5 citations · 5 across the 4 of their papers we have counts for
5 papers
DeepStratNet: A Context-Aware Coordinate Regression Framework for Seismic Horizon Tracking under Sparse Labels
Aniq Ahmad, Musham Ahmad Malik, Ahmad Mustafa +1
Automatic horizon tracking is a foundational task in 3D seismic interpretation. Most existing deep learning approaches formulate it as dense semantic segmentation, typically using…
Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
Aniq Ahmad, Heather Bedle, Ahmad Mustafa
The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation. The Segment Anything Model (SAM), in p…
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar +1
In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine learning-based cl…
A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior
Jorge Quesada, Chen Zhou, Prithwijit Chowdhury +5
Machine learning has taken a critical role in seismic interpretation workflows, especially in fault delineation tasks. However, despite the recent proliferation of pretrained model…
CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults
Mohit Prabhushankar, Kiran Kokilepersaud, Jorge Quesada +6
Crowdsourcing annotations has created a paradigm shift in the availability of labeled data for machine learning. Availability of large datasets has accelerated progress in common k…