Schumann resonance data processing programs and four-year measurements from Sierra Nevada ELF station

• Published in: Computers & geosciences Vol. 165; p. 105148
• Main Authors: Salinas, A., Rodríguez-Camacho, J., Portí, J., Carrión, M.C., Fornieles-Callejón, J., Toledo-Redondo, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2022
• Subjects: algorithms; computer software; Data processing; electric potential difference; electromagnetic field; ELF station; mountains; Schumann resonances; Spain; Spain; Schumann resonances; Data processing; ELF station
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2022.105148

In this work, we present to the scientific community the measurements taken during four years, from March 2013 to February 2017 inclusive, by the Extremely Low Frequency Sierra Nevada station, Spain, together with the data processing programs developed in Python (version 3.8) to extract the Schumann resonance (SR) parameters (i.e., amplitudes, resonant frequencies, resonance widths) in 10 min time periods from these records. The measurements correspond to the voltage induced by the atmospheric electromagnetic field at the north-south and east-west oriented magnetometers of the station. The process comprises four stages. The spectrum, calibrated in the frequency band ranging from 6 Hz to 25 Hz, is obtained at the first stage using the Welch method with Hann windows. The second step eliminates the anthropogenic noise generated by different undesired sources. Next, a non-linear fit of the measured spectrum combining Lorentzian functions together with a linear term is carried out in order to identify the presence of SRs and quantitatively characterize them. This third step is carried out using the Python package Lmfit, which implements the Levenberg-Marquad algorithm. Finally, a compact and easy-to-read output is generated at the fourth stage, using the power of the Numpy arrays and the npz format. In addition, four Jupyter notebooks with the description of the code and the possibility of their use in interactive mode are presented as supplementary material with this paper. •A four-year dataset of raw and processed measurements from the Sierra Nevada ELF station is made public.•The data processing programs, written in Python, that obtain Schumann resonance parameters from ELF measurements are shown.•Details of the code are given using Jupyter notebooks as supplementary material of the program code.