Her research integrates seismic observations, physics-based modelling, and high-performance computational methods to investigate crustal deformation, fault mechanics, and seismic hazard. A significant component of her work focuses on the Himalayan region, particularly the structural and mechanical behaviour of the Main Himalayan Thrust. She is also engaged in research on seismic anisotropy, temporal velocity variations, intraplate and continental deep earthquakes, and the application of machine learning techniques in large-scale seismic data analysis.
Dr. Kanaujia’s work emphasizes reproducible research practices, rigorous quantitative interpretation, and the development of computational frameworks for handling large seismic datasets. She is committed to building a strong research ecosystem that integrates field observations, theory, and data-driven science.
In addition to research, she teaches core and advanced courses in Solid Earth Geophysics, Introduction to Earth Sciences, Climate Data Analysis, and Science Communication. Her teaching philosophy is rooted in conceptual clarity, quantitative reasoning, and connecting theoretical principles with real geophysical observations.
My research focuses on computational and quantitative seismology, aimed at understanding earthquake processes, lithospheric structure, and tectonic deformation through integrated data-driven and physics-based approaches. The central objective is to extract physically meaningful constraints on Earth structure and fault mechanics from large seismic datasets using robust analytical and numerical frameworks.
A significant component of my work investigates the Himalayan orogenic system, with particular emphasis on imaging the geometry and mechanical behaviour of the Main Himalayan Thrust (MHT). Using techniques such as Seismic tomography, seismic anisotropy analysis, receiver functions, ambient noise tomography, and waveform modelling, my research seeks to constrain crustal structure, strain localization, and the role of inherited structures in controlling seismicity.
I also work on temporal seismic velocity variations and anisotropic signatures as potential indicators of stress evolution and crustal fluid interactions. These studies contribute to a deeper understanding of earthquake preparation processes and lithospheric dynamics.
Methodologically, my research integrates high-performance computing, numerical simulations, inverse modelling, and increasingly, machine learning techniques for large-scale seismic data analysis. Emphasis is placed on reproducibility, quantitative uncertainty estimation, and the physical interpretation of seismic observables.
Through the Computational Seismology Lab, the goal is to develop a rigorous and computationally advanced research program that bridges observational seismology, tectonics, and geodynamic modelling.
Research Supervision (Present Status)
Postdoctoral Researcher
Doctoral Student
Published/ Accepted
* Corresponding Author
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Department of Earth and Environmental Sciences
Indian Institute of Science Education and Research (IISER) Mohali
The Computational Seismology Lab focuses on quantitative and physics-based investigation of earthquake processes and lithospheric structure using advanced computational methods. The lab integrates seismic observations, numerical modelling, and data-driven approaches to study crustal deformation, fault mechanics, and seismic hazard.
Current research themes include:
The lab emphasizes reproducible computational workflows and high-performance analysis of large seismic datasets.
| Instrument | Make / Model | Primary Utility | Technical Capability |
|---|---|---|---|
| Broadband Seismometer | Kinemetrics (Model: MBB-2 Miniature 120s BroadBand Seismometer) | Recording broadband ground motion for local, regional, and teleseismic earthquakes. Used for crustal imaging, source studies, and tectonic investigations. | High dynamic range; wide frequency bandwidth; low self-noise; suitable for permanent and temporary deployments. |
| Distributed Acoustic Sensing (DAS) System | Kinemetrics DAS (Model: Pebble three component datalogger) | Fiber-optic based dense seismic monitoring for high spatial resolution measurements. Suitable for near-surface imaging and structural monitoring. | Dense spatial sampling along fiber length; real-time acquisition; high temporal resolution; scalable deployment. |
| Dedicated Computational Server | High-performance multi-core Master and compute server | Large-scale seismic data processing, waveform inversion, numerical simulations, and parallel computing tasks. | Multi-core CPU architecture; high RAM capacity; optimized for parallel processing; suitable for HPC-based geophysical modelling. |
| Network Attached Storage (NAS) Server | Enterprise-grade NAS system | Secure storage, backup, and archival of seismic datasets and modelling outputs. | Redundant storage configuration (RAID); high data throughput; multi-user access; scalable storage architecture. |
| High-End Computational Workstations (6 Units) | Custom-built scientific workstations | Data visualization, waveform analysis, machine learning workflows, and routine computational tasks. | High-performance CPUs/GPUs; large RAM; optimized for geophysical modelling, inversion, and AI/ML applications. |
The Computational Seismology Lab welcomes applications from motivated students (MS, PhD, and Postdocs) and researchers interested in quantitative seismology, computational modelling, and data-driven Earth science research. Prospective candidates may contact the PI with a CV and research statement.
Professional Appointments
Selected Scientific Contributions
Research Grants and Fellowships
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