Segmentation and characteristic extraction for Schumann Resonance transient events

In this article we propose a novel methodology for obtaining Schumann Resonances’ relevant parameters from ELF transient register. Using this methodology, it is possible to extract a large amount of data and characterize individual transient events and their more relevant features. To use this methodology a new narrow band sensor is presented, centered in the 1st Schumann Resonance mode and specialized in capturing with high precision the associated transient events. The new methodology based on Hilbert transform and Heidler function is presented and used to segment and characterize each transient event. This method is validated first with an automatic classifier algorithm and then an extensive statistical analysis is performed. The validation process is shown as one of the possible applications of the methodology. The introduced set of narrow band hardware and software tools represents an important milestone for the study of transient events focused on a high amount of data.

• Segmentation and Feature extraction of ELF transitory events under novel methodology.
• Developed Methodology based on Hilbert envelope and Heidler lightning fit.
• Schumann Resonances’ ELF narrow band sensor presented, centered in first mode.
• Time domain study of Schumann Resonances’ first mode from a unique narrow band perspective.
• Validation based on statistical analysis and clustering, related to unsupervised learning.