基于稀疏GNSS网络的近实时模拟7.8和7.5 Mw kahramanmaraku地震序列电离层扰动探测

IF 4.5 1区 地球科学 Q1 REMOTE SENSING
GPS Solutions Pub Date : 2025-01-01 Epub Date: 2025-01-18 DOI:10.1007/s10291-024-01808-2
F Luhrmann, J Park, W-K Wong, L Martire, S Krishnamoorthy, A Komjáthy
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引用次数: 0

摘要

2023年2月6日,kahramanmaraku地震序列在叙利亚中南部和西北部造成了严重的地面震动和灾难性的损失。这些地震事件产生的电离层扰动可在全球导航卫星系统(GNSS)总电子含量(TEC)测量中检测到。这项工作旨在开发并将一种近实时(NRT)电离层干扰检测方法纳入JPL的GUARDIAN系统。我们的方法使用长短期记忆(LSTM)神经网络来检测电离层异常行为,例如同震电离层扰动等。我们的方法在UTC时间10:24:48(当地时间13:24)第二次mw7.5地震后检测到异常信号,但在UTC时间01:17:34(当地时间04:17)第一次mw7.8地震后没有发出警报,这可能是由于夜间电离水平较低以及滑移的多源机制造成的,因此具有较小幅度的可见扰动。地震活动,包括2023年2月6日在基耶共和国发生的破坏性kahramanmaraki地震序列,导致地面垂直位移,引起大气波。这些波向上传播到外层大气,干扰电离层的电子含量。这种干扰影响由定位卫星(如GPS)广播并由地面接收器接收的信号。如果接收器的位置是已知的,对这些信号的影响可以用来测量这些地震引起的大气波引起的电子密度扰动。这类研究通常依赖于对事件的先验认识。使用深度学习神经网络,我们的目标是自动检测异常信号。我们建议利用这种方法在大的地理区域内探测地震引起的扰动。本文提出的探测方法成功地探测到了kahramanmaraki地震序列中第二次地震后约10分钟的电离层异常事件。补充信息:在线版本包含补充资料,提供地址为10.1007/s10291-024-01808-2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence.

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 M w  7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first M w  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.

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来源期刊
GPS Solutions
GPS Solutions 工程技术-遥感
CiteScore
8.10
自引率
12.20%
发文量
138
审稿时长
3.1 months
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.
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