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State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 11852

Special Issue Editors


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Guest Editor
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
Interests: seismo-electromagnetics; electromagnetic satellite; plasma physics; radio wave propagation; seismo-ionospheric physics
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Guest Editor
School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: magnetic field; GNSS system; LAI coupling; seismo-anomalies; geophysics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Interests: seismic; electromagnetics; numerical modeling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Graduate School of Science; Center for Environmental Remote Sensing; Chiba University, Chiba 263-8522, Japan
Interests: seismo-electromagnetics; geophysics; ionospheric disturbances; tectonics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many events can help to induce geomagnetic disturbances such as solar activity, magnetic storm, volcano eruption, and earthquakes. As a major physical property of the Earth, the geomagnetic field is also a direct medium which acts to connect the lithosphere, atmosphere, ionosphere, and magnetosphere. Partial geomagnetic disturbances cause malfunctions in global satellite navigation systems, radar systems, and communication systems. In recent years, with global coverage by dedicated satellites such as the Swarm constellation and China Seismo-Electromagnetic Satellite (CSES), the scientific community has achieved long-term data integration. This provides an important way for researchers to study electromagnetic monitoring and its near-Earth space dynamics on different time scales and illustrate the coupling processes among different geospheres during the significant events of outer space and the Earth. Artificial intelligence technologies such as computer vision and deep learning can also be combined in order to evaluate the reliability and accuracy of abnormal signals and to distinguish and improve prediction efficiency for natural hazards.

It is our pleasure to announce the launch of a new Special Issue of Remote Sensing. The goal in doing so is to gather research contributions related to ground-based and space-borne geomagnetic and electromagnetic observations in order to deepen our understanding of global geomagnetic events, space plasma physics, the lithosphere–atmosphere–ionosphere coupling mechanism, and the core dynamics of the lithospheric and ionospheric magnetic fields.

Prof. Dr. Xuemin Zhang
Prof. Dr. Chieh-Hung Chen
Prof. Dr. Yongxin Gao
Prof. Dr. Katsumi Hattori
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electromagnetic satellite
  • geomagnetic field disturbances
  • ionospheric perturbations
  • space weather
  • natural hazards
  • lithosphere–atmosphere–ionosphere coupling model
  • artificial intelligence technology

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Related Special Issue

Published Papers (10 papers)

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Research

28 pages, 9942 KiB  
Article
Spherical Magnetic Vector Forwarding of Isoparametric DGGS Cells with Natural Superconvergent Points
by Peng Chen, Shujin Cao, Guangyin Lu, Dongxin Zhang, Xinyue Chen and Zhiming Chen
Remote Sens. 2024, 16(18), 3448; https://doi.org/10.3390/rs16183448 - 17 Sep 2024
Viewed by 432
Abstract
With the rapid advancement of satellite remote sensing technology, many scientists and organizations, including NASA, ESA, NAOC, and Roscosmos, observe and study significant changes in the geomagnetic field, which has greatly promoted research on the geomagnetic field and made it an important research [...] Read more.
With the rapid advancement of satellite remote sensing technology, many scientists and organizations, including NASA, ESA, NAOC, and Roscosmos, observe and study significant changes in the geomagnetic field, which has greatly promoted research on the geomagnetic field and made it an important research direction in Earth system science. In traditional geomagnetic field research, tesseroid cells face degradation issues in high-latitude regions and accuracy limitations. To overcome these limitations, this paper introduces the Discrete Global Grid System (DGGS) to construct a geophysical model, achieving seamless global coverage through multi-level grid subdivision, significantly enhancing the processing capability of multi-source and multi-temporal spatial data. Addressing the challenges of the lack of analytical solutions and clear integration limits for DGGS cells, a method for constructing shape functions of arbitrary isoparametric elements is proposed based on the principle of isoparametric transformation, and the shape functions of isoparametric DGGS cells are successfully derived. In magnetic vector forwarding, considering the potential error amplification caused by Poisson’s formula, the DGGS grid is divided into six regular triangular sub-units. The triangular superconvergent point technique is adopted, and the positions of integration points and their weight coefficients are accurately determined according to symmetry rules, thereby significantly improving the calculation accuracy without increasing the computational complexity. Finally, through the forward modeling algorithm based on tiny tesseroid cells, this study comprehensively compares and analyzes the computational accuracy of the DGGS-based magnetic vector forwarding algorithm, verifying the effectiveness and superiority of the proposed method and providing new theoretical support and technical means for geophysical research. Full article
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20 pages, 22245 KiB  
Article
A Comparative Study on Multi-Parameter Ionospheric Disturbances Associated with the 2015 Mw 7.5 and 2023 Mw 6.3 Earthquakes in Afghanistan
by Rabia Rasheed, Biyan Chen, Dingyi Wu and Lixin Wu
Remote Sens. 2024, 16(11), 1839; https://doi.org/10.3390/rs16111839 - 22 May 2024
Cited by 1 | Viewed by 788
Abstract
This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various [...] Read more.
This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions. Full article
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20 pages, 11542 KiB  
Article
Detection Method and Application of Nuclear-Shaped Anomaly Areas in Spatial Electric Field Power Spectrum Images
by Xingsu Li, Zhong Li, Jianping Huang, Xuming Yang, Wenjing Li, Yumeng Huo, Junjie Song and Ruiqi Yang
Remote Sens. 2024, 16(4), 726; https://doi.org/10.3390/rs16040726 - 19 Feb 2024
Cited by 1 | Viewed by 939
Abstract
It is found that there are some anomalous high-energy nuclear-shaped regions in the VLF frequency band of the space electric field. To detect and statistically analyze these nuclear-shaped anomaly areas, this paper proposes a nuclear-shaped anomaly area detection method based on the electric [...] Read more.
It is found that there are some anomalous high-energy nuclear-shaped regions in the VLF frequency band of the space electric field. To detect and statistically analyze these nuclear-shaped anomaly areas, this paper proposes a nuclear-shaped anomaly area detection method based on the electric field power spectrum image data of the China Seismo Electromagnetic Satellite (CSES-01). First, the logarithm of VLF frequency band data was calculated and rotated counterclockwise to create power spectrum images and label them to form a sample image dataset; then, images were enhanced (which involved resizing, scaling, rotation, gaussian denoising, etc.) to solve the problems of the model overfitting and sample imbalance. Finally, the U-net network model based on the ResNet50 encoder was trained to obtain the optimal kernel anomaly detection model ResNet50_Unet. Comparative experiments with various semantic segmentation algorithms show that the ResNet50_Unet model has the best performance. Applying this model to detect the electric field power spectrum images from November 2021 to February 2022, a total of 101 nuclear-shaped anomaly areas were found, distributed between 45° and 70° of the north–south latitude. This model can quickly detect nuclear-shaped anomaly regions from massive data, providing reference significance for the detection of other types of ionospheric spatial disturbances. At the same time, it has important scientific significance and practical value for understanding the ionosphere and space communication. Full article
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19 pages, 7877 KiB  
Article
Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake
by Syed Faizan Haider, Munawar Shah, Bofeng Li, Punyawi Jamjareegulgarn, José Francisco de Oliveira-Júnior and Changyu Zhou
Remote Sens. 2024, 16(2), 222; https://doi.org/10.3390/rs16020222 - 5 Jan 2024
Cited by 9 | Viewed by 1300
Abstract
Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex [...] Read more.
Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon. Full article
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14 pages, 2715 KiB  
Article
Automatic Identification and Statistical Analysis of Data Steps in Electric Field Measurements from CSES-01 Satellite
by Jianping Huang, Zongyu Li, Zhong Li, Wenjing Li, Livio Conti, Hengxin Lu, Na Zhou, Ying Han, Haijun Liu, Xinfang Chen, Zhaoyang Chen, Junjie Song and Xuhui Shen
Remote Sens. 2023, 15(24), 5745; https://doi.org/10.3390/rs15245745 - 15 Dec 2023
Viewed by 895
Abstract
The spaceborne Electric Field Detector (EFD) is one of the payloads of the China Seismo-Electromagnetic Satellite (CSES-01), which can measure electric field data at near-Earth orbit for investigating fundamental scientific topics such as the dynamics of the top-side ionosphere, lithosphere–atmosphere–ionosphere coupling, and electromagnetic [...] Read more.
The spaceborne Electric Field Detector (EFD) is one of the payloads of the China Seismo-Electromagnetic Satellite (CSES-01), which can measure electric field data at near-Earth orbit for investigating fundamental scientific topics such as the dynamics of the top-side ionosphere, lithosphere–atmosphere–ionosphere coupling, and electromagnetic field emissions possibly associated with earthquake occurrence. The Extremely Low-Frequency (ELF) waveform shows anomalous step variations, and this work proposes an automatic detection algorithm to identify steps and analyze their characteristics using a convolutional neural network. The experimental results show that the developed detection method is effective, and the identification performance reaches over 90% in terms of both accuracy and area under the curve index. We also analyze the rate of the occurrence of steps in the three components of the electric field. Finally, we discuss the stability of the statistical results on steps and their relevance to the probe’s function. The research results provide a guideline for improving the quality of EFD data, and further applications in monitoring the low-Earth electromagnetic environment. Full article
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19 pages, 6970 KiB  
Article
A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content
by Hakan Uyanık, Erman Şentürk, Muhammed Halil Akpınar, Salih T. A. Ozcelik, Mehmet Kokum, Mohamed Freeshah and Abdulkadir Sengur
Remote Sens. 2023, 15(24), 5690; https://doi.org/10.3390/rs15245690 - 11 Dec 2023
Cited by 6 | Viewed by 1418
Abstract
Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large [...] Read more.
Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large earthquakes (e.g., the ionospheric Total Electron Content (TEC)). The TEC is an important parameter used to detect pre-earthquake changes by measuring ionospheric disturbances and space weather indices, such as the global geomagnetic index (Kp), the storm duration distribution (Dst), the sunspot number (R), the geomagnetic storm index (Ap-index), the solar wind speed (Vsw), and the solar activity index (F10.7), have also been used to detect pre-earthquake ionospheric changes. In this study, the feasibility of the 6th-day earthquake prediction by the deep neural network technique using the previous five consecutive days is investigated. For this purpose, a two-staged approach is developed. In the first stage, various preprocessing steps, namely TEC signal improvement and time-frequency representation-based TEC image construction, are performed. In the second stage, a multi-input convolutional neural network (CNN) model is designed and trained in an end-to-end fashion. This multi-input CNN model has a total of six inputs, and five of the inputs are designed as 2D and the sixth is a 1D vector. The 2D inputs to the multi-input CNN model are TEC images and the vector input is concatenated space weather indices. The network branches with the 2D inputs contain convolution, batch normalization, and Rectified Linear Unit (ReLU) activation layers, and the branch with the 1D input contains a ReLU activation layer. The ReLU activation outputs of all the branches are flattened and then concatenated. And the classification is performed via fully connected, softmax, and classification layers, respectively. In the experimental work, earthquakes with a magnitude of Mw5.0 and above that occurred in Turkey between 2012 and 2019 are used as the dataset. The TEC data were recorded by the Turkey National Permanent GNSS Network-Active (TNPGN-Active) Global Navigation Satellite System (GNSS) stations. The TEC data five days before the earthquake were marked as “precursor days” and the TEC data five days after the earthquake were marked as “normal days”. In total, 75% of the dataset is used to train the proposed method and 25% of the dataset is used for testing. The classification accuracy, sensitivity, specificity, and F1-score values are obtained for performance evaluations. The results are promising, and an 89.31% classification accuracy is obtained. Full article
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37 pages, 6476 KiB  
Article
The Correlation between Ionospheric Electron Density Variations Derived from Swarm Satellite Observations and Seismic Activity at the Australian–Pacific Tectonic Plate Boundary
by Wojciech Jarmołowski, Paweł Wielgosz, Manuel Hernández-Pajares, Heng Yang, Beata Milanowska, Anna Krypiak-Gregorczyk, Enric Monte-Moreno, Alberto García-Rigo, Victoria Graffigna and Roger Haagmans
Remote Sens. 2023, 15(23), 5557; https://doi.org/10.3390/rs15235557 - 29 Nov 2023
Cited by 1 | Viewed by 1066
Abstract
Swarm electron density (Ne) observations from the Langmuir probe (LP) can detect ionospheric disturbances at the altitude of a satellite. Along-track satellite observations provide a large number of very short observations of different places in the ionosphere, where Ne is disturbed. Moreover, different [...] Read more.
Swarm electron density (Ne) observations from the Langmuir probe (LP) can detect ionospheric disturbances at the altitude of a satellite. Along-track satellite observations provide a large number of very short observations of different places in the ionosphere, where Ne is disturbed. Moreover, different perturbations occupy various Ne signal frequencies. Therefore, such short signals are more recognizable in two dimensions, where aside from their change in time, we can observe their diversity in the frequency domain. Spectral analysis is an essential tool applied here, as it enables signal decomposition and the recognition of composite patterns of Ne disturbances that occupy different frequencies. This study shows a high-resolution application of short-term Fourier transform (STFT) to Swarm Ne observations in the Papua New Guinea region in the vicinity of earthquakes, tsunamis, and related general seismic activity. The system of tectonic plate junctions, including the Pacific–Australian boundary, is located orthogonally to Swarm track footprints. The selected wavelengths of seismically induced ionospheric disturbances detected via Swarm are compared with the three sets of three-month records of seismic activity: in the winter solstice of 2016/2017, when seismic activity was highest, and in the summer solstice and vernal equinox of 2016, which were calmer. Moreover, more Swarm data records are analyzed at the same latitudes for validation purposes, in a place where there are no tectonic plate boundaries that are orthogonal to the Swarm orbital footprint. Additional validation is supplied through Swarm Ne observations from completely different latitudes, where the Swarm orbital footprint orthogonally crosses a different subducting plate boundary. Aside from the seismic energy, the solar radio flux (F10.7), equatorial plasma bubbles (EPBs), and geomagnetic ap and Dst indices are also reviewed here. Their influence on the ionospheric Ne is also found in Swarm observations. Finally, the Pearson correlation coefficient (PCC), applied to the pairs of 3-month time series created from Swarm Ne variations, seismic energy, ap, Dst, and F10.7, summarizes the graphical inspection of mutual correlations. It points to the predominant correlation of Swarm Ne disturbances with seismicity, especially during nighttime. We show that most of the Ne disturbances at a selected wavelength of 300 km correlate more with seismicity than with geomagnetic and solar indices. Therefore, Swarm LP can be assessed as being capable of observing the lithosphere–atmosphere–ionosphere coupling (LAIC) from the orbit. Full article
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20 pages, 3988 KiB  
Article
A Multi-Parameter Empirical Fusion Model for Ionospheric TEC in China’s Region
by Jianghe Chen, Pan Xiong, Haochen Wu, Xuemin Zhang, Jiandi Feng and Ting Zhang
Remote Sens. 2023, 15(23), 5445; https://doi.org/10.3390/rs15235445 - 21 Nov 2023
Viewed by 1427
Abstract
This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model’s performance is enhanced by considering solar flux and geomagnetic activity [...] Read more.
This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model’s performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models. Full article
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16 pages, 6094 KiB  
Communication
Horizontal Magnetic Anomaly Accompanying the Co-Seismic Earthquake Light of the M7.3 Fukushima Earthquake of 16 March 2022: Phenomenon and Mechanism
by Busheng Xie, Lixin Wu, Wenfei Mao, Ziqing Wang, Licheng Sun and Youyou Xu
Remote Sens. 2023, 15(20), 5052; https://doi.org/10.3390/rs15205052 - 21 Oct 2023
Cited by 1 | Viewed by 1201
Abstract
A horizontal magnetic disturbance accompanying the co-seismic earthquake light (EQL) of the M7.3 Fukushima earthquake of 16 March 2022 was detected by a fluxgate magnetometer installed at the KAK station, which is 270 km south of the EQL and 210 km west of [...] Read more.
A horizontal magnetic disturbance accompanying the co-seismic earthquake light (EQL) of the M7.3 Fukushima earthquake of 16 March 2022 was detected by a fluxgate magnetometer installed at the KAK station, which is 270 km south of the EQL and 210 km west of the epicenter. The instantaneous change of the declination component of the geomagnetic field reached about 1.7″, much exceeding the threshold of three-fold error (0.72″). Considering the direction information of the geomagnetic data, the horizontal magnetic disturbance vector was further analyzed, which manifested the normal of the horizontal magnetic disturbance vector passing through the position of the EQL. Combined with the experimental results of pressure-simulated rock current (PSRC), the mechanism of the EQL and the geomagnetic anomaly was proposed to interpret the spatiotemporal correlation between the EQL and the horizontal magnetic disturbance vector, which should be a manifest of the induced magnetic horizontal vector (IMHV), attributed to the upward seismic PSRC. Different from previous precursor studies on geomagnetic disturbance on the power spectrum, vertical component, or polarization, this paper focuses on the direction information of the horizontal magnetic disturbance vector, which could be further applied to locate potential seismogenic zones based on the IMHVs observed by multiple geomagnetic stations. Full article
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20 pages, 4096 KiB  
Article
The Analysis of Lithosphere–Atmosphere–Ionosphere Coupling Associated with the 2022 Luding Ms6.8 Earthquake
by Jiang Liu, Xuemin Zhang, Xianhe Yang, Muping Yang, Tiebao Zhang, Zhicheng Bao, Weiwei Wu, Guilan Qiu, Xing Yang and Qian Lu
Remote Sens. 2023, 15(16), 4042; https://doi.org/10.3390/rs15164042 - 16 Aug 2023
Cited by 8 | Viewed by 1327
Abstract
Taking the Luding Ms6.8 earthquake (EQ) on 5 September 2022 as a case study, we investigated the potential seismic anomalies of the ionosphere, infrared radiation, atmospheric electrostatic field (AEF), and hot spring ions in the seismogenic region. Firstly, we analyzed the multi-parameter anomalies [...] Read more.
Taking the Luding Ms6.8 earthquake (EQ) on 5 September 2022 as a case study, we investigated the potential seismic anomalies of the ionosphere, infrared radiation, atmospheric electrostatic field (AEF), and hot spring ions in the seismogenic region. Firstly, we analyzed the multi-parameter anomalies in the ionosphere around the epicenter and found synchronous anomalous disturbances in the ground parameters, namely the global ionospheric map (GIM), GPS, TEC, and satellite parameters, such as the He+ and O+ densities on 26 August under relatively quiet solar–geomagnetic conditions (F10.7 < 120 SFU; Kp < 3; Dst > −30 nT; |AE| < 500 nT). Next, both the anomaly analysis of the infrared radiation and AEF, and the survey results of the Luding EQ scientific expedition on the hot spring ions showed pre-seismic anomalous variations at different time periods in the seismogenic region. The characteristics of Earth’s multi-sphere coupling anomalies in temporal evolution and spatial distribution were obvious, which validated the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism. Finally, combining the analysis results and the LAIC mechanism, we suggested that the multi-sphere coupling anomalies were more likely associated with the Luding Ms6.8 EQ, and that the differential motion and the regional crustal stress accumulation between the Chuandian block and the Bayan Har block might have led to this EQ. Furthermore, remote sensing and ground-based monitoring technologies can play an important role in corroborating and compensating each other, while further study of the multi-sphere coupling mechanism will provide a clearer understanding of the seismogenic process for major EQs. Full article
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