Leveraging Multi-Modal Smartphone Sensors for Ranging and Estimating the Intensity of Explosion Events


Our society, unfortunately, is increasingly becoming exposed to explosion events that could have serious consequences. While explosion events like intentionally triggered bombs cause obvious harm to life and property, other explosions intended for benign purposes in quarries and construction zones may also cause unintended harm as a result of emanating seismic vibrations. As of today, detecting explosions, ranging them, and estimating their intensities are all accomplished only by seismometers that sense the associated ground vibrations and pressure changes as a result of their triggering. Unfortunately, seismometers are bulky, expensive and unsuitable for the ubiquitous use. In this paper, our broad motivation is to demonstrate the feasibility of leveraging the pervasive sensing and processing capabilities of modern smartphones to analyze explosion events. Within this context, we specifically address the problem of ranging and estimating the intensity of an explosion by leveraging the accelerometer and pressure sensors in the smartphone. To do so, we emplaced a number of smartphones in the vicinity of real explosion blasts conducted at a university mining laboratory, where the material blasted was Dynamite with Ammonium Nitrate Fuel Oil (ANFO). We then collected the corresponding accelerometer and pressure readings sensed by the phone. We extracted a number of novel features, and designed a machine learning based algorithmic framework for ranging and estimating the intensity of the explosion event. After an extensive validation, we find that the average-case error in ranging (i.e., estimating the distance to the source of the explosion event) and estimating the intensity of explosive material (in terms of its charge weight) are 8.24% and 7.37%, respectively. We also present perspectives on encoding our algorithm as a smartphone app, identify several critical challenges that will be encountered in real-time data processing of smartphone accelerometers and pressure sensors in the context of pervasive sensing of explosions, and also identify other practical issues like the diversity of smartphones. To the best of our knowledge, our work is pioneering in demonstrating the feasibility of using smartphones to analyze explosion events. We believe there are significant societal benefits emanating from our work.


Computer Science

Keywords and Phrases

Accelerometers; Artificial Intelligence; Data Handling; Education; Explosives; Learning Systems; Pressure Sensors; Range Finding; Seismographs; Seismology; Signal Encoding; Smartphones; Algorithmic Framework; Average Case Error; Critical Challenges; Explosive Materials; Participatory Sensing; Processing Capability; Real-Time Data Processing; Seismic Vibrations; Explosions; Machine Learning; Ranging

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Article - Journal

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