F Luhrmann, J Park, W-K Wong, L Martire, S Krishnamoorthy, A Komjáthy
{"title":"Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence.","authors":"F Luhrmann, J Park, W-K Wong, L Martire, S Krishnamoorthy, A Komjáthy","doi":"10.1007/s10291-024-01808-2","DOIUrl":null,"url":null,"abstract":"<p><p>On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL's GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second <math><msub><mi>M</mi> <mi>w</mi></msub> </math> 7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first <math><msub><mi>M</mi> <mi>w</mi></msub> </math> 7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip. <i>Plain Language Summary</i> Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s10291-024-01808-2.</p>","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"29 1","pages":"54"},"PeriodicalIF":4.5000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839748/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GPS Solutions","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10291-024-01808-2","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/18 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL's GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second 7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first 7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip. Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.
Supplementary information: The online version contains supplementary material available at 10.1007/s10291-024-01808-2.
期刊介绍:
GPS Solutions is a scientific journal. It is published quarterly and features system design issues and a full range of current and emerging applications of global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, BeiDou, local systems, and augmentations. Novel, innovative, or highly demanding uses are of prime interest. Areas of application include: aviation, surveying and mapping, forestry and agriculture, maritime and waterway navigation, public transportation, time and frequency comparisons and dissemination, space and satellite operations, law enforcement and public safety, communications, meteorology and atmospheric science, geosciences, monitoring global change, technology and engineering, GIS, geodesy, and others.
GPS Solutions addresses the latest developments in GNSS infrastructure, mathematical modeling, algorithmic developments and data analysis, user hardware, and general issues that impact the user community. Contributions from the entire spectrum of GNSS professionals are represented, including university researchers, scientists from government laboratories, receiver industry and other commercial developers, public officials, and business leaders.