Search Results (23)

The b-value is a critical parameter for gauging seismic activity and is essential for seismic hazard assessment, monitoring stress evolution in focal zones, and forecasting major earthquakes. The minimum magnitude of completeness ($M_c$), a key indicator of the completeness of an earthquake catalog, reflects the monitoring capability of a seismic network and serves as a crucial foundation for the accurate calculation of the b-value. We began by integrating multi-source earthquake catalogs for mainland China using the nearest-neighbor method. Building on this, we employed a combination of partitioned time-series analysis and a grid-based spatial scanning technique to systematically investigate the spatiotemporal evolution of the $M_c$ and the b-value across mainland China and its adjacent regions. Our findings indicate the following: (1) Since the 1980s, the overall trend of $M_c$ has shifted from high and unstable values to low and stable ones. However, significant earthquake events can cause a notable short-term increase in the $M_c$. (2) The b-value exhibits strong fluctuations, primarily influenced by the dual effects of the tectonic stress field and catalog completeness. These fluctuations are particularly pronounced in highly active seismic regions such as the Sichuan–Yunnan area and Taiwan, whereas the western Tibetan Plateau has consistently maintained a low b-value. (3) The spatial distributions of both the $M_c$ and the b-value are markedly heterogeneous. By developing a unified and complete earthquake catalog for mainland China, our research highlights the qualitative leap in monitoring capabilities brought about by the continuous densification and technological upgrading of seismic networks. This dataset provides a solid foundation for future seismological research, disaster prevention practices, and especially for the development of AI-based earthquake prediction models. Full article

Earthquake prediction remains a significant challenge in seismology, and advancements in signal processing techniques have opened new avenues for improving prediction accuracy. This paper explores the application of Time–Frequency Distributions (TFDs) to seismic signals to identify diagnostic precursory patterns of major earthquakes. TFDs provide a comprehensive analysis of the non-stationary nature of seismic data, allowing for the identification of precursory patterns based on energy concentration features. Current earthquake prediction models primarily focus on long-term forecasts, predicting events by identifying a cycle in historical data, or on nowcasting, providing alerts seconds after a quake has begun. However, both approaches offer limited utility for disaster management, compared to short-term earthquake prediction methods. This paper proposes a new possible precursory pattern of major earthquakes, tested through analysis of recent major earthquakes and their respective prior minor earthquakes for five earthquake-prone countries, namely Türkiye, Indonesia, the Philippines, New Zealand, and Japan. Precursors in the time–frequency domain have been consistently identified in all datasets within several hours or a few days before the major earthquakes occurred, which were not present in the observation and analysis of the earthquake catalogs in the time domain. This research contributes towards the ongoing efforts in earthquake prediction, highlighting the potential of quadratic non-linear TFDs as a significant tool for non-stationary seismic signal analysis. To the best of the authors’ knowledge, no similar approach for consistently identifying earthquake diagnostics precursors has been proposed, and, therefore, we propose a novel approach in reliable earthquake prediction using TFD analysis. Full article

Long-term observations in wells make it possible to study changes in groundwater pressure/level during individual earthquakes (seismo-hydrogeodynamic effects—SHGEs) over a wide range of periods of their manifestation. Information on the morphological features and durations of the SHGEs together with data on earthquake parameters form the basis for creating the unique typifications of SHGEs for individual observation wells. With reliable verification, such SHGE typifications provide the practical use of well observation data to predict strong earthquakes and assess their impact on groundwater. During long-term (1996–2022) precision observations of pressure/water level variations in wells of the Petropavlovsk–Kamchatsky test site (Kamchatka Peninsula, northwest Pacific seismic belt), SHGE typifications describing the manifestations of various types of SHGEs at the earthquakes in ranges of magnitudes M_w = 5.0–9.1 and epicentral distances d_e = 80–14,600 km were developed. At the same time, the issue of verifying created SHGE typifications for individual wells in relation to the strongest and closest earthquakes, accompanied by noticeable tremors in the observation area, is relevant. On 3 April 2023, an earthquake, M_w = 6.6 (EQ), occurred at an epicentral distance d_e = 67–77 km from observation wells. Various changes in the groundwater pressure/level were recorded in the wells: oscillations and other short-term and long-term effects of seismic waves, coseismic jumps in water pressure caused by a change in the static stress state of water-bearing rocks during the formation of rupture in the earthquake source, and supposed hydrogeodynamic precursors. The EQ was used to verify the SHGE typifications for wells YuZ-5 and E-1 with the longest observation series of more than 25 years. In these wells, the seismo-hydrogeodynamic effects recorded during the EQ corresponded to the previously observed SHGE during the two strongest earthquakes with M_w = 7.2, d_e = 80 km and M_w = 7.8, d_e = 200 km. This correspondence is considered an example of the experimental verification of previously created SHGE typifications in individual wells in relation to the most powerful earthquakes in the wells’ area. Updated SHGE typifications for wells E-1 and YuZ-5 are presented, showing the patterns of water level/pressure changes in these wells depending on earthquake parameters and thereby increasing the practical significance of well observations for assessing earthquake consequences for groundwater, searching for hydrogeodynamic precursors and forecasting strong earthquakes. The features of the hydrogeodynamic precursor manifesting in the water level/pressure lowering with increased rates in well E-1 before earthquakes with M_w ≥ 5.0 at epicentral distances of up to 360 km are considered. A retrospective statistical analysis of the prognostic significance of this precursor showed that its use for earthquake forecasting increases the efficiency of predicting earthquakes with M_w ≥ 5.0 by 1.55 times and efficiency of predicting earthquakes with M_w ≥ 5.8 by 2.34 times compared to random guessing. This precursor was recorded during the 92 days before the EQ and was identified in real time with the issuance of an early prognostic conclusion on the possibility of a strong earthquake to the Kamchatka branch of the Russian Expert Council for Earthquake Forecasting. Full article

As the forefront of inland extension on the Indian plate, the northeastern Tibetan Plateau, marked by low strain rates and high stress levels, is one of the regions with the highest seismic risk. Analyzing seismicity through statistical methods holds significant scientific value for understanding tectonic conditions and assessing earthquake risk. However, seismic monitoring capacity in this region remains limited, and earthquake frequency is low, complicating efforts to improve earthquake catalogs through enhanced identification and localization techniques. Bi-scale empirical probability integral transformation (BEPIT), a statistical method, can address these data gaps by supplementing missing events shortly after moderate to large earthquakes, resulting in a more reliable statistical data set. In this study, we analyzed six earthquake sequences with magnitudes of M_S ≥ 6.0 that occurred in northeastern Tibet since 2009, following the upgrade of the regional seismic network. Using BEPIT, we supplemented short-term missing aftershocks in these sequences, creating a more complete earthquake catalog. ETAS model parameters and b values for these sequences were then estimated using maximum likelihood methods to analyze parameter variability across sequences. The findings indicate that the b value is low, reflecting relatively high regional stress. The background seismicity rate is very low, with most mainshocks in these sequences being background events rather than foreshock-driven events. The p-parameter of the ETAS model is high, indicating that aftershocks decay relatively quickly, while the α-parameter is also elevated, suggesting that aftershocks are predominantly induced by the mainshock. These conditions suggest that earthquake prediction in this region is challenging through seismicity analysis alone, and alternative approaches integrating non-seismic data, such as electromagnetic and fluid monitoring, may offer more viable solutions. This study provides valuable insights into earthquake forecasting in the northeastern Tibetan Plateau. Full article

This study conducts a comprehensive analysis of observations related to the M_L6.2 Hualien earthquake that struck eastern Taiwan on 6 February 2018, focusing particularly on gamma-ray emissions and total electron content (TEC) as earthquake precursors. Prior research has shown that significant gamma-ray enhancements are frequently detected at the YMSG (Yangmingshan gamma-ray) station prior to major earthquakes in eastern and northeastern Taiwan, suggesting that gamma-ray anomalies may serve as reliable indicators for identifying seismic precursors in this area. Our findings reveal a significant rise in gamma-ray emissions at the YMSG station from 19 January to 4 February 2018, which corresponds to a precursor period of approximately 18 days before the Hualien earthquake. Positive and negative TEC anomalies were observed in Taiwan on 20–21 January and 5 February, respectively, and may be considered as ionospheric precursors to the earthquake. Additionally, deep-learning techniques applied to TEC data facilitate the detection of ionospheric precursors associated with the Hualien earthquake, enabling forecasts of an approaching seismic event. Collectively, these observations indicate that all identified anomalies are regarded as short-term precursors, explicable through the theoretical framework of lithosphere–ionosphere coupling (LIC). Full article

The paper presents the author’s vision of the problem of earthquake hazards from the physical point of view. The first part is concerned with the processes of precursor’s generation. These processes are a part of the complex system of the lithosphere–atmosphere–ionosphere–magnetosphere coupling, which is characteristic of many other natural phenomena, where air ionization, atmospheric thermodynamic instability, and the Global Electric Circuit are involved in the processes of the geosphere’s interaction. The second part of the paper is concentrated on the reliable precursor’s identification. The specific features helping to identify precursors are separated into two groups: the absolute signatures such as the precursor’s locality or equatorial anomaly crests generation in conditions of absence of natural east-directed electric field and the conditional signatures due to the physical uniqueness mechanism of their generation, or necessity of the presence of additional precursors as multiple consequences of air ionization demonstrating the precursor’s synergy. The last part of the paper is devoted to the possible practical applications of the described precursors for purposes of the short-term earthquake forecast. A change in the paradigm of the earthquake forecast is proposed. The problem should be placed into the same category as weather forecasting or space weather forecasting. Full article

Electromagnetic indices play a potential role in the forecast of short-term to imminent M ≥ 5.5 earthquakes and have good application prospects. However, despite possible progress in earthquake forecasting, concerns remain because it is difficult to obtain accurate epicenter forecasts based on different forecast indices, and the forecast time span is as large as months in areas with multiple earthquakes. In this study, based on the actual demand for short-term earthquake forecasts in the Gansu–Qinghai–Sichuan region of western China, we refined the construction of earthquake forecast indicators in view of the abundant electromagnetic anomalies before moderate and strong earthquakes. We revealed the advantageous forecast indicators of each method for the three primary earthquake elements (time, epicenter, magnitude) and the spatiotemporal evolution characteristics of the anomalies. The correlations between the magnitude, time, intensity, and electromagnetic anomalies of different M ≥ 5.5 earthquakes indicate that the combination of short-term electromagnetic indices is pivotal in earthquake forecasting. Full article

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies. Full article

The statistical analysis of the impact of the top 50 X-class solar flares (1997–2024) on global seismic activity as well as on the earthquake preparation zones located in the illuminated part of the globe and in an area of 5000 km around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases, an increase in seismicity is observed, especially in the region around the subsolar point (up to 33%) during the 10 days after the solar flare in comparison with the preceding 10 days. The case study of the aftershock sequence of a strong M_w = 9.1 earthquake (Sumatra–Andaman Islands, 26 December 2004) after the solar flare of X10.16 class (20 January 2005) demonstrated that the number of aftershocks with a magnitude of M_w ≥ 2.5 increases more than 17 times after the solar flare with a delay of 7–8 days. For the case of the Darfield earthquake (M_w = 7.1, 3 September 2010, New Zealand), it was shown that X-class solar flares and M probably triggered two strong aftershocks (M_w = 6.1 and M_w = 5.9) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from the point of view of the fault electrical conductivity and orientation. Based on the obtained results, the possible application of natural electromagnetic triggering of earthquakes is discussed for the earthquake forecast using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites. Full article

A seismic hazard can be quantified by using probabilities. Modern seismic forecasting models (e.g., Operational Earthquake Forecasting systems) allow us to quantify the short-term variations in such probabilities. Indeed these probabilities change with time and space, in particular after strong seismic events. However, the short-term seismic hazard could also change during seismic swarms, i.e., a sequence with several small-/medium-sized events. The goal of this work is to quantify these changes, using the Italian Operational Earthquake Forecasting system, and also estimate the variations in the Gutenberg–Richter b-value. We focus our attention on three seismic swarms that occurred in Central Italy in October–November 2023. Our results indicate that short-term variations in seismic hazard are limited, less than an order of magnitude, and also that b-value variations are not significant. Placing our findings in a more general context, we can state that according to currently available models and catalogs, the occurrence of seismic swarms does not significantly affect the short-term seismic hazard. Full article

This manuscript intends to demonstrate the diagnostic value of the previously discussed integrated parameter called atmospheric chemical potential (ACP) for tracking the atmospheric anomalies before strong earthquakes generated by the chain of processes initiated by air ionization due to radon emanation from the Earth’s crust. For this purpose, we considered several kinds of critical processes in the atmosphere using the ACP as an indicator and diagnostic tool: hurricane dynamics, the effects of radioactive pollution (the Chernobyl NPP accident), volcano eruptions and pre-earthquake atmospheric anomalies. We established that in all cases, some unusual features of the studied critical processes were revealed to be invisible when using certain methods of monitoring. This means that the application of ACP may improve the operative monitoring of the critical processes in atmosphere. In the cases of volcano eruptions and earthquakes, ACP can be used for short-term forecast. Full article

We designed a convolutional neural network application to detect seismic precursors in geomagnetic field records. Earthquakes are among the most destructive natural hazards on Earth, yet their short-term forecasting has not been achieved. Stress loading in dry rocks can generate electric currents that cause short-term changes to the geomagnetic field, yielding theoretically detectable pre-earthquake electromagnetic emissions. We propose a CNN model that scans windows of geomagnetic data streams and self-updates using nearby earthquakes as labels, under strict detectability criteria. We show how this model can be applied in three key seismotectonic settings, where geomagnetic observatories are optimally located in high-seismicity-rate epicentral areas. CNNs require large datasets to be able to accurately label seismic precursors, so we expect the model to improve as more data become available with time. At present, there is no synthetic data generator for this kind of application, so artificial data augmentation is not yet possible. However, this deep learning model serves to illustrate its potential usage in earthquake forecasting in a systematic and unbiased way. Our method can be prospectively applied to any kind of three-component dataset that may be physically connected to seismogenic processes at a given depth. Full article

Nearly 20 years ago, the observation that major earthquakes are generally preceded by an increase in the seismicity rate on a timescale from months to decades was embedded in the “Every Earthquake a Precursor According to Scale” (EEPAS) model. EEPAS has since been successfully applied to regional real-world and synthetic earthquake catalogues to forecast future earthquake occurrence rates with time horizons up to a few decades. When combined with aftershock models, its forecasting performance is improved for short time horizons. As a result, EEPAS has been included as the medium-term component in public earthquake forecasts in New Zealand. EEPAS has been modified to advance its forecasting performance despite data limitations. One modification is to compensate for missing precursory earthquakes. Precursory earthquakes can be missing because of the time-lag between the end of a catalogue and the time at which a forecast applies or the limited lead time from the start of the catalogue to a target earthquake. An observed space-time trade-off in precursory seismicity, which affects the EEPAS scaling parameters for area and time, also can be used to improve forecasting performance. Systematic analysis of EEPAS performance on synthetic catalogues suggests that regional variations in EEPAS parameters can be explained by regional variations in the long-term earthquake rate. Integration of all these developments is needed to meet the challenge of producing a global EEPAS model. Full article

We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements. Full article

The investigation of short-term earthquake-clustering features is made feasible through the application of a purely stochastic Epidemic-Type Aftershock Sequence (ETAS) model. The learning period that is used for the estimation of the parameters is composed by earthquakes with M ≥ 2.6 that occurred between January 2008 and May 2017. The model predictability is retrospectively examined for the 12 June 2017 Lesvos earthquake (M_w6.4) and the subsequent events. The construction of time-dependent seismicity maps and comparison between the observed and expected earthquake number are performed in order to temporally and spatially test the evolution of the sequence, respectively. The generation of 127 target events with M ≥ 3.0 in the period June–July 2017, just before the main shock occurrence, is examined in a quantitative evaluation. The statistical criteria used for assessing the model performance are the Relative Operating Characteristic Diagram, the R-score, and the probability gain. Reliable forecasts are provided through the epidemic model testifying its superiority towards a time-invariant Poisson model. Full article