Sunspot Number Research Articles

Ionospheric TEC modeling using COSMIC-2 GNSS radio occultation and artificial neural networks over Egypt

Abstract The ionospheric delay significantly impacts GNSS positioning accuracy. To address this, an Artificial Neural Network (ANN) was developed using the high-quality COSMIC-2 ionospheric profile dataset to predict the Total Electron Content (TEC). ANNs are adept at addressing both linear and nonlinear challenges. For this research, eight distinct ANNs were cultivated. These ANNs were designed with the following inputs Year, Month, Day, Hour, Latitude, and Longitude. Along with solar and geomagnetic parameters such as the F10.7 solar radio flux index, the Sunspot Number (SSN), the Kp index, and the ap index. The goal was to discern the most influential parameters on ionosphere prediction. After pinpointing these key parameters, an enhanced model utilizing a pioneering technique of a secondary ANN was employed with the main ANN to predict TEC values for events in 2023. The study’s findings indicate that solar parameters markedly enhance the model’s accuracy. Notably, the augmented model featuring a prelude secondary network achieved a stellar correlation coefficient of 0.99. Distributionally, 41 % of predictions aligned within the (−1≤ ΔTEC ≤1) TECU spectrum, 28 % nestled within the (1< ΔTEC ≤2) and (−2≤ ΔTEC <−1) TECU ambit, while a substantial 30 % spanned the broader (2< ΔTEC ≤5) and (−5≤ ΔTEC <−2) TECU range. In essence, this research underscores the potential of incorporating solar parameters and advanced neural network techniques to refine ionospheric delay predictions, thus boosting GNSS positioning precision.

On the external forcing of global eruptive activity in the past 300 years

The decryption of the temporal sequence of volcanic eruptions is a key step in better anticipating future events. Volcanic activity results from a complex interaction between internal and external processes, with time scales spanning multiple orders of magnitude. We review periodicities that have been detected or correlated with volcanic eruptions/phenomena and interpreted as resulting from external forces. Taking a global perspective and longer time scales than a few years, we approach this interaction by analyzing three time-series using singular spectral analysis: the global number of volcanic eruptions (NVE) between 1700 and 2022, the number of sunspots (ISSN), a proxy for solar activity, and polar motion (PM) and length of day (lod), two proxies for gravitational force. Several pseudo-periodicities are common to NVE and ISSN, in addition to the 11-year Schwabe cycle that has been reported in previous work, but NVE shares even more periodicities with PM. These quasi-periodic components range from −5 to −130 years. We interpret our analytical results in light of the Laplace’s paradigm and propose that, similarly to the movement of Earth’s rotation axis, global eruptive activity is modulated by commensurable orbital moments of the Jovian planets together with Pluto, whose influence is also detected in solar activity.

Solar Cycle Observations

We describe the defining observations of the solar cycle that provide constraints for the dynamo processes operating within the Sun. Specifically, we report on the following topics: historical sunspot numbers and revisions; active region (AR) flux ranges and lifetimes; bipolar magnetic region tilt angles; Hale and Joy’s law; the impact of rogue ARs on cycle progression and the amplitude of the following cycle; the spatio-temporal emergence of ARs that creates the butterfly diagram; polar fields; large-scale flows including zonal, meridional, and AR in-flows; short-term cycle variability; and helioseismic results including mode parameter changes.

Low latitude Pi2 pulsations at Desalpar, Gujarat, India: A statistical analysis of the influences of magnetic storms/substorms, seasons, and solar cycles

In this paper, we investigate the characteristics of geomagnetic pulsations (Pi2) using ground magnetometer data from the Induction Coil Magnetometer of Desalpar, Kachchh, Gujarat, India. We report here the morphology of Pi2 bursts (6–25 mHz) and their local temporal variations over the period of 2013–2017. The investigation is being conducted to study the effects of Sun-Earth interaction on the occurrences of low-latitude Pi2 pulsations. Local trend analysis was performed by classifying the data into 3 h local time slots (LT = UT+5.5). The Pi2 periods and the frequency of occurrence in local time in 2013 show that the maximum number of events occurs in the early morning and at midnight, and a very small number of events in the middle of the day. This could be due to tail currents at magnetic reconnection during the night-time. We have observed more events in the Y component compared to the X component. On the other hand, the observed periods of the X component are longer compared to the Y component. Enhanced periodicity of Pi2 observed during 18.0–19.0 LT. The periodicity of the H component is increased from 6.0 LT and reaches a maximum during 18.0–19.0 LT and then decreases. The maximum amplitude is noted at 6.0 LT. The amplitude of the Y component noticed is the largest than the X component during the study period. Number of Pi2s are found different in different ranges of Kp, Interplanetary Magnetic Field (IMF) and Solar wind speed (SWS), Proton density (PD) with maximum number in lower Kp values, IMF B∼5 nT, SWS∼400 Km/s, PD∼5–10N/cc. The identified peak-to-peak amplitudes of Pi2s are in relation with the generated plasma dynamics of magnetosphere by the solar parameters as various global parameters such as the IMF, interplanetary electric field (IEy), sunspots numbers (SSN), planetary index (Kp), SWS, PD, and Bursty Bulk Flows (BBFs) generated Pi2s have been noted during midnight (18.0–21.0 UT and 3.0–9.0 UT) during the 2013–2015 compared to the 2016–2017. This is evident with the longer period of Pi2s greater than 120 Sec. These longer period Pi2s have been accompanied with the field aligned currents produced by braking BBFs. The winter and fall seasons showed a good substantial generated Pi2s in our study compared to other seasons. We found an inverse relationship of occurrence of Pi2 is with global parameters such as SSN, Kp and IEy on our site. 74–78% of Pi2s generated is during the substorm period. The non-substorm Pi2s seems to be more in 2013–2015 (Solar maximum) than in 2016–2017 (Solar minimum).

Geomagnetic Storm Associated D‐Region Anomalies Estimated From VLF Observations at a Low‐Latitude Station, Suva, Fiji

AbstractA comprehensive analysis of subionospheric Very Low Frequency (VLF) signals from transmitters with call‐signs NWC, NPM, and NLK monitored at a low‐latitude station, Suva, Fiji, has been carried out to determine the D‐region effects of the intense geomagnetic storms for a period of 7 years (2011–2016 and 2018). The periods of 2011–2016 and 2018 fall under the 24th solar cycle with yearly mean sunspot numbers 80.8–39.8 and 7.0, respectively. Seven out of 12 geomagnetic storms revealed VLF anomalies during the storm main phase day and storm recovery phase days. A pronounced decrease in the VLF amplitude and phase for the storms having main phase onset in the nighttime was found. Out of seven geomagnetic storms, a storm of St Patrick's Day of 17–18 March 2013 is presented as a case study and the same analysis was done for all the storms. The anomalies in the VLF amplitude and phase have been modeled using Long‐Wave Propagation Capability code V2.1 to obtain the changes in Wait parameters (reference height, H′ and sharpness factor, ꞵ) and changes in the D‐region electron density. In most cases of storms, an increase in the H′ and a decrease in the ꞵ during the storm main and recovery phases were obtained which for the 17–18 March 2013 storm increased by 7.4–7.6 km and decreased from 0.03 to 0.07 km−1, respectively. Wavelet analysis of signal anomalies showed clear wave‐like spectra (0.05–0.18 mHz) of atmospheric gravity waves indicating traveling ionospheric disturbances propagating toward low latitudes due to storm‐associated Joule heating at high latitudes.

Impact of solar variability on Indian summer monsoon through large scale circulations

Via large-scale circulations, the role of solar variability on the Indian summer monsoon is investigated. Standardized anomaly is used to identify years of solar maxima and minima. Statistical analysis such as moving mean, empirical mode decomposition, and wavelet analysis are used to determine the plausible relationship between solar variability, large scale circulations such as El- Niño 3.4 SST anomaly, Dipole Mode Index (DMI), and Atlantic Multidecadal Oscillation (AMO), and Indian summer monsoon rainfall (ISMR). In addition, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP-NCAR) reanalysis data collection is used to assess time-averaged composite anomalies of wind, latent heat, geopotential height during maximum/minimum solar activity years.The 31-years moving mean of DMI shows significant negative correlation (−0.35) with sunspot number and with 31-years moving mean ISMR (−0.28). Similarly, 31-years moving mean of AMO exhibits a substantial positive connection with ISMR (0.68) and Niño 3.4 SST shows significant negative correlation (−0.62) with ISMR. Using intrusive mode function analyses it has noted that India receives rainfall for both phase of solar forcing but from wind studies at 850 hPa and 200 hPa, it is found that the phenomena for ISMR is different during solar maximum and minimum conditions. During solar maximum, a strong Low Level Jet is observed, and during solar minimum, a strong Tropical Easterly Jet is observed. Also, the area of influences of ISMR is different for solar maximum and solar minimum. The mean difference plot of seasonal rainfall shows that during solar maximum, north India receives reasonable amount of rainfall however during solar minimum, south India receives comparatively more rainfall than north India. A weakening of the local Hadley cell during solar minima is also observed.

Inter-annual variation of the mean temperature and rainfall in Chad and the solar activity during the period from 1950 to 2020

Given the increased variability of rainfall, the resurgence of extreme meteorological phenomena such as droughts, floods, heat waves, and violent winds and considering the influence of solar activity on certain meteorological parameters, a study on the variation of temperature and rainfall in relation to solar activity is necessary. The present study aimed to analyze the inter-annual evolution of meteorological parameters (mean temperature and rainfall) in Chad over the period from 1950 to 2020 in two geographically distinct areas (Sudanian and Sahelian zone) and then to establish a link between these parameters and solar activity to predict the variations of these two parameters over time. The rainfall results showed that the wettest years were 1959 in N'Djamena and 1951 in Moundou. On the other hand, the driest years recorded were 1984 in N'Djamena and 1998 in Moundou. The study of the mean temperature in these two areas during the same period led to the conclusion that the hottest year was 2010 in N'Djamena; however, in Moundou the hottest year recorded was 1997. Globally, in the two zones concerned, analysis of the results on variations in rainfall showed a downward trend. However, the study of average temperature showed a steady rise over the same period. The superposition of rainfall and mean temperature with the sunspot numbers Rz led to the conclusion that high sunspot activity increases temperature and thus warming, while low sunspot activity brings high precipitation.

Ionospheric foF2 nowcast based on a machine learning GWO-ALSTM model

The ionospheric foF2 prediction model GWO-ALSTM is proposed in this paper, which combines the Gray Wolf Optimization (GWO) algorithm and the attention mechanism based on the long short-term memory (LSTM) model. The datasets used in this model were obtained from the oblique ionosondes of the China Ground-based Seismo-ionospheric Monitoring Network. The data from 2010 to 2012 was used for training, and the data of 2013 was used for verification. The input parameters of the model include local time, day number, sunspot number, F10.7 solar flux, Ap index, Dst index, and foF2 at the previous moment, and the output parameter is foF2 at the current moment. The IRI2016 model, GABP model, LSTM model and GWO-ALSTM model were compared with the observed results for analysis. The results show that the GWO-ALSTM model is superior to the LSTM model, the GABP model and the IRI2016 model. Meanwhile, the comparative analyses of the diurnal variations of foF2 show that the curve of the GWO-ALSTM model fits the observed values more closely than the curve of any other model under the geomagnetic quiet and storm conditions.

Investigating the Relationship between Climate Variables and Solar Activity: A Regression Analysis Approach

This study employs regression analysis to investigate the relationships between carbon dioxide levels, sunspot occurrences, and global temperatures, encompassing both land and sea. By uncovering these connections, the study contributes to our understanding of climate change and solar phenomena interactions. The primary objective is to reveal the intricate associations between these elements, potentially influencing climate change and solar activity. The study's outcomes have significant implications for climate change research and solar activity monitoring. The positive correlation between carbon dioxide concentration and ocean temperatures emphasizes the impact of atmospheric carbon dioxide on sea temperature fluctuations. Conversely, the inverse correlation between sunspot numbers and land/global temperatures suggests solar activity's potential role in shaping Earth's temperature oscillations. This research introduces novelty by concurrently investigating the interconnectedness of these factors. The study establishes substantial connections between carbon dioxide concentration, sunspot numbers, and global temperatures. While the models shed light on some variability, the complexity of climate change and solar activity calls for further exploration of additional factors. This underscores the need to consider multiple variables for a comprehensive understanding. Further research is recommended to enhance the precision of these models.

Katsue Misawa's sunspot observations in 1921–1934: a primary reference for the Wolfer-Brunner transition

Abstract Our knowledge of the long-term solar variability is largely based on the International Sunspot Number time series (SN), a composite index based on multiple visual sunspot observers from the 18th century onward and maintained by the World Data Center ‘Sunspot Index and Long-term Solar Observations’ (SILSO). However, over the period 1919–1944, our capacity to diagnose the homogeneity of this time series is currently limited because most of the archived source data of the Zürich Observatory are presently missing over that interval and were not published. Therefore, the recovery of any long-duration series from an individual sunspot observer active during this period is essential to bridge this Zürich data gap. In this context, Katsue Misawa has conducted regular sunspot observations from 1921 to 1934 (mean coverage of 25.4 days/month), which were not accessible for the Zürich Observatory and thus form a valuable addition to the database maintained by the WDC-SILSO. In this study, we digitized his observational records, documented his observing technique, and reconstructed his total and hemispheric sunspot numbers. We compared his data with the International Sunspot Number (current version V2) and evaluated their stability. Misawa's data series generally agrees well with SN V2. However, Misawa's data show a significant transitory drift in 1925–1928, when the Zürich pilot observer changed from Alfred Wolfer to William Otto Brunner.

Kill rates on native ungulates by Mexican gray wolves in Arizona and New Mexico

AbstractMexican gray wolf (Canis lupus baileyi) reintroduction began in 1998 in Arizona, USA, with 11 individuals initially; wolves from this population dispersed into New Mexico, USA, the following year. Numbers of wolves have steadily increased (≥241 individuals in Dec 2022) and the size of the recovery area has expanded in both states. Understanding kill rates is fundamental to the recovery and management of Mexican wolves to properly maintain a healthy and sustainable population of wolves and their prey species. We used global positioning system (GPS) cluster analysis to locate kill sites and estimate kill rates on native ungulate prey taken in the recovery area by 10 Mexican wolf groups during winter (15 Feb–15 Mar) and 8 wolf groups during summer (15 Jun–15 Jul) in 2015–2017. Elk (Cervus canadensis) composed 94% of the native ungulates documented at Mexican wolf kill sites (n = 139); the remaining 6% were mule deer (Odocoileus hemionus; n = 9). Two‐thirds of all wolf‐killed native ungulates were elk calves. We estimated kill rates as ungulate biomass killed/wolf/day. From combined results for all years, we estimated that each Mexican wolf killed on average 9.0 ± 5.4 kg (±95% CI) of ungulate biomass per day during winter and 7.8 ± 6.2 kg during the summer. Our estimates of kill rates could be overestimated if Mexican wolves obtained a substantial amount of their biomass from scavenging, particularly during 4 months of hunting seasons (Sep–Dec) not captured in winter and summer. Using data from the first 2 years of the study (2015, 2016), we developed a logistic regression model to predict probability of an elk kill occurring at a GPS cluster as a function of hours spent by a collared wolf at that GPS cluster and size of the wolf group. Applying this model to the final year of the study (2017) resulted in similar numbers of predicted (n = 16.1 and 27.2 in winter and summer, respectively) and observed numbers (n = 15 and 26) of elk kills. Additionally, the demographic composition of the 2017 elk kills closely matched that of the previous 2 years. The results of this study indicate similar predation patterns of Mexican wolves to those of wolves in northern locations and can help wildlife managers plan for potential effects from a larger population of wolves across a broad area in Arizona and New Mexico.

The Sunspot Number Forecasting Using a Hybridization Model of EMD, LSTM and Attention Mechanism

Sunspot number forecasting is a significant task for human beings in order to observe solar activity, and it is a classical chaotic time series. To improve the forecasting accuracy, we proposed a hybrid forecasting model based on empirical mode decomposition, long short‐term memory neural network and attention mechanism. First, the empirical mode decomposition is used to transfer the sunspot number to several sub‐components called IMFs and residual. Then, the IMFs and residual are input to the prediction module that is composed of long short‐term memory neural network and attention mechanism. Finally, the forecasting result is accumulated by the IMFs and residual through the predict module. The RMSE, MAE, MAPE and R2 indexes of our method are 1.567, 1.048, 1.8% and 0.9997. We also compare with GRU, informer and XGBoost‐DL, our method improves accuracy above 24 and 18 of RMSE and MAE, respectively. The experiment results demonstrate our method has higher precision. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Влияние гелиометеорологических факторов на эпидемический процесс геморрагической лихорадки с почечным синдромом и прогнозирование заболеваемости на территории лесостепного и степного ландшафтов

On the territory of the Orenburg region over a 25-year period, we studied the influence of 43 helio-meteorological factors on the cumulative incidence of hemorrhagic fever with renal syndrome (HFRS) in the population of the forest-steppe and steppe landscapes. It is shown that in of the forest-steppe landscape the priority helio-meteorological risk factors for the incidence of HFRS were: an increase in precipitation in March of the current year and in October of the previous year, as well as an increase in the Wolf number with a shift relative to the incidence to the left by 6 years. On the territory of the steppe landscape, the leading helio-meteorological risk factors for the epidemic process of HFRS were: a decrease in precipitation in June of the previous year, an increase in precipitation in February of the current year, and an increase in the Wolf number with a shift to the left relative to the incidence by 3 years. We have obtained regression equations for a short-term (for the current year) forecast of the intensity of the epidemic process of HFRS, taking into account helio-meteorological factors in the territory of the forest-steppe and steppe landscapes. These mathematical equations have been tested against actual morbidity rates. They can be recommended for practical use, as well as further improvement of the model. Keywords: hemorrhagic fever with renal syndrome, forest-steppe and steppe landscapes, prognosis, incidence.

Ionospheric prediction algorithm and its application in low-latitude regions based on the physically constrained polynomial model

With sufficient consideration of the ionospheric variation subject to solar activity and geomagnetic variation, a high-precision ionospheric prediction model, i.e., a physically constrained polynomial model (PCPM), was constructed by adding the Kp index, Dst index, F10.7P, R sunspot number, and other geomagnetic and solar variation indexes. Based on the data of Continuously Operating Reference System (CORS) stations in China Southern Power Grid, the ionospheric prediction results of PCPM were compared with the widely used and well-recognized seasonal enhanced product autoregressive integrated moving average (Arima) model without additional physical parameters. The prediction performance of the two models in different prediction time spans and the influence of model products on Precise Point Positioning-Real Time Kinematic (PPP-RTK) was emphatically analyzed. The experimental results demonstrated that the accuracy of PCPM is better than that of the seasonal Arima model on the first day of prediction, the prediction accuracy of the two models decreases with the increase of prediction time span, and the lowest accuracy of the seasonal Arima model on the first three days is 1.5 TECU. The Arima model outperforms the PCPM over a long-time span; in light of the overall accuracy, PCPM is more available in five southern provinces, outperforming the seasonal Arima model; the results of the PPP-RTK experiment showed that the PCPM could not only improve the velocity of ambiguity fixing but also improve the positioning accuracy after ambiguity fixing. Compared with the seasonal Arima model, the velocity of ambiguity fixing is increased by 16.7%, and the positioning accuracy after ambiguity fixing is improved by 61.0% in the E direction and 6.9% in the U direction compared with the results without ionospheric constraints.

CASHEW, A RESPONSE TO THE COCOA CRISIS IN DIMBOKRO IN THE N'ZI REGION? (IVORY COAST)

As the Sun progresses through its solar cycle and its activity increases, a greater number of sunspots occur, and solar fluxes become more intense. The change in solar activity is related to the change in electron and ion density of the ionosphere. Studying this relation is very important in terms of space plasma studies and space weather predictions, which play a significant role in radio and satellite communication as well as GPS navigation. In this paper, we are presenting the sunspot and solar flux data for solar cycle 24 (year 2008-2020). We are using C/NOFS satellite data to understand the variationof ion densities of the upper atmosphere with solar cycle 24. Our analysis shows decrease of H+ density by a factor of 10 and increase of O+ density by a factor of 10 during solar maxima. This data set can be used as a framework for future advancement in empirical modelling of regional and global ion density of the ionosphere.

New Space Companies Meet a “Normal” Solar Maximum

AbstractThe monthly mean sunspot number has been larger in June–July 2023 than the double peak of solar cycle 24 (146 in February 2014 and 139 in November 2011) and brings us back to the sunspot level of solar cycle 23. However, the number of rocket launches, satellites in orbit and private space companies has increased dramatically in the past 20 years. Additionally, there is a growing interest for space exploration beyond Earth's orbit, to the Moon and beyond, which comes with higher risk of being affected by space weather. Here, we discuss some of these trends and the role of the journal to improve awareness of space weather impacts.

Key Signatures of Prominence Materials and Category of Cold Materials Identified by Random Forest Classifier

The origin of cold materials identified by different criteria is unclear. They are highly suspected to be erupted prominences. However, some cold materials defined by charge depletion exist in both quiet solar wind and interplanetary coronal mass ejections (ICMEs). Recently, solar observations show failed prominence eruption in coronal mass ejections (CMEs) that the prominence sometimes did not propagate into interplanetary space. This work uses Random Forest Classifier (RFC), which is an interpretable supervised machine-learning algorithm to study the distinct signatures of prominence cold materials (PCs) compared to quiet solar wind (QSW) and ICMEs excluding cold materials (ICMEEs). Twelve physical features measured by ACE at 1 au and the monthly averaged sunspot number are used in this study. The measurements from ACE are proton moments, magnetic field component B z , He/H, He/O, Fe/O, mean charge of oxygen and carbon, C6+/C5, C6+/C4+, and O7+/O6+. According to the returned weights from RFC that are checked by support vector machine classifier, the most important in situ signatures of PCs are obtained. Next, the trained RFC is used to check the category of the cold materials not related to CME observations. The results show that most segments of the cold materials are from prominences, but four of them are possibly from ICMEEs; another one segment is possibly from QSW. The most distinct signatures of PCs are lower (C6+/C5+)/(O7+/O6+), proton temperature, and He/O. Considering the obvious overlaps on key physical features between QSW, ICMEEs, and PCs, the multifeature classifier shows an advantage in identifying them than solid criteria.

Assessment of Outer Space Events on Troposphere and Climate Change over Iraq

The aim of the study is to investigate the effects of space weather on the troposphere, where our climate exists. This work is useful to give us an idea of ​​the interaction between solar activity and some meteorological parameters. The sunspot number (SSN) data were extracted from the World Data Center for the production, preservation, and dissemination of the international sunspot number (SILSO), top net solar radiation (TSR) and temperature 2 meters from the ERA5 model of the Copernicus Climate Change Service (C3S) from the Climate Data Store with 0.25 grid Resolution, providing a rich source of climate data for researchers. This study was conducted from 2008 to 2021 (solar cycle 24 and the beginning of 25) over Iraq located within latitude (38°N-28°S) and longitude (38°W- 49°E). The results that have been reached were a decrease in solar activity in solar cycle 24, accompanied by an increase in the length of the solar cycles, and there was a direct proportion between TSR and temperature. TSR and temperature had almost similar spatial distribution over Iraq. The northern regions received the least amounts, while from the middle toward the southeast regions, they received the highest amount of TSR and temperature over Iraq. There was a reverse regression between sunspot numbers with TSR and positive regression with temperature, but the correlation was not significant.

New insights into potential effects of solar activity on the oxygen isotope composition of modern speleothems: Observations from Jiguan Cave, central China II

The oxygen isotope compositions (δ18O) of stalagmites have been widely used to reconstruct summer monsoon variability, which is primarily controlled by the solar radiation on orbital to seasonal timescales. However, the relationship between solar activity and the monsoon system remains unclear. This study is based on a 13-year, from 2010 to 2022, monitoring campaign of the δ18O values of precipitation, cave drips and modern speleothems in Jiguan Cave, central China. By analyzing moisture circulation and the local meteoric water line (LMWL) under different ENSO (El Niño Southern Oscillation) events, it was found that on seasonal–interannual timescales, the precipitation δ18O values reflect the atmospheric moisture circulation. In addition, we used cross-wavelet transform analysis to investigate the periodic relationship between the sunspot number (SSN) and the Asian Summer Monsoon (ASM) since solar activity is one of the most important external influences on the ASM. The stalagmite δ18O data for our study areas reflect the solar activity-induced decadal-scale variations in the local precipitation. The key factors related to the δ18O value include the driving mechanism of the solar activity and ENSO-related sea surface temperature (SST) anomalies. We speculate that changes in solar activity cause anomalies in the tropical SST gradient, which affects central China's hydrological cycle by changing the variability of ENSO and the Indian Ocean Dipole (IOD) and ultimately causes the variations in stalagmites δ18O records.

Applying support vector machine (SVM) using GPS-TEC and Space Weather parameters to distinguish ionospheric disturbances possibly related to earthquakes

The effort to identify and comprehend potential earthquake-related phenomena shares a common goal: successful earthquake forecasting. Advancements in science and technology have made this goal multidisciplinary. Currently, possibly earthquake-related anomalies in the Vertical Total Electron Content (VTEC) of the Earth’s ionosphere are being investigated. Global Navigation Satellite Systems (GNSS) can be used to calculate this ionospheric parameter. In this research work, GPS VTEC was calculated for periods between the years 2015 and 2019. The selection of this periods considered both seismically active and non-seismically areas in Mexico. The Mw ≥ 5 earthquakes under study were registered by the National Seismological Service. Moreover, different geomagnetic storm and solar activity parameters, such as the geomagnetic equatorial Dst index and the F10.7 index, were analyzed. Additionally, the daily average and monthly mean number of sunspots (R, SSN, respectively) were included as a direct, long-term record of the development of the solar cycle. To the periods under study different statistical methods were applied, such as Mean-Square Error (MSE) and cross-correlation. The above aims to apply a machine learning technique capable of classifying between periods with seismic and non-seismic activity. The features were constructed using statistical data and results from the implemented analysis. Furthermore, Principal Component Analysis (PCA) was applied to reduce the feature vector dimensions, and accuracy scores were compute using k-fold cross-validation. The results from the Support Vector Machine (SVM) model indicated an accuracy of 88.9% for the training set, and an accuracy of 80% was obtained for the test set. One of the limitations of the current study was the sample size. However, the present initial approach for classifying seismic events from non-seismic periods using SVM demonstrated promising results when considering the indicated parameters and the days under study.