Assistant Professor · Institute of Geophysics, Polish Academy of Sciences
Seismic imaging · Full-waveform inversion · Physics-informed machine learning
I image the Earth's interior with seismic waves, and I build the computational methods that make those images trustworthy.
My work sits between classical geophysics and machine learning: wide-angle and 3-component seismic imaging of the crust and upper mantle, full-waveform and impedance inversion, and deep learning that is constrained by physics rather than replacing it. A recurring theme in my research is calibrated uncertainty — an inversion result is only useful if you know how much to trust it.
- Lithospheric-scale seismic imaging, crust–mantle structure and seismic anisotropy
- Full-waveform inversion, velocity model building, tomography
- Physics-informed deep learning for inverse problems
- CCUS: seismic characterisation and monitoring for CO2 storage
Previously at CSIR-NGRI (India), where my PhD produced high-resolution crustal velocity and anisotropy models of the Archean Dharwar Craton from 3-C wide-angle seismic data.
| Project | What it does |
|---|---|
| DEEP_FWI_GIPE | Reliability-calibrated deep residual FWI. A geometry-invariant physics encoding lets one trained ensemble transfer to acquisition geometries it has never seen, and a held-out-shot physics audit repairs its confidence intervals without ground truth. |
| Physics-Informed-Neural-Network | ADMM-guided physics-informed deep learning for 2-D acoustic impedance inversion with reweighted ℓ1 sparse regularization. |
| Seismic_Processing_Inversion_Modelling_WaveSimulation | Notebooks for seismic processing, wave simulation and inversion workflows. |
| Milestone_Project_AIML | Applied machine-learning and deep-learning projects: time-series forecasting, CNN image classification, NLP and unsupervised clustering. |
Everything is MIT-licensed and reproducible — pre-trained checkpoints, example data and quick-start scripts included.
- Reliability-calibrated deep residual full-waveform inversion using geometry-invariant physics encoding — submitted to Computers & Geosciences · arXiv:2607.28535
- Calibrated uncertainty for wide-angle crustal models: how firmly is the Dharwar Craton Moho actually constrained? — submitted to Geophysical Journal International · arXiv:2607.28199
- ADMM-guided physics-informed deep learning for 2-D acoustic impedance inversion — submitted to IEEE TGRS · EarthArXiv
- Deep crustal structure and compositions of the Dharwar Craton from 3-C wide-angle seismic data — Journal of Asian Earth Sciences (2022)
- Upper-crustal structure along the Perur–Chikmagalur 3-C profile, Archean Dharwar Province — Acta Geophysica (2023)
Seismic software — ProMax · FOCUS (Echoes) · Seismic Unix · Petrel · OpendTect · DSG Landmark · Rayinvr · Deepwave
Open to collaboration on seismic imaging, inverse problems, uncertainty quantification and CCUS monitoring.