Beyond Surveillance: A Hybrid Acoustic Pressure Data Model for Early Detection of Third Party Interference on Pipelines

Abstract

Pipeline vandalism and crude theft remain the primary threats to the resilience of Nigeria's energy infrastructure and environment. Conventional pressure monitoring systems often generate high rates of false alarms or detect breaches only after containment is lost. This paper evaluates a lightweight, hybrid software architecture combining Distributed Acoustic Sensing (DAS) data with hydraulic pressure transient analysis to identify intrusion attempts before a spill occurs. Due to the classified nature of empirical breach data, the methodology involved generating a high-fidelity synthetic training dataset modeling manual digging, mechanical drilling, and standard operational background noise. A Multi-Input Convolutional Neural Network (CNN) was developed to perform feature-level sensor fusion. The architecture transformed 1D acoustic time-series data into 2D Mel-spectrograms, fusing these spatial features with 1D temporal pressure wave data. The hybrid model achieved a 92% accuracy rate in distinguishing between theft attempts and benign operational vibrations, significantly outperforming standalone pressure monitoring systems. Crucially, the system demonstrated a geolocation accuracy of within 20 meters, enabling rapid security response. The study concludes that advanced, computationally efficient sensor fusion democratizes digital transformation for marginal field operators, successfully shifting the focus from reactive leak detection to proactive, cost-effective interference prevention.