Search Results (10)

Understanding the process of earthquake preparation is of utmost importance in mitigating the potential damage caused by seismic events. That is why the study of seismic precursors is fundamental. However, the community studying non-seismic precursors relies on measurements, methods, and theories that lack a causal relationship with the earthquakes they claim to predict, generating skepticism among classical seismologists. Nonetheless, in recent years, a group has emerged that seeks to bridge the gap between these communities by applying fundamental laws of physics, such as the application of the second law of thermodynamics in multiscale systems. These systems, characterized by describing irreversible processes, are described by a global parameter called thermodynamic fractal dimension, denoted as $D$. A decrease in $D$ indicates that the system starts seeking to release excess energy on a macroscopic scale, increasing entropy. It has been found that the decrease in $D$ prior to major earthquakes is related to the increase in the size of microcracks and the emission of electromagnetic signals in localized zones, as well as the decrease in the ratio of large to small earthquakes known as the b-value. However, it is still necessary to elucidate how $D$, which is also associated with the roughness of surfaces, relates to other rupture parameters such as residual energy, magnitude, or fracture energy. Hence, this work establishes analytical relationships among them. Particularly, it is found that larger magnitude earthquakes with higher residual energy are associated with smoother faults. This indicates that the pre-seismic processes, which give rise to both seismic and non-seismic precursor signals, must also be accompanied by changes in the geometric properties of faults. Therefore, it can be concluded that all types of precursors (seismic or non-seismic), changes in fault smoothness, and the occurrence of earthquakes are different manifestations of the same multiscale dissipative system. Full article

Systematic measurements of water temperature are lacking but useful in understanding the relationship between water temperature and earthquakes. Based on the water temperature data, geological structure, borehole structure, and temperature gradient in the Jinjia well, Southwest China, we systematically analysed the water temperature changes related to earthquakes. The water temperature of the Jinjia well recorded the coseismic changes caused by the Wenchuan M7.9 and Panzhihua M6.1 earthquakes in 2008. We also found abnormal changes in the water temperature, after which moderate to strong earthquakes occurred in the surrounding region. The preseismic abnormal changes of the Jinjia well were rising-recovery (rising to a high value and continuing for a period of time before decreasing or quickly recovering), with the range of 0.007–0.07 °C. The maximum change (0.07 °C) occurred before the M7.9 Wenchuan earthquake in 2008. According to the Molchan error diagram, the most likely time for an earthquake to occur is within approximately 4 months after the water temperature exceeds the threshold temperature. In the Jinjia well, the installation depth of the temperature sensor affected the correlation between the temperature changes and earthquakes with a seismic energy density above 10⁻³ J·m⁻³. The shorter the distance between the sensor and the fault, the higher the probability of water temperature changes related to earthquakes. Full article

On 6 February 2023, at 1:17:34 UTC, a powerful Mw = 7.8 earthquake shook parts of Turkey and Syria. Investigating the behavior of different earthquake precursors around the time and location of this earthquake can facilitate the creation of an earthquake early warning system in the future. Total electron content (TEC) obtained from the measurements of GPS satellites is one of the ionospheric precursors, which in many cases has shown prominent anomalies before the occurrence of strong earthquakes. In this study, five classical and intelligent anomaly detection algorithms, including median, Kalman filter, artificial neural network (ANN)-multilayer perceptron (MLP), long short-term memory (LSTM), and ant colony optimization (ACO), have been used to detect seismo-anomalies in the time series of TEC changes in a period of about 4 months, from 1 November 2022 to 17 February 2023. All these algorithms show outstanding anomalies in the period of 10 days before the earthquake. The median method shows clear TEC anomalies in 1, 2 and, 3 days before the event. Since the behavior of the time series of a TEC parameter is complex and nonlinear, by implementing the Kalman filter method, pre-seismic anomalies were observed in 1, 2, 3, 5, and 10 days prior to the main shock. ANN as an intelligent-method-based machine learning also emphasizes the abnormal behavior of the TEC parameter in 1, 2, 3, 6, and 10 days before the earthquake. As a deep-learning-based predictor, LSTM indicates that the TEC value in the 10 days prior to the event has crossed the defined permissible limits. As an optimization algorithm, the ACO method shows behavior similar to Kalman filter and MLP algorithms by detecting anomalies 3, 7, and 10 days before the earthquake. In a previous paper, the author showed the findings of implementing a fuzzy inference system (FIS), indicating that the magnitude of the mentioned powerful earthquake could be predicted during about 9 to 1 day prior to the event. The results of this study also confirm the findings of another study. Therefore, considering that different lithosphere–atmosphere–ionosphere (LAI) precursors and different predictors show abnormal behavior in the time period before the occurrence of large earthquakes, the necessity of creating an earthquake early warning system based on intelligent monitoring of different precursors in earthquake-prone areas is emphasized. Full article

The 1884 Andalusia Earthquake, with an estimated magnitude between 6.2 and 6.7, is one of the most destructive events that shook the Iberian Peninsula, causing around 1200 casualties. According to paleoseismology studies and intensity maps, the earthquake source relates to the normal Ventas de Zafarraya Fault (Granada, Spain). Diverse studies registered and later analyzed hydrological effects, such as landslides, rockfalls, soil liquefaction, all-around surge and loss of springs, alterations in the phreatic level, discharge in springs and brooks and well levels, along with changes in physical and chemical parameters of groundwater. Further insight into these phenomena found an interplay between hydromechanical processes and crust surface deformations, conditions, and properties. This study focuses on analyzing and simulating the features involved in the major 1884 event and aims at elucidating the mechanisms concerning the mentioned effects. This ex-post analysis builds on the qualitative effects and visible alterations registered by historical studies. It encompasses conceptual geological and kinematic models and a 2D finite element simulation to account for the processes undergone by the Zafarraya Fault. The study focuses on the variability of hydromechanical features and the time evolution of the ground pore–pressure distribution in both the preseismic and coseismic stages, matching some of the shreds of evidence found by field studies. This procedure has helped to shed light on the causal mechanisms and better understand some parameters of this historical earthquake, such as its hypocenter and magnitude. This methodology can be applied to other events registered in the National Catalogues of Earthquakes to achieve a deeper insight, further knowledge, and a better understanding of past earthquakes. Full article

Nowadays, there has been a growing interest in understanding earthquake forerunners, i.e., anomalous variations that are possibly associated with the complex process of earthquake evolution. In this context, the Robust Satellite Technique was coupled with 10 years (2012–2021) of daily night-time MODIS-Land Surface Temperature remote sensing data to detect thermal anomalies likely related to the 27 September 2021, strong onshore earthquake of magnitude Mw6.0 occurring near the Arkalochori village in Central Crete, Greece. Eight intense (signal-to-noise ratio > 3) and infrequent, quite extensive, and temporally persistent thermal signal transients were detected and characterized as pre-seismic anomalies, while one thermal signal transient was identified as a co-seismic effect on the day of the main tectonic event. The thermal anomalies dataset was combined with tectonic parameters of Central Crete, such as active faults and fault density, seismogenic zones and ground displacement maps produced using Sentinel-1 satellite imagery and the Interferometric Synthetic Aperture Radar technique. Regarding the thermal anomaly of 27 September, its greatest portion was observed over the footwall part of the fault where a significant subsidence up to 20 cm exists. We suggest that the thermal anomalies are possibly connected with gas release which happens due to stress changes and is controlled by the existence of tectonic lines and the density of the faults, even if alternative explanations could not be excluded. Full article

Low Earth orbit satellites collect and study information on changes in the ionosphere, which contributes to the identification of earthquake precursors. Swarm, the European Space Agency three-satellite mission, has been launched to monitor the Earth geomagnetic field, and has successfully shown that in some cases it is able to observe many several ionospheric perturbations that occurred as a result of large earthquake activity. This paper proposes the SafeNet deep learning framework for detecting pre-earthquake ionospheric perturbations. We trained the proposed model using 9017 recent (2014–2020) independent earthquakes of magnitude 4.8 or greater, as well as the corresponding 7-year plasma and magnetic field data from the Swarm A satellite, and excellent performance has been achieved. In addition, the influence of different model inputs and spatial window sizes, earthquake magnitudes, and daytime or nighttime was explored. The results showed that for electromagnetic pre-earthquake data collected within a circular region of the epicenter and with a Dobrovolsky-defined radius and input window size of 70 consecutive data points, nighttime data provided the highest performance in discriminating pre-earthquake perturbations, yielding an F1 score of 0.846 and a Matthews correlation coefficient of 0.717. Moreover, SafeNet performed well in identifying pre-seismic ionospheric anomalies with increasing earthquake magnitude and unbalanced datasets. Hypotheses on the physical causes of earthquake-induced ionospheric perturbations are also provided. Our results suggest that the performance of pre-earthquake ionospheric perturbation identification can be significantly improved by utilizing SafeNet, which is capable of detecting precursor effects within electromagnetic satellite data. Full article

The groundwater level might be adopted as a useful tool to explore pre-seismic stress change in the earth crust, because it circulates in the deep crust and should be altered by the processes associated with the preparation of earthquakes. This work makes a new attempt that applies the load/unload response ratio (LURR) technique to study the stress state of the source media before the large earthquakes by calculating the ratio between the water levels during the loading and unloading phases. The change of Coulomb failure stress induced by earth tides in the tectonically preferred slip direction on the fault surface of the mainshock is adopted for differentiating the loading and unloading periods. Using this approach, we tested the groundwater level in the wells near the epicenters of some large earthquakes that occurred in the Sichuan-Yunnan region of southwest China. Results show that the LURR time series fluctuated narrowly around 1.0 for many years and reached anomalously high peaks 2~8 months prior to the mainshocks. For the earthquakes with multiple observation wells, the magnitude of the maximum values decreases with the distance from the epicenter. The underlying physics of these changes should be caused by the pre-seismic dilatancy. The corresponding volume variations in the crust could be observed in the geodetic time series in the same neighborhoods and during the same period. Full article

This study proposes a workflow that enables the accurate identification of earthquake-induced damage zones by using coherence image pairs of the Sentinel-1 satellite before and after an earthquake event. The workflow uses interferometric synthetic aperture radar (InSAR) processing to account for coherence variations between coseismic and preseismic image pairs. The coherence difference between two image pairs is useful information to detect specific disasters in a regional-scale area after an earthquake event. To remove background effects such as the atmospheric effect and ordinal surface changes, this study employs the two-step threshold method to develop the coseismic coherence difference (CCD) map for our analyses. Thirty-four Sentinel-1 images between January 2015 and February 2016 were collected to process 30 preseismic image pairs and two coseismic image pairs for assessing multiple types of disasters in Tainan City of southwestern Taiwan, where severe damages were observed after the Meinong earthquake event. The coseismic unwrapping phases were further calculated to estimate the surface displacement in east-west and vertical directions. Results in the CCD map agree well with the observations from post-earthquake field surveys. The workflow can accurately identify earthquake-induced land subsidence and surface displacements, even for areas with insufficient geological data or for areas that had been excluded from the liquefaction potential map. In addition, the CCD details the distribution of building damages and structure failures, which might be useful information for emergency actions applied to regional-scale problems. The conversion of 2D surface displacement reveals the complex behavior of geological activities during the earthquake. In the foothill area of Tainan City, the opposite surface displacements in local areas might be influenced by the axis activities of the Kuanmiao syncline. Full article

The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well. Full article

Unusual behavior before earthquakes has been reported for millennia but no plausible mechanism has been identified. One possible way in which animals could be affected by pre-earthquake processes is via stress activated positive holes leading to the formation of hydrogen peroxide at the rock water interface. Aquatic and fossorial animals could be irritated by H₂O₂ and move down the concentration gradient. Here, we carry out avoidance tests with hydrogen peroxide in two model organisms; Daphnia pulex and earthworms. Daphnia were found to move away from increasing concentrations of H₂O₂ but earthworms appeared unaffected. It is possible that earthworm swarming behavior, reported frequently before earthquakes, is caused by electric field shifts or another unknown mechanism, whereas zooplankton may be affected by increasing levels of H₂O₂. Full article