Impending Earthquake Research Articles

Off-fault deformation feedback and strain localization precursor during laboratory earthquakes

Recent large-scale seismological observations have shown that off-fault strain localization and foreshock migration could serve as an early warning of an impending earthquake. However, this process is still largely unknown. In this study, state-of-the-art friction experiments were conducted in a oil-confined biaxial shear apparatus to investigate the link between stick-slip nucleation and off-fault deformation. Our findings indicate that there is a direct link between stick-slip nucleation and off-fault deformation, provided that the fault is conditionally unstable (a − b < 0). Inelastic off-fault deformation may trigger unstable slip by decreasing the stiffness of the surrounding rock volume, which favors earthquake nucleation. Additionally, the study presents laboratory observation of precursory strain localization around a fault during stick-slip cycles. These findings suggest that volumetric deformation processes could be a main factor in the nucleation of large ruptures and strain localization could be a reliable harbinger of large earthquakes.

Identification of Possible Short-Term Ionospheric Earthquake Precursors for Seismic Events with Intermediate Hypocentrals Depths by Measuring the Standard Parameters of the Mid-Latitude Es Layer

Changes in deviations from the background values of the parameters of the sporadic E ionosphere (Es) are studied: the effective (virtual) height h’Es and limiting reflection frequency (foEs). Basically, the analysis was carried based on hourly measurement data from several Japanese ground stations for vertical sounding of the ionosphere in order to identify possible short-term ionospheric earthquake precursors with intermediate (from 60 to 300 km) hypocentral depths. All known events (12 earthquakes, from 1969 to 2022) are considered for which the necessary ionospheric data in the Japan region and magnitudes (M) ranging from 6.5 to 7.6 are available. From coincidence of the maxima in changes in the considered Es characteristics on the same day at pairs of stations separated by hundreds of kilometers, the time of appearance of possible ionospheric earthquake precursors was recorded. According to the ionospheric data available during the preparation period for the studied earthquakes, a tendency has been identified according to which the time the moment of the main influence is anticipated depends on the magnitude of the impending earthquake. Similarities and differences in the responses of the ionosphere to the preparation of surface (crustal) earthquakes and earthquakes with an intermediate hypocentral depth are revealed. Another tendency is also revealed: earlier appearance of the identified earthquake precursors with increasing hypocentral depth for earthquakes with an intermediate hypocentral depth at the same distances from the epicenter to the observation point.

Crustal stress buildup/relaxation and pore pressure in preparation and sequential brittle-shear fault rupture — Implications for a general theory of earthquake nucleation

Compelling geoscience evidence has heightened the appreciation of abnormal (suprahydrostatic, sub/quasi/supralithostatic) pore pressure and multimode (brittle-shear) fault rupture leading to seismicity. However, preparation and rupture nucleation are yet to be adequately constrained and quantitatively modeled. This challenges crucial schemes, notably physics-based earthquake forecasting/prediction. In this multidisciplinary study, the transitions and associated critical points linking the preexisting fluid overpressure in the fault patches, temporal crustal stress, and sequential brittle-shear fault failure were considered. In preparation, tectonic loading was accompanied by processes such as off-fault yielding, permeability enhancement, and foreshocks that facilitate local/regional stress relaxation. Subsequent equalization of stress and the preexisting local overpressure triggers hydraulic fracturing that destabilizes major asperities. Almost instant shear failure follows with the spatially varying rupture velocity/intensity of the frictional slip because of localized asperity stress and syn-slip fluid-/melt-driven fracturing/dilation and lubrication. Based on the aforementioned critical points, quantitative modeling associated stress drop with the evolution of the stress and pore pressure. Equations for the time to onset of stress relaxation and time to rupture were derived using fluid flow and viscoelastic models. The seismic moment was estimated with classical seismological relations after modifications accounting for the smaller surface area during frictional slip. For retrospective testing, two cases of induced seismicity (2016 Fairview, Oklahoma, USA, and 2017 Pohang, South Korea) and multiple cases of natural seismicity (including the 2024 Noto Peninsula earthquake, Japan) were considered. The replication of triggering mechanisms, source properties, and time to rupture suggested that stress temporal relaxation and triggered anomalies (STRATA) encompass fundamental hydromechanical processes in seismogenesis. The setting and scale invariance of STRATA suggest it might be a general theory of earthquake nucleation. Based on the identified preparatory processes/retrospective validations, a physics-based earthquake forecasting/prediction scheme was developed. The nurseries/hypocenters of impending earthquakes are identified through the simultaneous consideration of locally preexisting fluid overpressure and spatiotemporal analysis of stress relaxation. Event size and rupture timing are estimated with the derived relations herein.

Investigation into potential TEC changes due to 9 seismic tremors of 2021–2022

This research document delves into the analysis of alterations in Total Electron Content (TEC) as identified through GPS observations leading up to nine earthquakes that transpired between the late months of 2021 and 2022. The comprehensive study investigates the TEC variations and provides a detailed examination of heat maps centred around the earthquake epicentres. These heat maps, characterized by diverse latitudes and longitudes, play a pivotal role in validating the precise locations of the earthquake epicentres. Notably, the examination of these maps unveils discernible GPS TEC variations several days before the occurrence of each earthquake. In addition to the TEC analysis, the research sheds light on the fluctuations in solar magnetic parameters. The study elucidates instances where TEC peaks surpassed normal values, particularly during periods characterized by minimal solar radiation effects. This correlation between solar magnetic parameters and TEC fluctuations adds a nuanced layer to the understanding of the complex interplay of factors leading to seismic events. The cumulative findings derived from the investigation point towards a compelling conclusion: GPS TEC observations and the analysis of heat maps serve as indispensable indicators for identifying precursory signs that precede an impending earthquake. This multidimensional approach enhances our comprehension of the temporal and spatial aspects of seismic activity and underscores the potential significance of solar magnetic parameters in influencing such geophysical events.

Observation of the Preparation Phase Associated with Mw = 7.2 Haiti Earthquake on 14 August 2021 from a Geophysical Data Point of View

On 14 August 2021, an earthquake of moment magnitude Mw = 7.2 hit Haiti Island. Unfortunately, it caused several victims and economic damage to the island. While predicting earthquakes is still challenging and has not yet been achieved, studying the preparation phase of such catastrophic events may improve our knowledge and pose the basis for future predictions of earthquakes. In this paper, the six months that preceded the Haiti earthquake are analysed, investigating the lithosphere (by seismic catalogue), atmosphere (by climatological archive) and ionosphere by China Seismo-Electromagnetic Satellite (CSES-01) and Swarm satellites, as well as Total Electron Content (TEC) data. Several anomalies have been extracted from the analysed parameters using different techniques. A comparison, especially between the different layers, could increase or decrease the probability that a specific group of anomalies may be (or not) related to the preparation phase of the Haiti 2021 earthquake. In particular, two possible coupling processes have been revealed as part of the earthquake preparation phase. The first one was only between the lithosphere and the atmosphere about 130 days before the mainshock. The second one was about two months before the seismic event. It is exciting to underline that all the geo-layers show anomalies at that time: seismic accumulation of stress showed an increase of its slope, several atmospheric quantities underline abnormal atmospheric conditions, and CSES-01 Ne depicted two consecutive days of ionospheric electron density. This suggested a possible coupling of lithosphere–atmosphere and ionosphere as a sign of the increased stress, i.e., the impending earthquake.

Enhancing seismic assessment and risk management of buildings: A neural network-based rapid visual screening method development

Some of the existing buildings are designed based on lower design standards or even without considering seismic design standards. Recent earthquakes have further highlighted the vulnerability of these buildings when subjected to severe seismic activity. Consequently, it has become imperative to conduct seismic vulnerability assessments of the existing building stock. Therefore, the assessment of the existing building stock is required through the utilization of Rapid Visual Screening (RVS) methods. However, the existing conventional RVS methods used in seismic building assessments have shown limited accuracy. Furthermore, because these methods were developed based on expert opinions and/or due to access limitations to detailed assessment-based generated data used for their development, further enhancing them is challenging. To address these limitations, a new RVS method, which leverages Neural Networks (NN) and building-specific parameters, for reinforced concrete, adobe mud, bamboo, brick, stone, and timber buildings has been proposed in this study. Unlike conventional methods that rely on site seismicity class, the developed data-driven approach incorporates building-specific parameters such as the fundamental structural period and building spectral acceleration. The developed RVS method is specifically tailored to analyze diverse types of buildings in regions with varying seismicity risks, all in preparation for an impending earthquake. In this study, the developed RVS method demonstrated a promising 68% test accuracy, effectively representing the building performance against earthquakes. These findings illustrate the potential of the developed NN based RVS method in assessing existing buildings, thereby mitigating potential loss of life and property during imminent earthquake and alleviating the associated economic burden. Furthermore, this study introduces a new RVS method that can pave the way for future advancements in the field of seismic vulnerability assessment of existing buildings.

Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.

RC Row Houses in Nepal and their Vulnerability to Impending Earthquakes

RC Row Houses in Nepal and their Vulnerability to Impending Earthquakes

Monitoring the workflow of Earthquake by using Machine Learning

The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are trained on historical earthquake records to identify patterns and anomalies prior to seismic events. Our approach focuses not only on detecting seismic events, but also on predicting the location and magnitude of earthquakes. Earthquake monitoring and response systems play an important role in reducing potential losses and providing rapid response to seismic events. Conventional methods rely on sensor networks and manual analysis, often with delays in detection and response. The system receives real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. The system can detect and describe seismic events using deep learning models such as convolutional neural networks (CNN) and random neural networks (RNN).In addition, anomaly detection techniques allow the identification of unusual seismic patterns that may indicate impending earthquakes or earthquakes. The system integrates geographic information systems (GIS) to comprehensively map seismic activity, helping to visualize and understand seismic patterns in the region. Machine learning models are trained on historical seismic data to improve their predictive capabilities and adapt to different seismic behaviors. This proposed system provides a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. The ability to continuously learn from incoming data enables adaptation to change in seismic activity, thereby increasing the efficiency and reliability of earthquake monitoring operations.

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

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 &gt; 4; Dst &lt; −70 nT; ap &gt; 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.

A Study on the Relationship between Strong Earthquake and Abnormality of Space Static Electricity with Sample

It has been observed that, before some strong earthquakes occur, the space static electricity near the ground is abnormal, which might be caused by a large amount of radioactive gas released from the Earth's crust. In this paper, the information diffusion technology for optimally processing small samples is used to analyze 30 cases, and the relationship between magnitude and parameters such as abnormality of space static electricity is constructed. Each case is composed of four observation values: abnormality e, epicenter distance d, impending time t and magnitude m. Using the causal relationship constructed in this paper, the magnitude m of an impending earthquake could be approximately inferred from abnormality e. According to the progress of locking the epicenter and the passage of time, the predicted magnitude could be adjusted in a timely manner. The research results provided in this paper do not eliminate the uncertainty of earthquake occurrence, so that the study is a work of analyzing seismic dynamic risk.Integrating the monitoring information from seismic stations and the physical field information in the air will promote impending earthquake prediction, which is a worldwide scientific challenge.

A model of TEC perturbations possibly related to seismic activity

For application of observed anomalies of the total electron content (TEC) in the ionosphere for short-term earthquake prediction, it is necessary to understand the physical nature of such anomalies during the preparation of a strong seismic event. The physical mechanism of occurrence of such anomalies is considered in order to distinguish the TEC disturbance of a seismic origin and to clear the processes in the lithosphere resulted in an appearance of the TEC anomaly in the ionosphere. It is supposed that the observed TEC anomalies arise as a result of occurrence of additional electric field in the ionosphere demonstrated by numerous satellite data before the impending earthquakes. At the same time, an injection of charged aerosols into the atmosphere in the epicentral earthquake area was detected. As a result, an electromotive force arises in the surface layer of the atmosphere, which initiates a perturbation of the electric current in the global circuit and the electric field appearance in the ionosphere. We have shown that the TEC disturbance arises as a result of the heating of the ionosphere by electric current and the plasma drift in the electric field of this current. The spatial distribution of TEC arises as a result of the combined action of these two factors, and its nature depends on the relationship between them. Based on the developed model the numerical study of the spatial distribution of TEC in the ionosphere is possible for a given horizontal distribution of the concentration of charged aerosols in the atmosphere near the Earth’s surface.

Feasibility Study on Earthquake Prediction Based on Impending Geomagnetic Anomalies

By deploying a magnetic monitoring network in the earthquake-prone areas of Sichuan, China, and conducting long-term observations, processing, and analysis of real-time geomagnetic data, it can be observed that the pre-earthquake geomagnetic anomalies are highly correlated with the occurrence time of earthquakes. We propose a novel algorithm that obtains a new quantity by accumulating geomagnetic anomaly energy to eliminate external environmental interference and take its gradient as a measure for predicting the occurrence time of an earthquake. Through observations of a large amount of geomagnetic data, it is confirmed that the proposed method can be used to predict the occurrence time of earthquakes with about 75% to 85% accuracy. Conclusions: The geomagnetic anomaly phenomenon can be accurately observed and recorded before an impending earthquake, and it has been confirmed by data that using this observation makes imminent earthquake prediction a practical prediction method.

Pronounced Changes of Multi-geophysical Parameters Associated with Maduo (M= 7.4) Earthquake

The China Earthquake Administration (CEA) has a large network for monitoring multiple geophysical parameters to forecast earthquake and to have early warning information about an impending earthquake. In this paper, we present the analysis of various geophysical measured and borehole parameters (resistivity, borehole strain, water level, water temperature, atmospheric temperature, etc.) within 400 km radius of the epicenter. The strain measured in the borehole show pronounced tensional and compressional changes, and the direction of the main compressive strain is found to rotate clockwise. The active compression of the whole Bayankara block is found to be affected, the resistivity near the fault began to decrease and increase in different directions. The water level observed at the Yushu water well located in the southwest from the epicenter is found to change in the opposite direction at the Huangyuan borehole located in the northeast from the epicenter. Yushu water well station is affected by the clockwise activity of the whole Bayankara block, at this water level increases from 2017 and begins to decline from August 2020. Such pronounced decline in water level far from the epicenter of the earthquake prior to the earthquake event provides an early warning of the Maduo earthquake. The decline of the water level is associated with the changes in the stress within the epicentral region, such changes could provide long-term precursory information about an impending earthquake.

Study of the OLR Anomalies before the 2023 Turkey M7.8 Earthquake

Using the model of the additive tectonic stress from celestial tide-generating force, we studied the relationship between the seismogenic structure and celestial tide-generating stress in the M7.8 Turkey earthquake on 6 February 2023. We analyzed the daily continuous variation characteristics of OLR before and after the Turkey earthquake and discussed the correlation characteristics of tidal stress, OLR, and the earthquake. The results showed that the observed OLR anomaly according to the tidal stress variation cycle “C” (1–15 February) presented a phase change in time, which was synchronized with a continuous trough-to-peak change in the additional tectonic main pressure stress. The spatial distribution of OLR anomalies was mainly concentrated in the southwest section of the East Anatolian Fault Zone, which indicates that seismic tectonic movements were the main causes of OLR anomaly variation during this earthquake. An OLR anomaly change was related to this M7.8 “Swarm Type” of earthquake in Turkey. Impending earthquake OLR anomalies represent that the stress of the seismogenic structure in the seismogenic region has entered a critical state, which can provide stress monitoring and a seismogenic region indication for earthquakes induced by tidal force. The change cycle of the celestial tide-generating force provides a time indication for the identification of seismic thermal anomalies, and it indicates that the combination of the additional tectonic stress of the tidal force and the change of OLR anomaly has value for the research on the short-impending earthquake precursor.

Atmospheric precursors from multiple satellites associated with the 2020 Mw 6.5 Idaho (USA) earthquake

Remote sensing has became a powerful tool for identifying lithosphere and atmosphere anomalies associated with the impending Earthquakes (EQs) in the vicinity of seismic breeding zone. In this paper, Land Surface Temperature (LST) of both the daytime and nighttime from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP) and Outgoing Longwave Radiations (OLR) are studied for the 2020 Idaho (USA) EQ of Mw 6.5. We found the EQ induced surface and atmospheric parameters anomalies just one day prior to EQ main shock using statistical analysis. Moreover, we observed a sharp increment in LST and AT followed by the drop in both AP and RH, which are responsible for cooling the hot gases emitted from epicenter during preparation period. Also, we observed a large increase in OLR on the same day confirming these anomalous variations to be related with the main shock. Furthermore, these abrupt variations are also confirmed using neural networks (nonlinear autoregressive network with exogenous inputs (NARX), multilayer perceptron (MLP) and continuous wavelet transformation (CWT). These multi-parameter and multi-technique analyses can contribute to assist in the main shock forecasting in future with an enhanced cluster of satellite observations.

Time-frequency analysis of radon and thoron data using continuous wavelet transform

Continuous exposure to environmental radiation, whether it derives from natural or artificial sources, is thought to pose a substantial risk to public health. In addition to the health effects associated with prolonged exposure to environmental radiations, long-term measurements of these radiations can be used for a variety of beneficial purposes, such as the forecasting of impending earthquakes. Signal processing is an important application used for the purpose of forecasting. Wavelets, being signal-processing tools, are helpful in many applications such as anomaly detection in time series data. However, selection of the best wavelet for a particular application is still a problem that hasn’t found a satisfactory solution. In this study, we used continuous wavelet transform (CWT) on environmental radiations, specifically radon time series (RTS) and thoron time series (TTS) data, for the investigation of time-frequency information (TFI). The distribution of energy in the output wavelet decomposition have been investigated by several wavelet families such COIF4, DB4, SYM4 to detect frequency composition of signal and its relation with anomalies hidden in the observed data. Using discrete wavelet transform (DWT), specifically SYM4, DB4, and COIF4, we transformed the radon and thoron time series into a time-dependent sum of frequency components. Using CWT scalograms, the anomalies in the both of time series datasets (TSD) have been identified, and these anomalies have been associated with the seismic events that occurred during the period of the study. The results show that DB4 and SYM4 wavelets are good at identifying anomalies in original radon and thoron TSD, but SYM4 performs better for DWT-decomposed radon and thoron TSD.

The One and Only Successful Earthquake Prediction Method, a Great Historic Solution to Earthquake Prediction

This paper is original research, no other authors previously published papers referred. The most important parameter in earthquake prediction is earthquake precursors that have been successfully identified. During generation process of earthquakes weather anomalies are generated as precursors, in this way the earthquake precursors are earthquakes related. Based on atrocious weather anomalies and the corresponding devastating earthquakes can be verified even after 100 years. For the first time in the history of earthquake prediction, the satellite image of storm of East Pacific have been used to successfully predict earthquakes in California, the most significant in this observation study. As weather anomalies are generated by earthquake process, both individual and cluster of earthquakes are always preceded by weather anomalies, so, can be verified even after 100 years. In this paper, the deadly and devastating earthquakes of M7.8 killed over 55,000 people and cause damage cost over 100 USD, though predicted in time but ignored and satellite image of storm to predict California earthquakes are tabulated. Experts can use this data catalogue to warn impending earthquakes to mitigate loss of lives and properties with more improvements.

Characteristic analysis of earthquake sound signal based on wavelet transform

Earthquake is a kind of natural disaster with strong destructive power. As a kind of earthquake precursor signal, earthquake sound is of great significance of earthquake prediction. According to the characteristics of the earthquake sound signal, the earthquake sound monitoring data ten days before the earthquake was analysed using wavelet technology, the earthquake sound monitoring signals of different frequency bands was compared, and the change rule of the earthquake sound signal within ten days before the earthquake was emphatically studied. It was concluded that: the infrasound wave signal with normal fluctuation changed regularly into time; Ten days before the earthquake, the peak mutation of infrasonic signal occurred, which increased exponentially compared with the mean peak mutation value of earthquake sound signal. The number of this abnormal phenomenon increased to the shortening of the impending earthquake time, especially five days before the earthquake; The intensity of the earthquake sound signal in the frequency band of the infrasonic wave is gradually decreasing from the increase in the signal frequency. By studying the variation characteristics of earthquake sound signal with time before earthquake, it is of certain reference significance to study the correlation between abnormal ground earthquake signal and earthquake in earthquake prediction.

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw &gt; 6 Earthquakes in America

The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.