240 citations · 323 across the 11 of their papers we have counts for
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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…
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…
Explainable AI for microseismic event detection
Ayrat Abdullin, Denis Anikiev, Umair Bin Waheed
Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications. We apply Explainable Artif…
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…
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
Kamaljyoti Nath, Khemraj Shukla, Victor C. Tsai +6
Full Waveform Inversion (FWI) is an important geophysical technique considered in subsurface property prediction. It solves the inverse problem of predicting high-resolution Earth…