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The paper presents the results of retrospective and tectonophysical analysis of the Tokachi-Oki earthquake source area on the September 25, 2003 (Mw = 8.3). Particular attention is paid to the Hidaka tectonic belt, the structures of which were affected by the Tokachi-Oki earthquake. The load–unload response ratio (LURR) method was applied at sufficient spatial (a square with a side of about 400 km) and temporal (continuous 20-year period) intervals to consider the results reliable. The forerunners of the Tokachi-Oki earthquake on the September 25, 2003, were revealed (on average, two years before the seismic event), which strictly determined the earthquake location. The data obtained with The method of cataclastic analysis (MCA), together with LURR data, made it possible to establish the stages of instability development in the Hidaka collision zone. The Hidaka tectonic belt became outlined by destruction zones (mainly horizontal compression) from 1997 to 2003 (up to the Tokachi-Oki earthquake). LURR anomalies were detected along the belt a couple of years before the earthquake. The prediction of the Tokachi-Oki earthquake by the LURR method is one of the most reliable in our practice. [ABSTRACT FROM AUTHOR]

The temporal distribution of earthquakes provides important basis for earthquake prediction and seismic hazard analysis. The relatively limited records of strong earthquakes have often made it difficult to study the temporal distribution models of regional strong earthquakes. However, there are hundreds of years of complete strong earthquake records in the North China Seismic Zone, providing abundant basic data for studying temporal distribution models. Using the data of M ≥ 6.5 earthquakes in North China as inputs, this paper estimates the model parameters using the maximum likelihood method with Poisson, Gamma, Weibull, Lognormal and Brownian passage time (BPT) distributions as target models. The optimal model for describing the temporal distribution of earthquakes is determined according to Akaike information criterion (AIC),and Kolmogorov–Smirnov test (K–S test). The results show that Lognormal and BPT models perform better in describing the temporal distribution of strong earthquakes in North China. The mean recurrence periods of strong earthquakes (M ≥ 6.5) calculated based on these two models are 8.1 years and 13.2 years, respectively. In addition, we used the likelihood profile method to estimate the uncertainty of model parameters. For the BPT model, the mean and 95% confidence interval of recurrence interval μ is 13.2 (8.9–19.1) years, and the mean and 95% confidence interval of α is 1.29 (1.0–1.78). For the Lognormal model, the mean value and 95% confidence interval of v is 2.09 (1.68–2.49), the mean value exp (v) corresponding to earthquake recurrence interval is 8.1 (5.4–12.1) years. In this study, we also calculated the occurrence probability of M ≥ 6.5 earthquakes in the North China Seismic Zone in the future, and found that the probability and 95% confidence interval in the next 10 years based on the BPT model is 35.3% (26.8%-44.9%); the mean value and 95% confidence interval of earthquake occurrence probability based on the Lognormal distribution is 35.4% (22.9%-49.7%); the mean probability and 95% confidence interval based on the Poisson model is 53.1% (41.1%-64%). The results of this study may provide important reference for temporal distribution model selection and earthquake recurrence period calculation in future seismic hazard analysis in North China. [ABSTRACT FROM AUTHOR]

This paper reports results of mathematical modeling applied to the stress and strain in epicentral zones before and after the large earthquakes that occurred on June 22, 2002 in the Qazvin Province, northwestern Iran (Mw = 6.4) and the Gujarat, India earthquake of January 26, 2001 (M = 6.9). The modeling relied on a method for calculating stress and strain in a blocky elastic isotropic heterogeneous medium disturbed by a set of faults that are due to an external tectonic stress field. The boundary conditions were specified based on geological and seismological data. It has been shown that the epicenters of large crustal earthquakes occur in zones of high stress concentration at the ends of tectonic faults. Rupture occurs when the relationship between acting tectonic stresses satisfies the requirement σyy/σxx >3, thus connecting zones of high stress. The evolution of the aftershock process is controlled by the stress drop caused by a new rupture, while the resulting aftershock clusters are in spatial correlation with the stress drop zone. The new rupture propagates in the direction of dominant tectonic fault orientation in the region. We show a relationship to exist between rupture length and the possible retrospectively predicted location and magnitude of the earthquake depending on the elastic energy buildup and its possible release under specified structural tectonic conditions. [ABSTRACT FROM AUTHOR]

This paper is the first in a series of studies generalizing the Map of Expected Earthquakes (MEE) medium-term earthquake forecast method and analyzing the prospects for its further improvement. Over the past 35 years, the MEE method has been used in seismically active regions around the world with different tectonic conditions and seismic regimes. The average prognostic efficiency of the algorithm was shown to be 2.5 times higher than for random guessing. The MEE can be further improved and upgraded. The new methods of data processing to be implemented into the upgraded MEE will significantly increase the amount of analyzed data, take into account the specific signs of the seismic process at different depths, use lineament–domain–focal (LDF) models of the structure of seismically active zones as stationary prognostic criteria, and build three-dimensional distributions of the probability of strong earthquakes. The improvement will involve an expansion of the list of used seismological precursors that have a physically justified link with the earthquake preparation process and the development of formalized techniques for identifying these precursors in the prognostic practice. [ABSTRACT FROM AUTHOR]

We consider some characteristics of seismicity in the region of Southwest Alaska. The aftershock zones of the Simeonof (July 22, 2020, Mw = 7.8) and Chignik (July 29, 2021, Mw = 8.2) earthquakes are identified. The characteristics of ring-shaped seismicity structures, which formed in a few decades prior to the Chignik earthquake, are described. The location and magnitude of a large event, which could be prepared to the east of the Simeonof earthquake rupture zone, was predicted in (Kopnichev and Sokolova, 2021a) based on the parameters of these structures. An estimate of the expected magnitude (Mw = 8.2 ± 0.2) was obtained using correlation dependences between the parameters of ring-shaped structures and the magnitudes of large and great earthquakes that occurred in the East Pacific region. This forecast, which was made on the basis of the data obtained prior to October 21, 2020, was presented in the mentioned paper, which was accepted for publication before July 29, 2021. We consider possible changes of the parameters of ring-shaped structures immediately prior to the Chignik earthquake and discuss geodynamic processes that can lead to the formation of ring-shaped structures. [ABSTRACT FROM AUTHOR]

The Philippines, as part of the Circum-Pacific belt, is considered as one of the most seismically active countries in the world. Earthquake occurrence is frequent and its effects vary depending on its size. Understanding how the occurrences happen is therefore important. Stochastic models of earthquake occurrence have been used to study seismic activities in various active earthquake zones globally. In this paper, we apply Poisson hidden Markov models (PHMM) using the January 1, 1960 to January 20, 2019 earthquake data of Metro Manila, Philippines. The parameters in the models are estimated using expectation-maximization (EM) algorithm. We determine using various statistical tests that the 5-state PHMM best represents the earthquake data and implement bootstrap algorithm to validate the acceptability of its parameter estimates. Moreover, we investigate the forecasting capability of the 5-state PHMM by comparing it to the ARIMA model. Using unscaled mean bounded relative absolute error (UMBRAE), we find that the 5-state PHMM gives closer one-step ahead forecasts and is a better forecasting model for the considered data. [ABSTRACT FROM AUTHOR]

This paper is an analysis of the seismic process in the source zones of great (M ~ 8) earthquakes occurring at the Pacific seismic belt and in Southeast Asia. We sought to enhance data homogeneity for different seismic regions by proposing the concept of a generalized energy class of earthquakes. The concept is a promising alternative to the existing procedure for the energy classification of earthquakes. We consider one important characteristic of the source zone of a great earthquake, namely, the energy spectrum of the seismic process; the Gutenberg–Richter law is generally treated as a first approximation to this concept. We have proposed a method for the study of the concept, have identified a significant peaking growth of the slope of the recurrence curve for the period between 5 and 10 years before a great event and a decrease immediately before it. These peaking anomalies are hypothesized to repeat themselves and to be quasi-periodic in character, with the periods of their occurrence prior to a great earthquake being arguably different in different regions. The results of this study can be used both to enhance the reliability of long-term earthquake prediction and for intermediate-term and short-term updating of earthquake hazard. [ABSTRACT FROM AUTHOR]

Abstract—: This paper reports the results of numerical modeling of the stress-strain state of the epicentral area before and after the August 24, 2014 earthquake in Napa, California, USA, compared to calculated data obtained in instrumental studies in the dilatation areas based on GPS observation results. Numerical modeling has made it possible to calculate the stress-strain state of the epicentral area affected by the tectonic fault system. GPS observation data on the epicentral area of the earthquake and the results of numerical modeling of the stress-strain state before and after the considered earthquake have been analyzed. A trend toward localization of earthquake epicenters in the region of high stress intensity concentration has been confirmed. It has been proved that aftershock development is due to the drop in stress caused by a new fracture and that the aftershock cluster that occurred was localized in the area of decreased stress intensity. [ABSTRACT FROM AUTHOR]

This paper aimed at assessing gravity variations as precursors for earthquake prediction in the Tibet (Xizang)-Qinghai-Xinjiang-Sichuan Region, western China. We here take a statistical approach to evaluate efficiency and possibility of earthquake prediction. We used the most recent spatiotemporal gravity field variation datasets of 2002-2008 for the region that were provided by the Crustal Movement Observation Network of China (CMONC). The datasets were space sparse and time discrete. In 2007-2010, 13 earthquakes (> M 6.0) occurred in the region. The observed gravity variations have a statistical correlation with the occurrence of these earthquakes through the Molchan error diagram tests that lead to alarms over a good fraction of space-time. The results show that the prediction efficiency of amplitude of analytic signal of gravity variations is better than seismicity rate model and THD and absolute value of gravity variation, implying that gravity variations before earthquake may include precursory information of future large earthquakes. [ABSTRACT FROM AUTHOR]

Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela. [ABSTRACT FROM AUTHOR]

Work was performed in 2008-2014 on the long-term prediction of great earthquakes at the Kuril-Kamchatka arc based on patterns that are observable in the locations of the rupture zones of great earthquakes (seismic gaps) and in the seismic cycle for such earthquakes. The prediction method has been successfully developed and applied to the region since 1965 (the LTEP method). This paper presents a long-term seismic forecast for the September 2013 to August 2018 period for the Kuril-Kamchatka arc earthquake-generating zone. The forecast predicts the following quantities for 20 constituent segments: phases of the seismic cycle, the normalized rate of small earthquakes A, the magnitudes of moderate-size earthquakes to be expected with probabilities 0.8, 0.5, and 0.15, the maximum possible magnitudes and probabilities of occurrence for great (M ≥ 7.7) earthquakes in the most active depth interval, viz., 0-80 km. We also discuss the seismic process in this region for the 2008-2013 period, namely, the patterns that are observable in the locations of earthquake swarms, the values of and changes in several characteristics of the seismic process in the Kuril-Kamchatka seismic zone during the period 2008-2013, which occurred in relation with great shallow earthquakes beneath the Sea of Okhotsk (July 5, 2008, M = 7.7; August 14, 2012, M = 7.7; May 24, 2013, M = 8.3, and the giant Tohoku earthquake of March 11, 2011 with M = 9.0). The results corroborate the high seismic hazard for the area of Petropavlovsk-Kamchatskii and the urgent necessity of continuing and intensifying the ongoing work on seismic retrofitting and seismic safety. [ABSTRACT FROM AUTHOR]

Site effect is usually associated with local geological conditions, which increase or decrease the level of shaking compared with standard attenuation relations. We made an attempt to see in the macroseismic data of Italy some other effects, namely, hot/cold spots in the terminology of O lsen (in Bull. Seismol. Soc. Am. 90, 6B, 577-594, ), which are related to local fault geometry rather than to soil conditions. We give a list of towns and villages liable to amplify (+) or to reduce (-) the level of shaking in comparison with the nearby settlements. Relief and soil conditions cannot always account for the anomalous sites. Further, there are sites where both (+) and (-) effects are observed depending on the earthquake. The opposite effects can be generated by events from the same seismotectonic zone and along the same direction to the site. Anomalous sites may group themselves into clusters of different scales. All isolated anomalous patterns presented in this paper can be used in hazard analysis, in particular, for the modeling and testing of seismic effects. [ABSTRACT FROM AUTHOR]

We present an analysis of spectral content for three large earthquakes which occurred in the Baikal–Mongolia region, for hard soils in Irkutsk and Ulaanbaatar cities. The earthquakes were recorded from the same seismic source zones (SSZ) in 2020 and in 2021. We used accelerograms, frequency characteristics of upper soil layers, and ground motion spectra to describe and analyze the behavior of the earth at recording sites. We estimated variations in the spectral content of the large earthquakes, and their records were used to set up a common basic signal with due account of the SSZ for soils of the first category in Irkutsk and Ulaanbaatar. The results of this analysis provide the basis for future prediction of seismic effects due to large earthquakes in the Baikal–Mongolia region. [ABSTRACT FROM AUTHOR]

The earthquake prediction problem can be defined as given a minimum Richter magnitude scale and a specified geographic region, predicting the possibility of an earthquake in that region within a time interval. This is a long-time studied research problem but not much progress is achieved until the last decade. With the advancement of computational systems and deep learning models, significant results are achieved. In this study, we introduce novel models using the structural recurrent neural network (SRNN) that capture the spatial proximity and structural properties such as the existence of faults in regions. Experimental results are carried out using two distinct regions such as Turkey and China where the scale and earthquake zones differ greatly. SRNN models achieve better performance results compared with the baseline and the state-of-the-art models. Especially the SRNNClass near model, that captures the first-order spatial neighborhood and structural classification based on fault lines, results in the highest F 1 score. [ABSTRACT FROM AUTHOR]

On the basis of the data on perturbations in the Earth's atmosphere recorded before and after 52 significant earthquakes that occurred during the period from 1997 to 2018 in the Asian region, it is shown that the amplitudes of internal gravity waves that originated from seismically active regions can be used at the saturation threshold level for a short-term indication of upcoming seismic events. [ABSTRACT FROM AUTHOR]

It is necessary to have reliable data on the distributions of earthquake hypocenters in the study area to solve many geological and geophysical problems, in particular, the forecast of strong earthquakes, seismic zoning and earthquake-proof construction. In turn, the completeness of the data on the distribution of earthquake hypocenters and the accuracy of their determination largely depends on the effectiveness of the observing system. The efficiency of the modern Ukrainian network of seismological observations that consists of 28 stations is estimated in this article. By the effectiveness of the seismological observation network we mean the ability of the network to record events with minimal magnitudes at certain epicentral distances and allowing us to determine its main parameters (time in the focus and coordinates of hypocenters) with minimal errors depending on the position of the earthquake hypocenter. Calculation of the minimum magnitude of earthquakes for the Ukrainian seismological network shows if each seismic station increase of susceptibility to 50,000 units then the seismological the network confidently registers earthquakes with a magnitude of 3 or more throughout the Ukraine. The errors in the determination of the coordinates of epicenters of earthquakes in latitude (δφ) (km) and in longitude (δλ) (km) do not exceed 1.0 km in the center of the network and 5.0 km on the periphery of the network. The values of errors in determining the depths of the earthquake (H) (km) centers in the center of the network does not exceed 5.0 km but on the whole territory of Ukraine does not exceed 20 km. The optimal configuration of the network of Ukrainian seismic stations based on the C-criterion of optimality, introduced earlier to the theory design of optimal experiments by one of the authors of the article, is proposed. [ABSTRACT FROM AUTHOR]

Earthquake magnitude prediction for Hindukush region has been carried out in this research using the temporal sequence of historic seismic activities in combination with the machine learning classifiers. Prediction has been made on the basis of mathematically calculated eight seismic indicators using the earthquake catalog of the region. These parameters are based on the well-known geophysical facts of Gutenberg-Richter's inverse law, distribution of characteristic earthquake magnitudes and seismic quiescence. In this research, four machine learning techniques including pattern recognition neural network, recurrent neural network, random forest and linear programming boost ensemble classifier are separately applied to model relationships between calculated seismic parameters and future earthquake occurrences. The problem is formulated as a binary classification task and predictions are made for earthquakes of magnitude greater than or equal to 5.5 ( $$M \ge$$ 5.5), for the duration of 1 month. Furthermore, the analysis of earthquake prediction results is carried out for every machine learning classifier in terms of sensitivity, specificity, true and false predictive values. Accuracy is another performance measure considered for analyzing the results. Earthquake magnitude prediction for the Hindukush using these aforementioned techniques show significant and encouraging results, thus constituting a step forward toward the final robust prediction mechanism which is not available so far. [ABSTRACT FROM AUTHOR]

This article presents site-specific probable seismic hazard of the Himachal Pradesh province, situated in a seismically active region of northwest Himalaya, using the ground motion relations presented in a companion article. Seismic recurrence parameters for all the documented probable sources are established from an updated earthquake catalogue. The contour maps of probable spectral acceleration at 0, 0.2, and 1 s (5% damping) are presented for 475 and 2475 years return periods. Also, the hazard curves and uniform hazard response spectrums are presented for all the important cities in this province. Results indicate that the present codal provision underestimates the seismic hazard at cities of Bilaspur, Shimla, Hamirpur, Chamba, Mandi, and Solan. In addition, regions near Bilaspur and Chamba exhibit higher hazard levels than what is reported in literature. [ABSTRACT FROM AUTHOR]

An earthquake forecasting model has been proposed for northeast India and surrounding region in a specified time span by exploiting the temporal and the spatial variations in the Gutenberg and Richter parameters ( a and b). The study region has been divided into grid with a grid interval of 0.1° in latitude and longitude and 14 zones for the seismic activity density and the seismicity parameters estimation, respectively, while the time span of the used earthquake catalog has been divided into time grids with a fixed time interval of 5 years. The seismic activity rate at a grid point has been estimated from seismic activity density at the grid point and seismic activity in zone comprising the grid. The normalized multivariate Gaussian mixture model is considered to estimate the seismic activity density, while seismicity parameters have been estimated using the Gutenberg and Richter relation and the method of maximum likelihood estimation. The seismic activity rates at each grid point in all time grids have been fitted to an autoregressive model of order three, and the inferred coefficients of the model has been used to forecast seismic activity rate in next time grid, e.g., in time grid from 2011 to 2015 including. [ABSTRACT FROM AUTHOR]

Instrumentally recorded seismicity from 1970/01/01 to 2014/01/01 of the central China north-south seismic belt (21.0°-41.5°N, 97.5°-107.5°E) was analyzed, emphasizing the applicability of the predictive algorithms based on the assumptions of meta-stable equilibrium. The seismicity in this region was shown to exhibit ergodicity from 1980 to the present, with sub-region dependence, and interrupted by the 2008 Wenchuan earthquake. pattern informatics algorithm, a statistical physics-based predictive model for five-year time scale, is put to forward forecast test for the period 2014/01/01 to 2019/01/01. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the earthquakes distribution according to their type of motions in the Kuril-Okhotsk region in Russia and the mechanisms of earthquake sources. The study uses data from the GlobalCMT catalogue to analyze the earthquake epicenters and evaluates distribution using the three vertical projections of earthquake sources by the direction orthogonal to the Kuril-Kamchatka arc position. Result shows that earthquake sources mechanism are at depths of up to 80 kilometers (km) in the southeastern part of the regions considered close to ocean. The analysis of the epicenter locations normal to the seismic focal zone (SFZ) reveals three zones from east to west, to which faults are found.