# Real-Time Machine Learning Enabled Low-Cost Magnetometer System

arXiv: https://arxiv.org/abs/2209.08065 · HTML: https://mlsanity.com/paper/2209.08065/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies](https://mlsanity.com/paper/2311.11452/) (2023)

## Abstract (arXiv)

Geomagnetically Induced Currents (GICs) are one of the most hazardous effects of space weather. The rate of change in ground horizontal magnetic component dBH/dt is used as a proxy measure for GIC. In order to monitor and predict dBH/dt, ground-based fluxgate magnetometers are used. However, baseline correction is crucial before such magnetometer data can be utilized. In this paper, a low-cost Machine Learning (ML) enabled magnetometer system has been implemented to perform realtime baseline correction of magnetometer data. The predicted geomagnetic components are then used to derive a forecast for dBH/dt. Two different ML models were deployed, and their real-time and offline prediction accuracy were examined. The localized peaks of the predicted dBH/dt are further validated using binary event analysis.