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20202025
most citedPINNtomo: Seismic tomography using physics-informed neural networks

41 citations · 82 across the 8 of their papers we have counts for

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physics.geo-ph2025

Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator

Ayrat Abdullin, Umair Bin Waheed, Leo Eisner +1

Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motiva…

physics.geo-ph2025

Learning from Imperfect Labels: A Physics-Aware Neural Operator with Application to DAS Data Denoising

Yang Cui, Denis Anikiev, Umair Bin Waheed +1

Supervised deep learning methods typically require large datasets and high-quality labels to achieve reliable predictions. However, their performance often degrades when trained on…

physics.geo-ph2025

SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning

Goodluck Archibong, Ardiansyah Koeshidayatullah, Umair Waheed +3

Seismic interpretation is vital for understanding subsurface structures but remains labor-intensive, subjective, and computationally demanding. While deep learning (DL) offers prom…

physics.geo-ph202434 cited

Dictionary Learning with Convolutional Structure for Seismic Data Denoising and Interpolation

Murad Almadani, Umair bin Waheed, Mudassir Masood +1

Seismic data inevitably suffers from random noise and missing traces in field acquisition. This limits the utilization of seismic data for subsequent imaging or inversion applicati…

physics.geo-ph2021

Deep learning for low-magnitude earthquake detection on a multi-level sensor network

Ahmed Shaheen, Umair bin Waheed, Michael Fehler +2

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe.…