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

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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: 30 June 2024 | Viewed by 7092

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Prof. Dr. Xuemin Zhang

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Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
Interests: seismo-electromagnetics; electromagnetic satellite; plasma physics; radio wave propagation; seismo-ionospheric physics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chieh-Hung Chen

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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

Prof. Dr. Yongxin Gao

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School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Interests: seismo-electromagnetics; geophysics; ionospheric disturbances; acoustic-gravity wave; numerical modelling

Prof. Dr. Katsumi Hattori

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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 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

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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

Published Papers (8 papers)

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Research

20 pages, 11542 KiB

Open AccessArticle

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

Viewed by 485

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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19 pages, 7877 KiB

Open AccessArticle

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 - 05 Jan 2024

Cited by 3 | Viewed by 798

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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14 pages, 2715 KiB

Open AccessArticle

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 605

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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19 pages, 6970 KiB

Open AccessArticle

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 2 | Viewed by 973

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 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 M_w5.0 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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37 pages, 6476 KiB

Open AccessArticle

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

Viewed by 673

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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20 pages, 3988 KiB

Open AccessArticle

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 1065

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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16 pages, 6094 KiB

Open AccessCommunication

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

Viewed by 804

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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20 pages, 4096 KiB

Open AccessArticle

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 3 | Viewed by 972

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 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

(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)

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