Thermal Radiation Anomalies Associated with Major Earthquakes

Recent developments of remote sensing methods for Earth satellite data analysis contribute to our understanding of earthquake related thermal anomalies. It was realized that the thermal heat fluxes over areas of earthquake preparation is a result of air ionization by radon (and other gases) and consequent water vapor condensation on newly formed ions. Latent heat (LH) is released as a result of this process and leads to the formation of local thermal radiation anomalies (TRA) known as OLR (outgoing Longwave radiation, Ouzounov et al, 2007). We compare the LH energy, obtained by integrating surface latent heat flux (SLHF) over the area and time with released energies associated with these events. Extended studies of the TRA using the data from the most recent major earthquakes allowed establishing the main morphological features. It was also established that the TRA are the part of more complex chain of the short-term pre-earthquake generation, which is explained within the framework of a lithosphere-atmosphere coupling processes

Application of Model-Based Time Series Prediction of Infrared Long-Wave Radiation Data for Exploring the Precursory Patterns Associated with the 2021 Madoi Earthquake

Taking the Madoi MS 7.4 earthquake of 21 May 2021 as an example, this paper proposes using time series prediction models to predict the outgoing long-wave radiation (OLR) anomalies and study short-term pre-earthquake signals. Five time series prediction models, including autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM), were trained with the OLR time series data of the aseismic moments in the 5° × 5° spatial range around the epicenter. The model with the highest prediction accuracy was selected to retrospectively predict the OLR values during the aseismic period and before the earthquake in the area. It was found, by comparing the predicted time series values with the actual time series value, that the similarity indexes of the two time series before the earthquake were lower than the index of the aseismic period, indicating that the predicted time series before the earthquake significantly differed from the actual time series. Meanwhile, the temporal and spatial distribution characteristics of the anomalies in the 90 days before the earthquake were analyzed with a 95% confidence interval as the criterion of the anomalies, and the following was found: out of 25 grids, 18 grids showed anomalies—the anomalies of the different grids appeared on similar dates, and the anomalies of high values appeared centrally at the time of the earthquake, which supports the hypothesis that pre-earthquake signals may be associated with the earthquake

Study of outgoing longwave radiation anomalies associated with Haiti earthquake

The paper presents an analysis by using the methods of Eddy field calculation mean and wavelet maxima to detect seismic anomalies within the outgoing longwave radiation (OLR) data based on time and space. The distinguishing feature of the method of Eddy field calculation mean is that we can calculate "the total sum of the difference value" of "the measured value" between adjacent points, which could highlight the singularity within data. The identified singularities are further validated by wavelet maxima, which using wavelet transformations as data mining tools by computing the maxima that can be used to identify obvious anomalies within OLR data. The two methods has been applied to carry out a comparative analysis of OLR data associated with the earthquake recently occurred in Haiti on 12 January 2010. Combining with the tectonic explanation of spatial and temporal continuity of the abnormal phenomena, the analyzed results have indicated a number of singularities associated with the possible seismic anomalies of the earthquake and from the comparative experiments and analyses by using the two methods, which follow the same time and space, we conclude that the singularities observed from 19 to 24 December 2009 could be the earthquake precursor of Haiti earthquake

Earth’s Outgoing Longwave Radiation Variability Prior to M ≥6.0 Earthquakes in the Taiwan Area During 2009–2019

This paper proposes an analysis method, using the National Oceanic and Atmospheric Administration satellite data, to trace variations in outgoing longwave radiation (OLR) for finding the precursors of earthquakes. The significance of these observations is investigated using data sets of recent M ≥6.0 earthquakes around the Taiwan area from 2009 to 2019. We suggest that the precursory signal could be an EIndex anomaly (EA) in the form of substantial thermal releases distributed near the epicenter. The consecutive appearances of OLR EAs are observed as precursors 2–15 days before significant earthquakes, and we refer to this as a pre-earthquake OLR EIndex anomaly (POEA). We interpret these thermal sources as possibly originating from electromagnetics together with gas emissions associated with pre-seismic processes. This study highlights the potential of OLR anomalous changes in earthquake precursor studies, at least in the Taiwan region

Atmospheric Signals Associated with Major Earthquakes. A Multi-Sensor Approach

We are studying the possibility of a connection between atmospheric observation recorded by several ground and satellites as earthquakes precursors. Our main goal is to search for the existence and cause of physical phenomenon related to prior earthquake activity and to gain a better understanding of the physics of earthquake and earthquake cycles. The recent catastrophic earthquake in Japan in March 2011 has provided a renewed interest in the important question of the existence of precursory signals preceding strong earthquakes. We will demonstrate our approach based on integration and analysis of several atmospheric and environmental parameters that were found associated with earthquakes. These observations include: thermal infrared radiation, radon! ion activities; air temperature and humidity and a concentration of electrons in the ionosphere. We describe a possible physical link between atmospheric observations with earthquake precursors using the latest Lithosphere-Atmosphere-Ionosphere Coupling model, one of several paradigms used to explain our observations. Initial results for the period of2003-2009 are presented from our systematic hind-cast validation studies. We present our findings of multi-sensor atmospheric precursory signals for two major earthquakes in Japan, M6.7 Niigata-ken Chuetsu-oki of July16, 2007 and the latest M9.0 great Tohoku earthquakes of March 11,201

Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018

Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M >6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected

Towards Advancing the Earthquake Forecasting by Machine Learning of Satellite Data

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases

Диагностирование аномалий температуры атмосферы в сейсмически активных регионах Азии по спутниковым данным

Results of diagnosis of temperature anomalies based on retrospective analysis of long-term timeseries of temperature in separation zone of troposphere and stratosphere over epicenters of 10 strong earthquakes with magnitude of M>6.5 that occurred in different seismically active regions of Asia are presented. Anomalous temperature perturbations were observed in all the studied cases 1-8 days before the main seismic eventПредставлены результаты диагностирования аномалий температуры, основанные на ретроспективном анализе долговременных спутниковых данных в зоне раздела тропосферы и стратосферы над эпицентрами 10 сильных землетрясений магнитудой M>6,5, произошедших в различных сейсмически активных регионах Азии. Аномальные возмущения температуры наблюдались во всех рассмотренных случаях за 1−8 дней до основного сейсмического событи

Principles of Organizing Earthquake Forecasting Based on Multiparameter Sensor-WEB Monitoring Data

The paper describes an approach that allows, basing on the data of multiparameter monitoring of atmospheric and ionospheric parameters and using ground-based and satellite measurements, to select from the data stream a time interval indicating the beginning of the final stage of earthquake preparation, and finally using intelligent data processing to carry out a short-term forecast for a time interval of 2 weeks to 1 day before the main shock. Based on the physical model of the lithosphere-atmospheric-ionospheric coupling, the precursors are selected, the ensemble of which is observed only during the precursory periods, and their identification is based on morphological features determined by the physical mechanism of their generation, and not on amplitude selection based on statistical data processing. Basing on the developed maquette of the automatic processing service, the possibility of real-time monitoring of the situation in a seismically active region will be demonstrated using the territory of the Kamchatka region and the Kuril Islands