[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126417-en":3,"doc-seo-126417-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126417,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Toward Automatic Detection of Pi2 Magnetic Pulsation Using Machine Learning","Magnetic pulsations of type Pi2 are an established category of ultra-low-frequency (ULF) waves, showing irregularly damped oscillations with periods of 40 to 150 seconds and frequencies around 6.7–25 mHz. Pi2 appears at the onset of geomagnetic substorms and links ionospheric and magnetospheric dynamics. With the discontinuation of a conventional index for Pi2 detection, the study proposes a machine-learning framework using geomagnetic field data, comparing linear, ensemble, and non-linear models and selecting the best via hyperparameter optimization.","Received 11 May 2025, accepted 22 June 2025, date of publication 24 June 2025, date of current version 1 July 2025. Digital Object Identifier 10.1109/ACCESS.2025.3582762  \nToward Automatic Detection of Pi2 Magnetic Pulsation Using Machine Learning  \nMOHAMED S. ABDALZAHER1,2,(Senior Member, IEEE), ESSAM GHAMRY3, KHAIRUL ADIB YUSOF4, AND MOSTAFA SHAABAN1,5,(Senior Member, IEEE)  \n1Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates  \n2Department of Seismology, National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, Egypt  \n3Department of Geomagnetic and Geoelectric, National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, Egypt  \n4Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Malaysia  \n5Energy, Water, and Sustainable Environment Research Center, College of Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates Corresponding author: Mohamed S. Abdalzaher ([msalah@aus.edu](msalah@aus.edu); [msabdalzaher@nriag.sci.eg](msabdalzaher@nriag.sci.eg))  \nThe work in this paper was supported, in part, by the open access program and project FRG24-C-E66 from the American University of Sharjah. This paper represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.  \nABSTRACT Magnetic pulsations of type Pi2 are a well-established category of Ultra Low Frequency (ULF) waves, characterized by irregularly damped oscillations with periods ranging from 40 to 150 seconds (6.7–25 mHz) . Nowadays, it is well known that Pi2 occurs at the onset of geomagnetic substorms, is considered an outstanding research topic in space physics, and is a link between ionospheric and magnetospheric processes. The discontinuation of the conventional index previously employed to detect Pi2 pulsations has driven this study to propose an innovative detection method leveraging machine learning (ML) . This paper introducesa novel ML-based classification framework that utilizes geomagnetic field data for Pi2 pulsation detection. A comprehensive analysis of various linear, ensemble, and non-linear ML models was conducted, employing hyperparameter optimization to identify the optimal model with high classification performance and minimal computational overhead during testing. Model robustness was assessed using multiple evaluation metrics, including accuracy, F1-score, kappa score, execution time, precision-recall curves, ROC curves, learning curves, and confusion matrices. The proposed gradient boost (GB) classifier demonstrated superior performance, achieving 98.21% accuracy in distinguishing Pi2 pulsations. This detection system offers a reliable and efficient tool for monitoring Pi2 pulsations in the nighttime, contributing to advancements in space weather analysis and substorm detection.  \nINDEX TERMS Machine learning, Pi2 pulsations, geomagnetic field, automatic detection, geophysical data classification.  \nI. INTRODUCTION  \nOne of the most interesting space weather phenomena is the geomagnetic substorm, which is considered a complex phenomenon due to enhanced plasma convection from the tail towards the Earth [1] . It lasts a few hours and can be divided into three distinct phases: a growth phase, which lasts typically about an hour and usually occurs when IMF has a southward component, in which energy is delivered from the solar wind to be stored in the night side tail of the magnetosphere; an expansion stage, lasts typically few minutes. Accordingly, the stored energy is  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .  \nexplosively released as a recovery phase that usually takes several hours, and the magnetosphere relaxes to a quiet state [2], [3] . Their occurrence triggers various phenomena, including auroral breakups, the release of energetic particles into th","cbCaim7MWl95g6N1","https://ap.wps.com/l/cbCaim7MWl95g6N1","pdf",1140229,3,1,12,"English","en",105,"# Introduction\n## Substorms and Pi2 pulsations\n## Limitations of conventional detection indices\n## Motivation for machine-learning detection","[{\"question\":\"What are Pi2 magnetic pulsations and why are they important?\",\"answer\":\"Pi2 magnetic pulsations are irregularly damped ULF wave oscillations with periods of about 40–150 seconds. They are associated with the onset of geomagnetic substorms and reflect coupling between ionospheric and magnetospheric processes.\"},{\"question\":\"Why is a new detection method needed?\",\"answer\":\"A previously used conventional index for detecting Pi2 pulsations was discontinued for public use at the end of 2019. This creates the need for alternative approaches.\"},{\"question\":\"How does the proposed system detect Pi2 pulsations?\",\"answer\":\"The work introduces a machine-learning classification framework that uses geomagnetic field data. It evaluates multiple linear, ensemble, and non-linear models with hyperparameter optimization, and the gradient boost classifier achieves the best reported performance for distinguishing Pi2.\"}]","Toward Automatic Detection of Pi2 Magnetic Pulsation Using Machine Learning | PDF",1785904947,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"toward-automatic-detection-of-pi2-magnetic-pulsation-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/toward-automatic-detection-of-pi2-magnetic-pulsation-using-machine-learning/126417/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What are Pi2 magnetic pulsations and why are they important?","Question",{"text":76,"@type":77},"Pi2 magnetic pulsations are irregularly damped ULF wave oscillations with periods of about 40–150 seconds. They are associated with the onset of geomagnetic substorms and reflect coupling between ionospheric and magnetospheric processes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is a new detection method needed?",{"text":81,"@type":77},"A previously used conventional index for detecting Pi2 pulsations was discontinued for public use at the end of 2019. This creates the need for alternative approaches.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed system detect Pi2 pulsations?",{"text":85,"@type":77},"The work introduces a machine-learning classification framework that uses geomagnetic field data. It evaluates multiple linear, ensemble, and non-linear models with hyperparameter optimization, and the gradient boost classifier achieves the best reported performance for distinguishing Pi2.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]