Deep Learning Approaches for Real-Time Quality Control in OBN Acquisition

This study presents a novel deep learning framework for real-time quality control in Ocean Bottom Node (OBN) seismic acquisition. Traditional QC methods rely on manual inspection and basic statistical measures, leading to delayed detection of data quality issues. Our approach employs convolutional neural networks trained on diverse OBN datasets to automatically identify noise patterns, sensor malfunctions, and data artifacts in real-time during acquisition.

The methodology integrates automated feature extraction with pattern recognition algorithms to classify data quality issues across multiple categories including instrument noise, environmental interference, and coupling problems. Field trials demonstrate 95% accuracy in detecting quality issues compared to expert manual assessment, with processing speeds enabling real-time implementation.

Results show significant improvements in data quality and reduced post-acquisition processing time. The system successfully identified problematic nodes 80% faster than conventional methods, enabling immediate corrective actions during acquisition campaigns. This approach enhances operational efficiency and data quality in modern seabed seismic surveys.”*