Castellanos-Velazco, C. I.Castro, A.2026-04-202026-04-202024-10-01Castellanos-Velazco, C. I., & Castro, A. (2025). Determining a local quiet day with a machine learning model approach. En L. Benyosef (Ed.),==$Proceedings of the XXth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing$==. Observatório Nacional. https://doi.org/10.29327/1737054https://hdl.handle.net/20.500.12816/5826Quiet Days (QDs) play a crucial role in modeling diurnal variations and removing their contribution to compute regional geomagnetic indices. This project aims to develop a local QD identification process by adapting van de Kamp's criteria for selecting Local Quiet Days (LQDs). However, empirical testing has revealed that days classified as LQDs are not always truly quiet, as they may lack the magnetic signatures associated with diurnal variation. To address this limitation, we propose an automatic, unsupervised machine learning method that combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs). This approach seeks to enhance the detection of truly LQDs within specified time windows, ensuring more accurate identification and analysis.application/pdfenghttp://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-nd/4.0/Local Quiet DayMachine LearningMagnetometershttp://purl.org/coar/resource_type/R60J-J5BDhttps://purl.org/pe-repo/ocde/ford#1.05.04Procedding of the XXth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processinghttps://doi.org/10.29327/1737054.20-27