix.net/attachments/7187?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Peidong Shi
(he/him)
- Available for postgraduate supervision
Teams and roles for Peidong Shi
Lecturer in Geophysics
Overview
My research bridges geophysics, machine learning, and earthquake science, addressing challenges such as induced seismicity, sustainable energy solutions, and real-time seismic monitoring. With research experience across various institutes at ETH Zurich, ISTerre, and Leeds University, I am dedicated to research integrating AI, data science, digital twins, and high-performance computing to investigate Earth’s dynamic processes, with a focus on earthquake physics, geo-hazards, and renewable geo-energy solutions. My research goal is to advance our understanding of earthquake physical processes for seismic hazard mitigation and geo-energy project optimization. Through research and international collaborations, I’m passionate about driving impactful scientific discoveries and inspiring the next generation of researchers. Explore my research, projects, and publications to learn how we can collaborate towards a more resilient and sustainable future.
Opportunities to Join Us:
(1) Fully Funded PhD Opportunities:
For international (all overseas countries) and UK students seeking a PhD position:
- MineSentinel: AI-Powered Monitoring and Forecasting for Safer and Sustainable Critical Mineral Mining, TARGET DTP program, Start date: 1 October 2026, Application deadline: Wednesday 7th January 2026 (GMT), Application portal: check https://target.le.ac.uk/how-to-apply/
- Unlocking the Earthquake Precursor Puzzle: from AI Detection to the Physics of Precursors, GW4+ DLTP program, Start date: 1 October 2026, Application deadline: Thursday 8 January 2026 at 2359 (GMT), Application portal: check here.
- Time dependent imaging of fracturing and faulting from mine induced seismicity, TARGET DTP program, Start date: 1 October 2026, Application deadline: Wednesday 7th January 2026 (GMT), Application portal: check https://target.le.ac.uk/how-to-apply/
For Chinese students seeking a PhD position:
- Topics on earthquake seismology, AI, Geohazard, and Georesources, China Scholarship Council PhD Scholarship, Start date: 1 October 2027, Application deadline: Early November 2026, Application portal: contact [email protected]
(2) Fully Funded Postdoctoral Research Associate (PDRA) Opportunities:
For global researchers and PhD candidates (will receive the degree soon):
- Marie Skłodowska-Curie Postdoctoral Fellowships, Application deadline: September each year, To apply for this scheme please contact [email protected] to discuss projects and get support.
- Royal Society Newton International Fellowships, Application deadline: Usually early March each year, To apply for this scheme please contact [email protected] to discuss projects and get support.
For Chinese researchers and PhD candidates (will receive the degree soon):
- Chinese Scholarship Council Postdoc Scheme, Application deadline: check official website, To apply for this scheme please contact [email protected] to discuss projects and get support.
(3) Fully Funded Visiting Scholars and Students Opportunities:
For Chinese visiting scholars and students:
- Chinese Scholarship Council Visiting Scheme, Application deadline: check official website, To apply for this scheme please contact [email protected] to discuss projects and get support.
Publication
2025
- Ermert, L. A. et al., 2025. Toward a digital twin for seismic waves of induced events in a geothermal reservoir. Bulletin of the Seismological Society of America 115 (6), pp.2705-2720. (10.1785/0120250125)
- He, Y. et al., 2025. Experimental observation of moduli dispersion and attenuation at seismic frequencies in saturated tight rock: effect of microstructure and fluid viscosity. Geophysical Journal International 240 , pp.1308–1330. (10.1093/gji/ggae442)
- Schultz, R. et al., 2025. The bound growth of induced earthquakes could de-risk hydraulic fracturing. Communications Earth & Environment 6 995. (10.1038/s43247-025-02881-2)
2024
- Finger, C. et al., 2024. Spatio-temporal evolution of hypocenters and moment tensors derived from time-reverse imaging. Presented at: EGU General Assembly 2024 Vienna, Austria 14-19 April 2024. (10.5194/egusphere-egu24-14828)
- He, Y. -. et al., 2024. Experimental investigation of pore-filling substitution effect on frequency-dependent elastic moduli of Berea sandstone. Geophysical Journal International 238 (2), pp.902–921. (10.1093/gji/ggae195)
- He, Y. et al., 2024. Laboratory experiments and theoretical study of pressure and fluid influences on acoustic response in tight rocks with pore microstructure. Geophysical Prospecting 72 (5), pp.1896-1918. (10.1111/1365-2478.13466)
- Obermann Anne, A. et al., 2024. High-resolution seismic methods for geothermal exploration and monitoring across the Hengill volcanic area. Presented at: EGU General Assembly 2024 Vienna, Austria 14–19 Apr 2024. (10.5194/egusphere-egu24-21112)
- Parastatidis, E. et al., 2024. Multichannel coherence migration grid search (MCMgs) in locating microseismic events recorded by a surface array. Geophysical Journal International 236 (2), pp.1042–1052. (10.1093/gji/ggad465)
- Shi, P. et al. 2024. From labquakes to megathrusts: scaling deep learning based pickers over 15 orders of magnitude. Journal of Geophysical Research: Machine Learning and Computation 11 (4) e2024JH000220. (10.1029/2024JH000220)
- Shi, P. et al. 2024. Machine learning based real-time microseismic monitoring and stimulated fracture characterization at the Utah FORGE Geothermal site. Presented at: EGU General Assembly 2024 Vienna, Austria 14–19 Apr 2024. (10.5194/egusphere-egu24-15621)
2023
- De solda, M. et al., 2023. A deep learning-based workflow for microseismic event detection. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-1638)
- Ermert, L. et al., 2023. Modeling the waveforms of induced seismicity sequences with application to Utah FORGE. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-11883)
- He, Y. et al., 2023. A physical modelling study of waveform amplification effects of reservoir heterogeneity on time‐lapse seismic attribute analysis. Geophysical Prospecting 71 (2), pp.206-226. (10.1111/1365-2478.13298)
- Mohammadigheymasi, H. et al., 2023. IPIML: A deep-scan earthquake detection and location workflow Integrating Pair-Input deep learning model and Migration Location method. IEEE Transactions on Geoscience and Remote Sensing 61 (10.1109/tgrs.2023.3293914)
- Mohammadigheymasi, H. et al., 2023. An automated earthquake detection algorithm by combining pair-input deep learning and migration location methods. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-15180)
- Nakata, N. et al., 2023. Elastic Characterization at Utah FORGE: P-wave Tomography and VSP Subsurface Imaging. Presented at: 48th Workshop on Geothermal Reservoir Engineering California, USA 6-8 February 2023. Proceedings 48th Workshop on Geothermal Reservoir Engineering.
- Shi, P. et al. 2023. Near-real-time microseismic monitoring with machine-learning and waveform back-projection at the Utah FORGE geothermal site. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-13227)
- Yan, B. , Ji, Y. and Shi, P. 2023. Frequency-dependent inversion based on spherical-wave reflection coefficient in elastic medium: Theory and methodology. Journal of Applied Geophysics 209 104908. (10.1016/j.jappgeo.2022.104908)
2022
- Ermert, L. et al., 2022. Modeling induced micro-earthquakes for an experimental enhanced geothermal system site. Presented at: 20th Swiss Geoscience Meeting 2022 Lausanne, Switzerland 18-20 November 2022.
- Obermann, A. et al., 2022. Combined large-N seismic arrays and DAS fiber optic cables across the Hengill geothermal field, Iceland. Seismological Research Letters 93 (5), pp.2498-2514. (10.1785/0220220073)
- Shi, P. et al. 2022. MALMI: An automated earthquake detection and location workflow based on machine learning and waveform migration. Seismological Research Letters 93 (5), pp.2467-2483. (10.1785/0220220071)
- Shi, P. et al. 2022. MALMI: towards combining machine learning and waveform migration for fully automated earthquake detection and location. Presented at: EGU General Assembly 2022 Vienna, Austria 23-27 May 2022. Copernicus {GmbH}(10.5194/egusphere-egu22-6087)
2021
- Parastatidis, E. et al., 2021. Minimising the computational time of a waveform based location algorithm. Presented at: EGU General Assembly (online) 2021 Online 19 - 30 April 2021. (10.5194/egusphere-egu21-15664)
- Shi, P. , Seydoux, L. and Poli, P. 2021. Unsupervised learning of seismic wavefield features: clustering continuous array seismic data during the 2009 L'Aquila earthquake. Journal of Geophysical Research: Solid Earth 126 (1) e2020JB020506. (10.1029/2020JB020506)
2020
- Li, L. et al., 2020. Recent advances and challenges of waveform‐based seismic location methods at multiple scales. Reviews of Geophysics 58 (1) e2019RG000667. (10.1029/2019RG000667)
- Shi, P. , Seydoux, L. and Poli, P. 2020. Direct fault states assessment from wavefield properties: application to the 2009 L'Aquila earthquake. Presented at: EGU General Assembly 2020 Online 4-8 May 2020. (10.5194/egusphere-egu2020-11655)
- Yuan, S. et al., 2020. Goal-oriented inversion-based NMO correction using a Convex l 2,1 -norm. IEEE Geoscience and Remote Sensing Letters 17 (1), pp.162-166. (10.1109/LGRS.2019.2915520)
2019
- Shi, P. et al. 2019. Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory. Geophysical Journal International 216 (3), pp.1842-1866. (10.1093/gji/ggy132)
- Shi, P. et al. 2019. Automated seismic waveform location using Multichannel Coherency Migration (MCM)—II. Application to induced and volcano-tectonic seismicity. Geophysical Journal International 216 (3), pp.1608-1632. (10.1093/gji/ggy507)
- Wang, T. et al., 2019. AVAZ inversion for fracture weakness based on three-term Rüger equation. Journal of Applied Geophysics 162 , pp.184-193. (10.1016/j.jappgeo.2018.12.013)
- Yuan, S. et al., 2019. Sparse bayesian learning-based seismic high-resolution time-frequency analysis. {IEEE} Geoscience and Remote Sensing Letters 16 (4), pp.623-627. (10.1109/lgrs.2018.2883496)
2018
- Shi, P. 2018. Microseismic full waveform modeling and location. PhD Thesis , University of Leeds.
- Shi, P. et al. 2018. Microseismic full waveform modeling in anisotropic media with moment tensor implementation. Surveys in Geophysics: An International Review Journal Covering the Entire Field of Earth and Space Sciences 39 (4), pp.567–611. (10.1007/s10712-018-9466-2)
- Shi, P. et al. 2018. Fracture identification in a tight sandstone reservoir: A seismic anisotropy and automatic multisensitive attribute fusion framework. IEEE Geoscience and Remote Sensing Letters 15 (10), pp.1525-1529. (10.1109/LGRS.2018.2853631)
- Yuan, S. et al., 2018. Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geoscience and Remote Sensing Letters 15 (2), pp.272-276. (10.1109/lgrs.2017.2785834)
2017
- Yan, B. et al., 2017. Improved eigenvalue-based coherence algorithm with dip scanning. Geophysics 82 (2), pp.1MA-Z12. (10.1190/geo2016-0149.1)
2014
- Chen, Y. et al., 2014. Irregular seismic data reconstruction using a percentile-half-thresholding algorithm. Journal of Geophysics and Engineering 11 (6) 065001. (10.1088/1742-2132/11/6/065001)
- Chuai, X. et al., 2014. Applications of texture attribute analysis to seismic interpretation. Journal of Central South University 21 (9), pp.3617–3626. (10.1007/s11771-014-2344-2)
Articles
- Chen, Y. et al., 2014. Irregular seismic data reconstruction using a percentile-half-thresholding algorithm. Journal of Geophysics and Engineering 11 (6) 065001. (10.1088/1742-2132/11/6/065001)
- Chuai, X. et al., 2014. Applications of texture attribute analysis to seismic interpretation. Journal of Central South University 21 (9), pp.3617–3626. (10.1007/s11771-014-2344-2)
- Ermert, L. A. et al., 2025. Toward a digital twin for seismic waves of induced events in a geothermal reservoir. Bulletin of the Seismological Society of America 115 (6), pp.2705-2720. (10.1785/0120250125)
- He, Y. -. et al., 2024. Experimental investigation of pore-filling substitution effect on frequency-dependent elastic moduli of Berea sandstone. Geophysical Journal International 238 (2), pp.902–921. (10.1093/gji/ggae195)
- He, Y. et al., 2024. Laboratory experiments and theoretical study of pressure and fluid influences on acoustic response in tight rocks with pore microstructure. Geophysical Prospecting 72 (5), pp.1896-1918. (10.1111/1365-2478.13466)
- He, Y. et al., 2025. Experimental observation of moduli dispersion and attenuation at seismic frequencies in saturated tight rock: effect of microstructure and fluid viscosity. Geophysical Journal International 240 , pp.1308–1330. (10.1093/gji/ggae442)
- He, Y. et al., 2023. A physical modelling study of waveform amplification effects of reservoir heterogeneity on time‐lapse seismic attribute analysis. Geophysical Prospecting 71 (2), pp.206-226. (10.1111/1365-2478.13298)
- Li, L. et al., 2020. Recent advances and challenges of waveform‐based seismic location methods at multiple scales. Reviews of Geophysics 58 (1) e2019RG000667. (10.1029/2019RG000667)
- Mohammadigheymasi, H. et al., 2023. IPIML: A deep-scan earthquake detection and location workflow Integrating Pair-Input deep learning model and Migration Location method. IEEE Transactions on Geoscience and Remote Sensing 61 (10.1109/tgrs.2023.3293914)
- Obermann, A. et al., 2022. Combined large-N seismic arrays and DAS fiber optic cables across the Hengill geothermal field, Iceland. Seismological Research Letters 93 (5), pp.2498-2514. (10.1785/0220220073)
- Parastatidis, E. et al., 2024. Multichannel coherence migration grid search (MCMgs) in locating microseismic events recorded by a surface array. Geophysical Journal International 236 (2), pp.1042–1052. (10.1093/gji/ggad465)
- Schultz, R. et al., 2025. The bound growth of induced earthquakes could de-risk hydraulic fracturing. Communications Earth & Environment 6 995. (10.1038/s43247-025-02881-2)
- Shi, P. et al. 2018. Microseismic full waveform modeling in anisotropic media with moment tensor implementation. Surveys in Geophysics: An International Review Journal Covering the Entire Field of Earth and Space Sciences 39 (4), pp.567–611. (10.1007/s10712-018-9466-2)
- Shi, P. et al. 2019. Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory. Geophysical Journal International 216 (3), pp.1842-1866. (10.1093/gji/ggy132)
- Shi, P. et al. 2022. MALMI: An automated earthquake detection and location workflow based on machine learning and waveform migration. Seismological Research Letters 93 (5), pp.2467-2483. (10.1785/0220220071)
- Shi, P. et al. 2024. From labquakes to megathrusts: scaling deep learning based pickers over 15 orders of magnitude. Journal of Geophysical Research: Machine Learning and Computation 11 (4) e2024JH000220. (10.1029/2024JH000220)
- Shi, P. et al. 2019. Automated seismic waveform location using Multichannel Coherency Migration (MCM)—II. Application to induced and volcano-tectonic seismicity. Geophysical Journal International 216 (3), pp.1608-1632. (10.1093/gji/ggy507)
- Shi, P. , Seydoux, L. and Poli, P. 2021. Unsupervised learning of seismic wavefield features: clustering continuous array seismic data during the 2009 L'Aquila earthquake. Journal of Geophysical Research: Solid Earth 126 (1) e2020JB020506. (10.1029/2020JB020506)
- Shi, P. et al. 2018. Fracture identification in a tight sandstone reservoir: A seismic anisotropy and automatic multisensitive attribute fusion framework. IEEE Geoscience and Remote Sensing Letters 15 (10), pp.1525-1529. (10.1109/LGRS.2018.2853631)
- Wang, T. et al., 2019. AVAZ inversion for fracture weakness based on three-term Rüger equation. Journal of Applied Geophysics 162 , pp.184-193. (10.1016/j.jappgeo.2018.12.013)
- Yan, B. , Ji, Y. and Shi, P. 2023. Frequency-dependent inversion based on spherical-wave reflection coefficient in elastic medium: Theory and methodology. Journal of Applied Geophysics 209 104908. (10.1016/j.jappgeo.2022.104908)
- Yan, B. et al., 2017. Improved eigenvalue-based coherence algorithm with dip scanning. Geophysics 82 (2), pp.1MA-Z12. (10.1190/geo2016-0149.1)
- Yuan, S. et al., 2020. Goal-oriented inversion-based NMO correction using a Convex l 2,1 -norm. IEEE Geoscience and Remote Sensing Letters 17 (1), pp.162-166. (10.1109/LGRS.2019.2915520)
- Yuan, S. et al., 2019. Sparse bayesian learning-based seismic high-resolution time-frequency analysis. {IEEE} Geoscience and Remote Sensing Letters 16 (4), pp.623-627. (10.1109/lgrs.2018.2883496)
- Yuan, S. et al., 2018. Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geoscience and Remote Sensing Letters 15 (2), pp.272-276. (10.1109/lgrs.2017.2785834)
Conferences
- De solda, M. et al., 2023. A deep learning-based workflow for microseismic event detection. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-1638)
- Ermert, L. et al., 2023. Modeling the waveforms of induced seismicity sequences with application to Utah FORGE. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-11883)
- Ermert, L. et al., 2022. Modeling induced micro-earthquakes for an experimental enhanced geothermal system site. Presented at: 20th Swiss Geoscience Meeting 2022 Lausanne, Switzerland 18-20 November 2022.
- Finger, C. et al., 2024. Spatio-temporal evolution of hypocenters and moment tensors derived from time-reverse imaging. Presented at: EGU General Assembly 2024 Vienna, Austria 14-19 April 2024. (10.5194/egusphere-egu24-14828)
- Mohammadigheymasi, H. et al., 2023. An automated earthquake detection algorithm by combining pair-input deep learning and migration location methods. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-15180)
- Nakata, N. et al., 2023. Elastic Characterization at Utah FORGE: P-wave Tomography and VSP Subsurface Imaging. Presented at: 48th Workshop on Geothermal Reservoir Engineering California, USA 6-8 February 2023. Proceedings 48th Workshop on Geothermal Reservoir Engineering.
- Obermann Anne, A. et al., 2024. High-resolution seismic methods for geothermal exploration and monitoring across the Hengill volcanic area. Presented at: EGU General Assembly 2024 Vienna, Austria 14–19 Apr 2024. (10.5194/egusphere-egu24-21112)
- Parastatidis, E. et al., 2021. Minimising the computational time of a waveform based location algorithm. Presented at: EGU General Assembly (online) 2021 Online 19 - 30 April 2021. (10.5194/egusphere-egu21-15664)
- Shi, P. et al. 2022. MALMI: towards combining machine learning and waveform migration for fully automated earthquake detection and location. Presented at: EGU General Assembly 2022 Vienna, Austria 23-27 May 2022. Copernicus {GmbH}(10.5194/egusphere-egu22-6087)
- Shi, P. et al. 2023. Near-real-time microseismic monitoring with machine-learning and waveform back-projection at the Utah FORGE geothermal site. Presented at: EGU General Assembly 2023 Vienna, Austria 23 - 28 April 2023. (10.5194/egusphere-egu23-13227)
- Shi, P. et al. 2024. Machine learning based real-time microseismic monitoring and stimulated fracture characterization at the Utah FORGE Geothermal site. Presented at: EGU General Assembly 2024 Vienna, Austria 14–19 Apr 2024. (10.5194/egusphere-egu24-15621)
- Shi, P. , Seydoux, L. and Poli, P. 2020. Direct fault states assessment from wavefield properties: application to the 2009 L'Aquila earthquake. Presented at: EGU General Assembly 2020 Online 4-8 May 2020. (10.5194/egusphere-egu2020-11655)
Thesis
- Shi, P. 2018. Microseismic full waveform modeling and location. PhD Thesis , University of Leeds.
Research
Induced Seismicity and Geo-energy Applications
Induced seismicity poses significant challenges for geo-energy projects such as enhanced geothermal systems, carbon geological sequestration, and natural gas storage. My research delves into the intricate interplay between fluid dynamics, stress changes, and rock properties to unravel induced seismicity mechanisms under realistic subsurface conditions. By advancing our understanding of these processes, I aim to mitigate induced earthquake risks, enabling safe and sustainable exploitation of geo-energy resources and carbon sequestration practices.
Earthquake Physics and Hazard Mitigation
Understanding earthquake rupture mechanics is a fundamental pursuit in Earth Sciences and is also crucial to mitigating seismic hazards. My research explores earthquake source parameters, including source geometry, focal mechanism, and stress drop to investigate earthquake preparation and nucleation processes. Additionally, I integrate site-specific conditions, fault dynamics, and probabilistic models to improve earthquake risk assessment and forecasting, contributing to proactive hazard mitigation strategies.
AI and Machine Learning Seismology
The advent of AI and deep learning has revolutionized seismic data analysis and promises to shape the future of seismology studies. My research is dedicated to developing state-of-the-art machine learning models/techniques to automate seismological analyses and elevate the accuracy of crucial processes, with a focus on real-time applications. Inspired by the remarkable "emergent ability" of generative models, I am particularly interested in pioneering earthquake foundation models for sophisticated seismological studies. These models hold immense promise for knowledge discovery in complex and massive geophysical data, fueling groundbreaking advancements in seismology.
Geophysical Monitoring and Imaging for Sustainable Energy
The transition to a sustainable and carbon-neutral future hinges on efficient and safe energy technologies, such as geothermal energy and carbon storage. My research develops innovative geophysical monitoring and imaging methods/tools to maximize energy exploitation and storage efficiency while mitigating potential geo-hazards. The advancements can empower us to identify favorable sites, optimize exploitation strategies, image hidden faults, and effectively de-risk associated processes, supporting the global energy transition.
Earth System Digital Twins and Smart City
Building digital twins of Earth systems and urban environments offers transformative possibilities for hazard forecasting, resilience planning, and urban development optimization. I am particularly interested in integrating geophysical imaging, geospatial data, and simulation techniques, to create multi-scale digital twins. With these innovations, I hope to create highly realistic digital twins that mirror the complexities of the real world across varying scales—from local geological scales and cities to the entire Earth systems, supporting interdisciplinary solutions for smart cities and sustainable development.
Current Project:
- EFFSIMMSI: Advancing Induced Earthquake Forecasting and Fracturing Dynamics via Innovative Scale-Invariant Seismic Monitoring and Multi-Sensor Integration, Swiss National Science Foundation (SNSF) Project Funding, Project PI, Budget: 316’960 CHF.
Teaching
- Hazards, Risk and Resilience
- Environmental and Water Hazards Case Studies
- Petroleum, Geoenergy and Basin Analysis
- Earth and Environmental System Modelling and Applications
- Numerical modelling of Environmental Hazard processes
- Geography Field Skills
Biography
- 2019 Ph.D. in Geophysics, University of Leeds, UK.
- 2015 Master in Applied Geophysics, China University of Petroleum-Beijing, China.
- 2012 Bachelor in Exploration Geophysics, China University of Petroleum-Beijing, China.
Honours and awards
- 2022 Wiley Top Cited Article 2020-2021
- 2021 Outstanding Prize of the 1st BGP Cup Exploration Geophysics Competition
Professional memberships
- American Geophysical Union (AGU)
- European Geosciences Union (EGU)
- European Association of Geoscientists and Engineers (EAGE)
- Society of Exploration Geophysicists (SEG)
Academic positions
- 2024 - Now Lecturer, School of Earth and Environmental Sciences, Cardiff University, UK.
- 2023 - 2024 Senior Researcher (Oberassistent), Swiss Seismological Service, ETH Zürich, Switzerland.
- 2021 - 2023 Postdoctoral Research Fellow, Swiss Seismological Service, ETH Zürich, Switzerland.
- 2019 - 2021 Postdoctoral Research Fellow, ISTerre, Université Grenoble Alpes, France.
- 2015 - 2019 Teaching Assistant, School of Earth and Environment, University of Leeds, UK.
Committees and reviewing
- Reviewer: Nature Communications; Nature Communications Earth & Environment; Geophysical Journal International; Journal of Geophysical Research: Machine Learning and Computation; Seismological Research Letters; Bulletin of the Seismological Society of America; Geophysics; Geophysical Prospecting; Scientific Reports; Frontiers in Earth Science; IEEE Signal Processing Magazine; IEEE Transactions on Geoscience and Remote Sensing; IEEE Geoscience and Remote Sensing Letters; Geomatics, Natural Hazards and Risk; Petroleum Science; Journal of Applied Geophysics; Computers and Geotechnics; Earth, Planets and Space; Energies; Acta Geophysica; Journal of Geophysics and Engineering; Petroleum Science Bulletin; Exploration Geophysics; SN Applied Sciences.
- Organizer and Guest editor of the special issue “Machine Learning Approaches for Geophysical Data Analysis” in Applied Sciences.
Supervisions
- Induced Seismicity and Geo-energy Applications
- AI and Machine Learning Seismology
- Earthquake Physics and Hazard Mitigation
- Earth System Digital Twins and Smart City
- Geophysical Monitoring and Imaging for Sustainable Energy
- Advanced Seismic Monitoring and Real-time Analysis
Current supervision
Shengyuan Zhang
Past projects
- Advancing Induced Earthquake Forecasting and Fracturing Dynamics via Innovative Scale-Invariant Seismic Monitoring and Multi-Sensor Integration Swiss National Science Foundation (SNSF) Project Funding
Contact Details
Research themes
Specialisms
- Machine learning seismology
- Earthquake physics
- Data science in geophysics
- Geo-energy exploitation
- Geohazard monitoring and mitigation