Geomagnetic Field Data Research Articles

Coherent flow structures and magnetic field patterns in rotating spherical shell convective dynamos: A data-driven approach

The Earth's outer core dynamics involve convective fluid motion generating an observable geomagnetic field. The velocity and magnetic fields exhibit characteristic spatiotemporal features possessing geophysical significance for which extensive datasets are available from direct observations and computational simulations. This study demonstrates the robustness of proper orthogonal decomposition (POD), a data-driven technique, in detecting prominent and relevant features in these datasets. Improvising on previous practices, the POD efficiently detects infinitesimal instabilities at the onset of convection, providing an accurate and objective methodology to determine the convective threshold, even for heterogeneous buoyancy forcing. Time evolution of paired, phase-shifted modes efficiently reconstructs the azimuthally drifting of traveling wave instabilities. Simultaneously reduced order modeling of velocity components clearly distinguish the equatorial and polar coherent flow structures. Supercritical convection-driven magnetic field data over long periods, generated using numerical simulations, produce dominant modes that are more accurately representative of time-averaged patterns than geocentric axial dipole patterns. Moreover, the quantitative significance of the dominant modes determines the extent of dimensional reduction complementing established diagnostics for dipolarity. Finally, analysis of observational geomagnetic field data reveals long-lived dominant patterns influenced by thermal core–mantle interaction consistent with numerical models employing tomographic heat flux boundary conditions in present as well as previous studies.

A Comprehensive Classification and Analysis of Geomagnetic Storms Over Solar Cycle 24

A geomagnetic storm is a global disturbance of Earth's magnetosphere, occurring as a result of the interaction with magnetic plasma ejected from the Sun. Despite considerable research, a comprehensive classification of storms for a complete solar cycle has not yet been fully developed, as most previous studies have been limited to specific storm types. This study, therefore, attempted to present complete statistics for solar cycle 24, detailing the occurrence of geomagnetic storm events and classifying them by type of intensity (moderate, intense, and severe), type of complete interval (normal or complex), duration of the recovery phase (rapid or long), and the number of steps in the storm's development. The analysis was applied to data from ground-based magnetometers, which measured the Dst index as provided by the World Data Center for Geomagnetism, Kyoto, Japan. This study identified 211 storm events, comprising moderate (177 events), intense (33 events), and severe (1 event) types. About 36% of ICMEs and 23% of CIRs are found to be geoeffective, which caused geomagnetic storms. Up to four-step development of geomagnetic storms was exhibited during the main phase for this solar cycle. Analysis showed the geomagnetic storms developed one or more steps in the main phase, which were probably related to the driver that triggered the geomagnetic storms. A case study was additionally conducted to observe the variations of the ionospheric disturbance dynamo (Ddyn) phenomenon that resulted from the geomagnetic storm event of 2015 July 13. The attenuation of the Ddyn in the equatorial region was analyzed using the H component of geomagnetic field data from stations in the Asian sector (Malaysia and India). The variations in the Ddyn signatures were observed at both stations, with the TIR station (India) showing higher intensity than the LKW station (Malaysia).

On the Impact of Geospace Weather on the Occurrence of M7.8/M7.5 Earthquakes on 6 February 2023 (Turkey), Possibly Associated with the Geomagnetic Storm of 7 November 2022

A joint analysis of solar wind, geomagnetic field, and earthquake catalog data showed that before the catastrophic M = 7.8 and M = 7.5 Kahramanmaras earthquake sequence on 6 February 2023, a closed strong magnetic storm occurred on 7 November 2022, SYM/H = −117 nT. The storm started at 08:04 UT. At this time, the high-latitudinal part of Turkey’s longitudinal region of future epicenters was located under the polar cusp, where the solar wind plasma would directly access the Earth’s environment. The time delay between storm onset and earthquake occurrence was ~91 days. We analyzed all seven strong (M7+) earthquakes from 1967 to 2020 to verify the initial findings. A similar pattern has been revealed for all events. The time delay between magnetic storm onset and earthquake occurrence varies from days to months. To continue these investigations, a retrospective analysis of seismic and other geophysical parameters just after preceded geomagnetic storms in the epicenter areas is desirable.

Pengesanan Denyutan Pi2 Hampir Masa Nyata Berdasarkan Model Pembelajaran Mesin Terautomatik

The impact of geomagnetic substorms on space weather and various technology on the Earth urges the need to detect the phenomenon. However, the brief period of substorm occurrences makes it more difficult to directly detect them, therefore, their detection by proxy in the form of precursors need to be done. Pi2 pulsations, which is a damped irregular signal in the horizontal component of the geomagnetic field ( as a reliable precursor to substorms. The cessation of Hz), is seen index that previously was the main detection indicator of Pi2 pulsations motivates this study to introduce a new detection method by implementing machine learning. In this study, several features were extracted from geomagnetic field data based on several statistical parameters, impulsive and signal metrics to be used in the development of classification model. The development was performed based on the automated machine learning (AutoML) approach that determines the best algorithm and optimizes the hyperparameters automatically. AutoML tries various algorithms like ensemble, neural network, Naïve Bayes, support vector machine, k-nearest neighbour and binary decision tree that are optimized by Asynchronous Successive Halving Algorithm. An ensemble classification model was determined to be the best performing model with an accuracy of 99.15%. It was then used in the development of near-real tine detection system. The system streams the geomagnetic field data continuously and reports the Pi2 detections on an open cloud repository, The detection system is envisioned to be one of the reference sources to inquire the occurrences of Pi2 pulsations globally.

Prediction of high latitude nightside Geomagnetic Field Disturbances (GGFDs) during the 9-March-2012 geomagnetic storm using Recurrent Neural Network (RNN)

Geomagnetically Induced currents (GICs) pose a potentially catastrophic threat to the safety and security of large-scale power distribution systems. Using a recurrent neural network, we predict high (≥60°) magnetic latitude (MLAT) and night side (06–18) magnetic local time (MLT) geomagnetic field disturbances (surface dBs/dt and vertical dBz/dt), the trigger of GICs, during a geomagnetic storms occurred on March 09, 2012. We use the high latitude geomagnetic field data from SuperMAG and the solar wind particle and magnetic field data from NASA-OMNIWeb. We divide the high latitude night side of Earth’s globe into multiple sectors and obtain a time series of maximum geomagnetic disturbances observed in each sector. We use the Long Short Term Memory (LSTM) network technique sector by sector on both, the solar wind data and the sectored geomagnetic field disturbance (10° geomagnetic latitude, 1-hour MLT) time series obtained from SuperMAG. Each sector prediction is plotted on a polar map to reveal high latitude night side geomagnetic disturbance patterns. This high latitude nigh side model has great potential. With solar wind input at a Lagrangian-1 point, and geomagnetic field data from SuperMAG, it can predict the peak magnetic disturbance at any given sector for couple of hours in advance. Our machine learning techniques provide the best set of solar wind input that gives the best disturbance prediction. This paper presents a prototype of the global geomagnetic disturbance model with low-resolution spatial grids and a subset of potential solar wind input parameters. Once matured, the global magnetic disturbance model will be provided to the public.

An alternate representation of the geomagnetic core field obtained using machine learning

Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Prevailing methods currently used in inferring the characteristic properties and the probable time evolution of the geodynamo are mostly based on reduced representations of magnetohydrodynamics (MHD). This study introduces a new inference method, referred to as Current Loop-based UNet Model Segmentation Inference (CLUMSI). Its long-term goal focuses on uncovering concentrations of electric current densities inside the core as the direct sources of the magnetic field itself, rather than computing the fluid motion using MHD. CLUMSI relies on simplified models in which equivalent current loops represent electric current systems emerging in turbulent geodynamo simulations. Various configurations of such loop models are utilized to produce synthetic magnetic field and secular variation (SV) maps computed at the core–mantle boundary (CMB). The resulting maps are then presented as training samples to an image-processing neural network designed specifically for solving image segmentation problems. This network essentially learns to infer the parameters and configuration of the loops in each model based on the corresponding CMB maps. In addition, with the help of the Domain Adversarial Training of Neural Networks (DANN) method during training, historical geomagnetic field data could also be considered alongside the synthetic samples. This implementation can increase the likelihood that a network trained primarily on synthetic data will appropriately handle real inputs. Our results focus mainly on the method's feasibility when applied to synthetic data and the quality of these inferences. A single evaluation of the trained network can recover the overall distribution of loop parameters with reasonable accuracy. To better represent conditions in the outer core, the study also proposes a computationally feasible process to account for magnetic diffusion and the corresponding induced currents in the loop models. However, the quality of the reconstruction of magnetic field properties is compromised by occasional poor inferences, and an inability to recover realistic SV.Graphical

Feasibility of Principal Component Analysis for Multi-Class Earthquake Prediction Machine Learning Model Utilizing Geomagnetic Field Data

Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal component analyses (PCA) for reducing the dimensionality of global geomagnetic field data to improve the accuracy of EQ predictive models. Multi-class ML models capable of predicting EQ intensity in terms of the Mercalli Intensity Scale were developed. Ensemble and Support Vector Machine (SVM) models, known for their robustness and capabilities in handling complex relationships, were trained, while a Synthetic Minority Oversampling Technique (SMOTE) was employed to address the imbalanced EQ data. Both models were trained on PCA-extracted features from the balanced dataset, resulting in reasonable model performance. The ensemble model outperformed the SVM model in various aspects, including accuracy (77.50% vs. 75.88%), specificity (96.79% vs. 96.55%), F1-score (77.05% vs. 76.16%), and Matthew Correlation Coefficient (73.88% vs. 73.11%). These findings suggest the potential of a PCA-based ML model for more reliable EQ prediction.

Substorm‐Time Ground dB/dt Variations Controlled by Interplanetary Shock Impact Angles: A Statistical Study

AbstractIn this study, we investigate the effects caused by interplanetary (IP) shock impact angles on the subsequent ground dB/dt variations during substorms. IP shock impact angles have been revealed as a major factor controlling the subsequent geomagnetic activity, meaning that shocks with small inclinations with the Sun‐Earth line are more likely to trigger higher geomagnetic activity resulting from nearly symmetric magnetospheric compressions. Such field variations are linked to the generation of geomagnetically induced currents (GICs), which couple to artificial conductors on the ground leading to deleterious consequences. We use a sub‐set of a shock data base with 237 events observed in the solar wind at L1 upstream of the Earth, and large arrays of ground magnetometers at stations located in North America and Greenland. The spherical elementary current system methodology is applied to the geomagnetic field data, and field‐aligned‐like currents in the ionosphere are derived. Then, such currents are inverted back to the ground and dB/dt variations are computed. Geographic maps are built with these field variations as a function of shock impact angles. The main findings of this investigation are: (a) typical dB/dt variations (5–10 nT/s) are caused by shocks with moderate inclinations; (b) the more frontal the shock impact, the more intense and the more spatially defined the ionospheric current amplitudes; and (c) nearly frontal shocks trigger more intense dB/dt variations with larger equatorward latitudinal expansions. Therefore, the findings of this work provide new insights for GIC forecasting focusing on nearly frontal shock impacts on the magnetosphere.

Earthquake Prediction Model Based on Geomagnetic Field Data Using Automated Machine Learning

Earthquake Prediction Model Based on Geomagnetic Field Data Using Automated Machine Learning

Analysis of fractal properties of horizontal component of Earth’s magnetic field of different geomagnetic conditions using MFDFA

Depending on the orientation of the interplanetary magnetic field, solar disturbances induce perturbations in the geomagnetic field as observed by magnetometers on the ground. This study examines the signatures of fractal property of the geomagnetic field’s horizontal component (H time series), as measured by the ground-based magnetometer at Bangui [BNG] (4.33°N, 18.57°E). The Multifractal detrended fluctuation analysis (MF-DFA) method is employed in the study, as it can explore higher-dimensional fractal and multifractal characteristics hidden in the time series. By incorporating MF-DFA, we investigated the changes in the fractal properties of H time series under various geomagnetic conditions, and quantified using Hurst exponent (H) and generalized Hurst exponent h(q). A clear difference in the behaviour of H component during quiet, moderate and intense storm periods is revealed through the analysis of the scaling exponents for different levels of geomagnetic activity. The generalized Hurst exponent h(q) values are found to be higher during the quiet periods, compared to moderate and severe storms periods. It is also observed that both the storm periods and geomagnetically quiet periods are characterized by fractional Brownian motion. The strong q-dependence of h(q) and multifractal scaling exponent τq indicates strong multifractal characteristics of the geomagnetic field data. Thus, the present study reveals that the degree of multifractality is more for the time series of quiet period compared to the one belongs to storm period. Also our results show that the scaling properties of the H time series possess a long-range correlation, showing a long-term persistence of the previous state.

Imprint of Mesospheric Quasi 2‐Day Wave in the Ground Geomagnetic Field Variations at Low Latitudes

AbstractThe strong quasi 2‐day wave (Q2DW) event that occurred during January 2015 was studied by employing medium frequency and meteor radar observations of winds from three different locations at low latitudes with considerable longitudinal separation. The large‐amplitude wave activity that occurred in mid‐January is the focus of this study. Using the advantage offered by the longitudinal separation among the stations, the zonal structure of the Q2DW has been examined. The presence of a dominant westward propagating zonal wavenumber 3 (W3) mode was revealed in the analysis. The availability of geomagnetic field data from a number of stations at low latitudes enabled us to observe the wave event at ionospheric dynamo region heights. The phase‐longitude cross‐section of the quasi 2‐day oscillation in the Y component of the geomagnetic field reiterates the presence of the dominant W3 mode observed at lower altitudes. Similar zonal characteristics and periods of the wave signatures found at mesospheric altitudes and in the manifestation of quiet‐time ionospheric currents in geomagnetic field are in conformity with the notion of the wave coupling of the atmosphere‐ionosphere system.

Preliminary Modelling of Solar Quiet Geomagnetic Field Average using Non-Linear Autoregressive with Exogeneous Input (NARX)

This paper discusses geomagnetic field attempt modelling using an Artificial Neural Network (ANN). The local horizontal component of geomagnetic field data was collected on April 2011 (equinox) during a solar quiet day at recent solar cycle inclination-24 using the Magnetic Data Acquisition System (MAGDAS) in Langkawi, Malaysia, in the low latitude region. The calculated average values (mean) of the H component geomagnetic field variation during Equinox 2011 characterised the dominant geomagnetic field during that particular solar cycle. The difference in amplitude of maximum and minimum values shows a regular diurnal variation of the geomagnetic field during Sq inthe low latitude region. The output training utilised these calculated mean values during the modelling attempt. Meanwhile, the input training utilised proton density, solar wind plasma speed, plasma flow pressure, and Interplanetary Magnetic Field (IMF) space data using Non-Linear Auto Regressive Input (NARX). The best modelling outputs were 3, 2, and 3 for input delay (𝑛𝑢), output delay (𝑛y), and hidden layer size (h), respectively. Residual test and model fit analysis show an unbiased and high overlap between predicted and actual geomagnetic field average values, suggesting the model can potentially anticipate the geomagnetic field average during solar quiet in November (month of Equinox) on solar cycle inclination.

Forecasting GIC Activity Associated With Solar Wind Shocks for the Australian Region Power Network

AbstractsSpace weather induced Geomagnetically Induced Currents (GICs) that are hazardous to power networks at high latitudes are often the result of rapid changes in near‐Earth space current systems such as the auroral electrojets and field‐aligned currents. GIC activity at low‐middle latitudes may be driven by currents that are more relevant to these regions such as the ring or magnetopause current systems. Solar wind shocks create rapid changes in the magnetopause current that manifest as large step changes in the geomagnetic field at the Earth's surface, often referred to as geomagnetic sudden impulses (SIs), that are effective drivers of GIC activity in power networks at low‐middle latitudes. This paper describes the results of a study into the relationship between the driver of SIs, solar wind shocks, and their potential impact on power systems in the Australian region, as quantified by a GIC‐index. The initial data set for analysis was produced by programmatically scanning solar wind data obtained from the ACE satellite spanning the period 1998–2008 for solar wind shocks. For each identified solar wind shock, geomagnetic field data from the Australian region were analyzed to determine the corresponding SI and GIC‐index. Statistical analyses of GIC‐indices and various solar wind parameters associated with the shocks resulted in an empirical model that is a function of solar wind dynamic pressure, geomagnetic latitude, and change in solar wind speed with good operational predictive capability. This model was further assessed using online catalogs of solar wind shock events.

Modeling geomagnetic induction in submarine cables

Submarine cables have become a vital component of modern infrastructure, but past submarine cable natural hazard studies have mostly focused on potential cable damage from landslides and tsunamis. A handful of studies examine the possibility of space weather effects in submarine cables. The main purpose of this study is to develop a computational model, using Python, of geomagnetic induction on submarine cables. The model is used to estimate the induced voltage in the submarine cables in response to geomagnetic disturbances. It also utilizes newly acquired knowledge from magnetotelluric studies and associated investigations of geomagnetically induced currents in power systems. We describe the Python-based software, its working principle, inputs/outputs based on synthetic geomagnetic field data, and compare its operational capabilities against analytical solutions. We present the results for different model inputs, and find: 1) the seawater layer acts as a shield in the induction process: the greater the ocean depth, the smaller the seafloor geoelectric field; and 2) the model is sensitive to the Ocean-Earth layered conductivity structure.

Reconstructing biblical military campaigns using geomagnetic field data

The Hebrew Bible and other ancient Near Eastern texts describe Egyptian, Aramean, Assyrian, and Babylonian military campaigns to the Southern Levant during the 10th to sixth centuries BCE. Indeed, many destruction layers dated to this period have been unearthed in archaeological excavations. Several of these layers are securely linked to specific campaigns and are widely accepted as chronological anchors. However, the dating of many other destruction layers is often debated, challenging the ability to accurately reconstruct the different military campaigns and raising questions regarding the historicity of the biblical narrative. Here, we present a synchronization of the historically dated chronological anchors and other destruction layers and artifacts using the direction and/or intensity of the ancient geomagnetic field recorded in mud bricks from 20 burnt destruction layers and in two ceramic assemblages. During the period in question, the geomagnetic field in this region was extremely anomalous with rapid changes and high-intensity values, including spikes of more than twice the intensity of today's field. The data are useful in the effort to pinpoint these short-term variations on the timescale, and they resolve chronological debates regarding the campaigns against the kingdoms of Israel and Judah, the relationship between the two kingdoms, and their administrations.

SARS-CoV-2 virus spreading in depending of the Earth’s magnetic field

The paper presents the results of a study on the possible relationship between the spread of SARS-CoV-2 virus and the Earth’s magnetic field based on an analysis of digital data for 95 countries. Article is based on the statistic data of geomagnetic field and coronavirus disease parameters, which allows, in our opinion, to obtain reliable results of their interpretation, which are as follows:
 The dependence of the spatial expansion of the SARS-CoV-2 virus on the value of modular values of the induction of the Earth’s main magnetic field BIGRF is established. The maximum number of diseases occurs in countries located in regions with reduced (25.0—30.0 mkT) and increased (48.0—55.0 nT) values.
 The SARS-CoV-2 virus expansion of the dynamics of the geomagnetic field over the past 70 years dependence is established. Maximum diseases number refers to areas with maximum changes, in the direction of decrease (up to –6500 nT) and increase 2500 nT).
 Comparison of SARS-CoV-2 disease expansion dynamics with Кр-geomagnetic field perturbation index — shows the practical absence of such connection. The ratio of the maximum daily growth of diseases to the minimums of external geomagnetic activity detected. This ratio may be due to the intensity of galactic cosmic radiation increase during this period.
 The connection between the SARS-CoV-2 disease process dynamics and solar activity is shown, namely: the beginning of a pandemic is due to the superposition of minima of 11 (between 24 and 25) and 110 (120?) solar activity annual cycles. The increase of diseases number correlates well with the increase of the 25th cycle solar activity. The Spanish pandemic was characterized by a similar ratio, which arose about 110 years ago between 14—15 cycles and took place during the 15th cycle of solar activity growth phase.

Separation of Spacecraft Noise From Geomagnetic Field Observations Through Density-Based Cluster Analysis and Compressive Sensing.

The use of magnetometers for space exploration is inhibited by magnetic noise generated by spacecraft electrical systems. Mechanical booms are traditionally used to extend magnetometers away from noise sources. If a spacecraft is equipped with multiple magnetometers, signal processing algorithms can be used to compare magnetometer measurements and remove stray magnetic noise signals. We propose the use of density‐based cluster analysis to identify spacecraft noise signals and compressive sensing to separate spacecraft noise from geomagnetic field data. This method assumes no prior knowledge of the number, location, or amplitude of noise signals, but assumes that they have minimal overlapping spectral properties. We demonstrate the validity of this algorithm by separating high latitude magnetic perturbations recorded by the low‐Earth orbiting satellite, SWARM, from noise signals in simulation and in a laboratory experiment using a mock CubeSat apparatus. In the case of more noise sources than magnetometers, this problem is an instance of underdetermined blind source separation (UBSS). This work presents a UBSS signal processing algorithm to remove spacecraft noise and minimize the need for a mechanical boom.

Magnetization Structure and its Temporal Change of Miyakejima Volcano, Japan, Revealed by Uncrewed Aerial Vehicle Aeromagnetic Survey

Miyakejima volcano experienced its latest eruption in 2000 with the summit subsidence, and the next event is expected in the near future. An aeromagnetic survey in Miyakejima was conducted in March 2021 in order to investigate the current state of its magnetization structure to identify the potential for another eruption and, thus, mitigate volcanic disaster. The survey flight was conducted using an uncrewed aerial vehicle (UAV), a multirotor drone, to deploy a scalar magnetometer. After processing geomagnetic field data from this survey, in combination with data from previous surveys conducted by using another UAV, an uncrewed helicopter, the average magnetization intensity was determined to be 12.4 A/m. Further, the surrounding area of the crater was relatively highly magnetized; however, the crater rim had a low magnetization intensity. Temporal variation was detected between 2014 and 2021 and dominated the central part of the observation area. Decreased magnetization intensity was identified beneath the caldera, which may become recently demagnetized due to heat supply traveling through fractures in the impermeable layer from the deep heat reservoir.

The Response of Geomagnetic Daily Variation and Ionospheric Currents to the Annular Solar Eclipse on 21 June 2020

AbstractIn this study, we examined the response of geomagnetic daily variation and ionospheric currents to the solar annular eclipse that passed through China at 10:00–17:30 LT on 21 June 2020. We utilized geomagnetic field data obtained from the Geomagnetic Network Center of China, Institute of Geophysics, China Earthquake Administration and International Real‐time Magnetic Observatory Network, which extend from middle latitudes to equatorial regions in China and surrounding areas. It is found that the eclipse obscuration reduces the intensity of geomagnetic daily variation at most stations as compared with the 5‐quiet‐day average. The response of geomagnetic daily variation to the solar eclipse depends on local time and latitude. In most shadowed areas of the eclipse, there is a reduction of northward (southward) component of the meridional magnetic daily field (ΔX) below (above) ∼30° magnetic latitudes. The solar eclipse can also induce a decrease of the zonal magnetic field variation (ΔY). Meanwhile, we observed that the solar eclipse reduces the intensity of vertical geomagnetic component (ΔZ) at the stations around the early afternoon aside the eclipse totality. In general, these geomagnetic responses imply that the solar eclipse can reduce the ionospheric solar quiet (Sq) current system. These ionospheric currents and ground geomagnetic field disturbances during the solar eclipse are also verified by a global physical model. The simulation demonstrates that the ionospheric currents in the southern hemisphere, where the solar eclipse does not cover, also have a weak response. Overall, the ionospheric current disturbance induced by the solar eclipse shows a counter‐Sq pattern.

Magnetic Anomaly Data Detection of Local Marine Geomagnetic Field Model considering Robust Trend Surface Scientific Calculation Algorithm

As a momentous part of marine data detection, modeling the marine geomagnetic field and detecting its magnetic data have momentous theoretical and practical value for obtaining its geomagnetic field parameters and characteristic distribution and then carrying out marine environment research. The detection of magnetic anomaly data of local marine geomagnetic field model has become a momentous prerequisite for obtaining key parameter info of local ocean and subsequent development and utilization. On account of this, this paper first analyzes the detection of local marine geomagnetic anomaly data, then studies the theoretical basis of local marine geomagnetic field model, as well as the measurement and modeling of local marine geomagnetic field data, and finally gives the analysis of the detection results of local marine geomagnetic anomaly data considering the robust trend surface.