Dr. Liang Ding — Research Website
🔍 Research Projects
Lithological Mapping and Uncertainty Quantification
• Lithological Mapping Using Aeromagnetic and Gravity Data,
• Swin Transformer–Based U-Shaped Network,
• Theoretical Analysis of Uncertainty Sources.
Ding, L., Bellefleur, G., Boulanger, O., & Vo, P. (2026). Supervised Swin Transformer-based predictive lithological mapping and uncertainty quantification using aeromagnetic and gravity data. Journal of Geophysical Research: Machine Learning and Computation, 3, e2025JH000882. https://doi.org/10.1029/2025JH000882
Read the full article →Seismic Data Denoising with Deep Learning
• Sparse-domain, image-to-image seismic denoising,
• Swin-Transformer-enhanced UNet,
• Strong, dataset-agnostic performance gains.
Earthquake Source Inversion
Improving seismic monitoring networks:
• Accurate source characterization using 3D background models
• Effective inversion using pre-computed 3D Greens function database
• Earthquake monitoring at regional and global networks
Triggering Mechanism of Earthquakes
Exploration of fault reactivation processes during hydraulic fracturing operations.
• Full moment tensors with uncertainties resolved using MTUQ.
• Foreshocks show +CLVD, indicating tensile opening linked to fluid injection processes.
• Aftershocks show +ISO and +CLVD, suggesting fluid migration and aseismic slip post-failure.
Ding, L., T. de Boer, M. H. Khosravi, G. Yang, E. Kravchinsky, S. K. Y. Lui, G. Grasselli, and Q. Liu (2026). Full moment tensor inversions of microseismic events revealing fault activation of the 17 August 2015 earthquake in the northern Montney Formation, British Columbia, Canada. Bulletin of the Seismological Society of America, accepted.
Explore Triggering Mechanisms →Laboratory Acoustic Emission Monitoring
Exploring failure processes through:
• Improving sensor coverage through network optimization
• AE monitoring and source characterization
• Controlled rock fracture experiments
• Uncertainty analysis and quantification
