Hardware-Software system for crowdsourced monitoring and mapping of the geomagnetic field

This paper addresses the problem of conducting high-resolution geomagnetic surveys in urban environments using a crowdsourced approach based on mobile devices. Existing solutions, such as CrowdMag, are primarily focused on global data collection and do not provide capabilities for operational mapping of local technogenic anomalies. The question of the reliability of anomaly detection based on mass measurements performed by various non-professional devices under real-world conditions remains open. To solve these problems, a hardware-software system called Ground Monitoring has been developed. Its architecture is built on a three-tier model and includes a client mobile application for Android, a server part based on PostgreSQL/PostGIS, and a web-GIS with visualization and interpolation modules. The application records all three components of the magnetic induction vector at a frequency of up to 5 Hz, with a resolution of 0.25 μT and an error not exceeding ±5 %. Experimental validation was conducted in last August in the territory of Ufa with the participation of several hundred student volunteers. Over 50,000 spatially referenced measurements were collected, achieving a coverage density of up to 1500 points/km 2 . It was found that, despite the scatter of individual measurements, the average for the dataset differs from the reference observatory measurement by less than 0.15 %. Furthermore, it was revealed that 87 % of anomalies are localized near man-made objects. Statistical analysis also confirmed the high reliability of anomaly detection, and the results of spatial interpolation confirmed its robustness to noisy data. The obtained results confirmed the feasibility of using a crowdsourcing approach to solve geophysical mapping problems with high spatial detail and measurements consistent with reference observatory measurements. The developed system can serve as an experimental foundation for urban environment monitoring systems. Future work prospects involve integrating this approach into citizen science projects.