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Seismicity de-clustering is the technique to isolate the earthquake catalog into aftershock-foreshock (clustered) and background (random) events. These isolated events are widely used in seismology for hazard assessment and to design the model for future earthquake predictions. The key challenge in seismic de-clustering is due to significant overlapping and high correlation between the space-time domain of aftershock-foreshock and background events. In this manuscript, a new model is proposed to de-cluster earthquake catalog based on a binary Non-dominated sorting genetic (B/NSGA)-II algorithm. In the fundamental version of the popular NSGA-II algorithm, one apprehension is that crossover and mutation are performed only on real-valued population. Here binary domain logical crossover and mutation operators are employed to optimally segregate the seismic events. The proposed model is tested on thirty year historical earthquake catalog of Turkey and Chile. Comparative analysis has been demonstrated with five benchmark de-clustering techniques. The simulation results demonstrate the potential of the proposed model efficiently discriminates the aftershocks and background events in the two catalogs.

Seismicity-related anomaly analysis or earthquake precursor cognition is a great challenge for geo-electromagnetic signals mining and remote sensing application. To mining seismic anomaly from satellite data including microwave data is of extreme importance. A spatio-temporally weighted two-step method (STW-TSM) is developed to retrieve or to mine seismicity-related microwave brightness temperature (MBT) anomaly with microwave satellite big data from Aqua AMSR-E and/or FY-3B MWRI sensors. To remove the general trend of MBT to retrieve basic MBT residuals of the shocking year, historical microwave data were given varied time-weights according to respective time intervals with the shocking year. To eliminate the regional meteorological noise so as to retrieve cleaned MBT residuals actually related with seismic activities, adjacent pixels inside the seismic preparation zone were endowed with varied space-weights according to Euclidean distances away from the epicenter With Wenchuan earthquake sequence in May, 2008 and Nepal earthquake sequence in April and May, 2015 being case studies, this paper demonstrated the practical application of STW-TSM. Results showed that 1) significant strip-shaped MBT anomalies appeared near the epicenter of Wenchuan earthquake days before the main shocking and migrated with aftershocks moving northeastward 2) the horizontal strip MBT anomalies covered exactly the epicenters and seismic concentration zone of Nepal earthquakes during shocking days 3) the MBT anomaly zones behaved highly spatial overlap with geological quaternary strata and cliff-shaped mountainous region. The mined MBT anomalies are supposed to be related with the effects of rock dielectric constant variation, which is most likely to be caused by crust stress activated positive electricity carriers from deep crustal. This research is of particular meanings for mining MBT anomaly from satellite microwave big data, and for searching geo-electromagnetic signals related with seismicity.

Aftershocks refer to the smaller earthquakes that occur following large earthquakes, in the same area of the main shock. The task of aftershocks detection, as a crucial and challenging issue in disaster monitoring, has attracted wide research attention in relevant fields. Compared with the traditional detection methods like STA/LTA algorithms or heuristic matching, neural network techniques are regarded as an advanced choice with better pattern recognition ability. However, current neural network-based solutions mainly formulate the seismic wave as ordinary time series, where existing techniques are directly deployed without adaption, and thus fail to obtain competitive performance on the intensive and highly-noise waveforms of aftershocks. To that end, in this paper, we propose a novel framework named Multi-Scale Description based Neural Network (MSDNN) for enhancing aftershock detection. Specifically, MSDNN contains a delicately-designed network structure for capturing both short-term scale and long-term scale seismic features. Therefore, the unique characteristics of seismic waveforms can be fully-exploited for aftershock detection. Furthermore, a multi-task learning strategy is introduced to model the seismic waveforms of multiple monitoring stations simultaneously, which can not only refine the detection performance but also provide additionally quantitative clues for discovering homologous earthquakes. Finally, comprehensive experiments on the data set from aftershocks of the Wenchuan M8.0 Earthquake have clearly validated the effectiveness of our framework compared with several state-of-the-art baselines.

In this paper, the 2015 Nepal Earthquake sequence, both the main shock (M W 7.8) on 25 April 2015 and the major aftershock (M W 7.2) on 12 May 2015 are investigated by using co-seismic DInSAR and GPS measurements. Source model and slip distribution of both events are determined using geodetic inversion based on an elastic dislocation model. The optimised source model for the main shock shows a thrust fault striking 285.9° NW-SE and dipping 7.7° NE with a slight right-lateral component. The maximum slip of this event is up to 5.1m. The peak slip of distributed slip model for the major aftershock was found at the similar depth of main shock. The triggering relationship between main shock and major aftershocks is demonstrated based on the calculation of Coulomb stress change.

Physarum optimization becomes popular after Liu et al in 2015 applied it for solving Steiner tree problem in networks. It has been effective in designing complicated road transportation networks, drug similarity networks and node-weighted protein-protein interaction network for cancer. In this paper, a clustering network is proposed based on Physarum optimization to identify the aftershocks (hazardous events) in the seismic catalog. In seismology, the process of identification of aftershocks and backgrounds (events due to regular earth movements) is popularly termed as declustering of earthquake catalog. Therefore the optimized networks obtained for aftershocks is termed as ‘Earthquake Clustering Network (ECN)’. The simulation studies are carried out on benchmark catalogs of Southern California and Japan. It is observed that the ECNs obtained from both the catalogs contain all the mainshocks and aftershocks occurred for the duration of the catalogs. The lambda plots obtained justify that identified aftershocks with ECN following the pattern of total events whereas the declustered events follow a uniform distribution. Comparative analysis demonstrates the effectiveness of the proposed model over three other benchmark declustering methods.

Important contributions of this work are: (1) Earthquake aftershock prediction, both location and time of occurrence, using seismographic data only; (2) Representing seismographic data as a directed weighted graph and analyzing it to extract relevant data for localized prediction; (3) Validation of the data and prediction algorithm. The key contribution is to make aftershock prediction using seismographic data alone. Seismographic data units are represented as nodes of a graph, and a special relation between them depicts the directed weighted edge between two nodes. This special relation facilitates connecting main earthquake with aftershocks caused by it. Converting vast amount of seismographic data to corresponding connected graph, and efficiently analyzing the graph to find cluster of nodes as hubs, is the first step. Though the link weights are real, in this work we consider whether two nodes are connected or not, depending on the connection weight is above some threshold or not. Extracting data of a hub, to use it for aftershock prediction of that individual localized area, is proposed. Data Driven approach is better because it is universally applicable and does not require domain knowledge (here, geophysical and geological knowledge of the plate structure). The idea could be extended for mining any big data.

Ecuador is located at the collision point of Nazca and South America tectonic plates, an area with high seismic activity; hence it is common that a large number of seismic events occur annually. On April 16, 2016, a M w 7.8 earthquake impacted coastal Ecuador, and plenty of aftershocks were located within the affected and surrounding areas. Determining similar seismic geographical zones of the such events has become a problem of great interest for post-earthquake mitigation actions. In this paper, we present an approach to cluster seismic events post M w 7.8 Ecuador earthquake. We carried out unsupervised pattern recognition through a clustering algorithm called MST-kNN. Our approach identified five clusters with similar seismic geographical zones along coastal provinces and Pacific Ocean. The results can be used to plan post-earthquake mitigation strategies such as identifying safe roads for evacuation plans or adequate location of ad hoc health and supply centers.

We design a system based on a convolutional neural network (CNN) for event detection and event time picking, and apply it to continuous recordings during aftershocks of the 2008 Mw7.9 Wenchuan Earthquake. The system detects 97% of the labeled seismic phases in a standard catalog. Off-line training of the CNN remains successful at 95% accuracy after reducing the training set size by a factor of ten to a few thousand training samples. When applied to a continuous aftershock dataset, the CNN-based method achieves accurate detection of manually labeled phases, precise arrival times of picked phases, as well as discovering many weak events not listed in the given seismic catalog. On-line processing of a 31-day recording takes less than 12 hours on a single GPU.

Earthquakes are the result of rupture in the Earth's crust. The rupture process is difficult to model deterministically due to the number of unmeasurable parameters involved and poorly constrained physical conditions, as well as the very diverse scales involved in their nucleation (meters) and complete failure (up to hundreds of kilometers). In this research work we focus on synthetic seismic catalogs generated with a stochastic modeling technique called Fiber Bundle Model (FBM). These catalogs can be readily compared with statistical measures computed from real earthquake series, but the link between the FBM parameters and the characteristics of the obtained earthquake series is difficult to assess. Furthermore, the stochastic nature of the model requires a large amount of realizations in order to attain statistical robustness. The aim of this work is to estimate the FBM parameters that generate aftershock sequences that are similar to those generated by real seismic events. In order to estimate the optimal combination of parameters that generate such sequences, we executed a large number of simulations with different combinations of parameters using High-Performance Computing (HPC) resources to reduce compute time. Lastly, the synthetic datasets were used to train a supervised Machine Learning (ML) model that analyzes and extracts statistical patterns that reproduce the observations regarding aftershock occurrence and its spatio-temporal distribution in real seismic events.

A magnitude dependent space-time correlation of earthquake events (foreshock and aftershocks occurred before and after the mainshocks respectively) require a classification approach to obtain an unbiased/uncorrelated estimation of seismicity (Background events; occurred due to regular movement of tectonic plates). In this paper, a shared nearest neighbor (SNN) based approach is introduced to classify events from an earthquake catalog in space, time and energy (magnitude) domain. The space-time separated mainshocks (events with high intensity in Richter scale) are considered the cluster centroids in this paper. Temporal zones are determined based on the cluster centroid with a single iteration distance algorithm. A space-time shared neighborhood criterion is incorporated to find the foreshock-aftershocks (clustered events) related to the respective cluster centroid for in each temporal zone. Earthquakes have higher magnitude are combined to space-time clusters and rest are considered as part of background seismicity. The proposed method is applied on California and Japan earthquake catalogs and obtained classification results are interpreted in terms of the epicenter plot, space-time plot, lambda and cumulative plots for true, clustered (forshock-aftershocks) and unclustered (backgrounds) events. The background seismicity has linear cumulative rate with respect to time and small deviation from the mean in lambda plot reveals the better performance of the model.

This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. These ground modifications often occur as a consequence of an earthquake. The island of Ischia (Southern Italy) has been chosen as case study since it was hit by a severe earthquake on the 21st of August 2017. The National Institute of Geophysics and Volcanology (INGV) and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) provided the displacement maps considering interferometric pairs close to the main shock event. In this work, a further interferometric pair, which also includes some aftershocks, has been used to calculate ground modifications. The results confirmed a ground subsidence up to 4 centimeters in the epicenter area, in agreement with the reference data. This study rely on open access data as well as on open software, both provided by the European Space Agency (ESA) under the Copernicus program. In particular, data from Sentinel-1 radar satellite mission and SNAP software have been used. The availability of such data and software is relevant for public institutions that can produce valuable information at almost no charge.

Predicting earthquake activity is challenging because of its complex mechanism. Theoretical models help us to understand some aspects; however, for practical purpose, it is difficult to predict earthquake activity using only models. Therefore, we usually utilize observational data by either fitting parameters to models or making models or indicators directly from the data. In this study, we chose the latter. We tried to reveal nonlinear relationships between observed data. We adopted machine learning, using three-layered neural network, as a method for extracting non-linear features. We applied this method to time-series data obtained from a multi-channel observatory before and after an earthquake. Thus, we obtained a trajectory of feature quantities that exhibited cyclic behavior, returning to the same state as before. We expect that the trajectory will be useful for judging whether aftershocks will occur, or not, by the feature quantities not returning, or returning, to their initial states.

The declustering of an earthquake catalog identify the background events (seismic events generated by regular earth movements), which leads to an unbiased estimation of seismic activities in a region. The ergodicity of a seismic region represents the ensemble average of events in time and space. The ergodicity of a seismic catalog is represented by a Thirumalai-Mountain (TM) metric. If the inverse TM metric becomes linear with time then the catalog is assumed to be declustered (it contains only the background events). But the original catalog normally contains backgrounds as well as seismically triggered events (Foreshocks and Aftershocks). The objective here is to optimally remove the triggered events from the catalog with an optimization algorithm so that the remaining catalog contains only the backgrounds. Here a parallel Grey Wolf Optimization (P-GWO) is introduced to perform the optimization task. Compared to the original GWO here the new updated positions of wolves are computed in parallel which reduces the computational complexity of the algorithm keeping the same accuracy level. The analysis is carried out on Southern California catalog and the results obtained are superior to that achieved by Cho et al. using PSO in 2010. Comparative results also demonstrate better performance over three benchmark statistical de-clustering methods by Gardner-Knopoff, Uhrhammer and Reseanberg.

The project aims at designing an earthquake monitoring and warning system that is capable of detecting earthquakes as well as warning people to take necessary precautions. This system will not only attempt to save human lives, but will also store the data for later use by professionals working at this sector. The objective of this project is to design a LabVIEW based control system by collaborating MEMS accelerometer sensor with Arduino microcontrollers in a ZigBee wireless network. The system also has telecom VAS capabilities to alert people and control machines. Existing earthquake systems are moreover designed for aftershock management and they hardly deal with real-time data. However, this system is mainly designed to react as soon as the sensor nodes come across a p-wave.

In humanitarian relief operations, especially when a disaster occurs, the transportation of both injured people to the medical centers and relief commodities to the affected area are vital. On the other hand, these disasters usually continue with a secondary disaster (e.g., Aftershocks of an earthquake) which decreases the service level of the affected area due to disruptions in the connection routes. This paper proposes a dynamic bi-objective, multi-commodity, multi-mode relief routing network, in which an inventory is taken into account for each affected area in preliminary periods of an earthquake response to overcome the probable shortages in subsequent periods. The objective functions attempt to simultaneously minimize total transportation and inventory cost and unserved injuries. Finally, a numerical example is solved using GAMS software.

This paper addresses the November 2013 Balochistan Earthquake. A co-seismic TerraSAR-X pair acquired in wide-swath ScanSAR mode has been used to derive two-dimensional deformation measurements (radar line-of-sight and azimuth direction) of the eastern part of the main M7.7 earthquake and the large M6.8 aftershock by correlating SAR amplitude images. Atmospheric and solid Earth tide corrections have been considered to achieve accuracy in the order of several centimeters. Correlation measurements from Landsat-8 images have been additionally estimated. The intention is to isolate vertical and horizontal components in order to obtain three-dimensional deformation measurements. Interferometric processing issues of ScanSAR data for non-stationary scenarios, specifically co-registration, are additionally discussed.

Wenchuan Earthquake triggered a lot of secondary disasters including landslide, collapse and mud-rock flow which have brought enormous difficulties on traffic, communications, and hydropower projects. As aftershocks will be last for a long time, the risk assessment of secondary disasters plays an important role for disaster prevention and mitigation. The remote sensing images of China-Brazil Earth Resource Satellite (CBERS) with geological map, soil map and chart of vegetation coverage were used to distinguish the potential dangerous regions in Wenchuan County and assess the risk degree of secondary disasters. The results show that high possibility risk areas are located in the tall mountains and river valley from northeast to southeast around Yingxiu, Weizhou, Miansi, Yinxing, Gengda and Xuankou Town.

In May 2012, two major earthquakes occurred in the Emilia-Romagna region, Northern Italy, followed by further aftershocks and earthquakes in June 2012. This sequence of earthquakes and shocks caused multiple casualties, and widespread damage to numerous historical buildings in the region. The Italian National Fire Corps deployed disaster response and recovery of people and buildings. In June 2012, they requested the aid of the EU-funded project NIFTi, to assess damage to historical buildings, and cultural artifacts located therein. To this end, NIFTi deployed a team of humans and robots (UGV, UAV) in the red-area of Mirandola, Emilia-Romagna, from Tuesday July 24 until Friday July 27, 2012. The team worked closely together with the members of the Italian National Fire Corps involved in the red area. This paper describes the deployment, and experience.

In September 2010, Christchurch, a city of 400,000 in New Zealand, was hit by a magnitude 7.1 earthquake 38 km away. The city sustained moderate damage with no loss of life. Nearly six months later, a shallow magnitude 6.3 aftershock close to the city centre caused serious damage and significant loss of life. Infrastructure was (and remains) severely damaged. Underground power cables were destroyed, and substations damaged. Most of the city lost power for periods ranging from hours to weeks.

The great earthquake of March 11, 2011 that occurred off shore the Tohoku region of Japan generated a very destructive tsunami that was anomalously higher than expected from its seismic waves. To understand the cause of the anomaly, an examination was undertaken of the seismotectonics of the region, of the earthquake' focal mechanism, of rupture patterns and of the spatial and temporal sequencing and clustering of major aftershocks - the latter defining the limits of crustal displacements, the amount of energy release and the tsunami generating area. Based on this analysis we conclude that the great tsunami resulted from a combination of up-thrust tectonic motions and crustal deformations of the ocean floor and from additional uplift due to a large coseismic lateral movement, which compressed and deformed compacted sediments along the accretionary prism on the overriding tectonic plate. Although a uniform elastic deformation of the sedimentary layer can be estimated and provide a seismic moment, the deformation was not elastic and occurred randomly and non-uniformly along faults that were either oblique or parallel to the earthquake's overall rupture direction, some failing in a sequential bookshelf manner. Both the 1992 Nicaragua and the 2004 Sumatra earthquakes demonstrated that bookshelf failures of sedimentary layers can generate anomalously high tsunamis. The same mechanism was responsible for the high tsunami generated by both the 2011 and the 1896 Sanriku earthquakes in Japan. The efficiency of the 2011 tsunami generation was greater along the shallow eastern segment of the fault off the Miyagi Prefecture where most of the energy release of the earthquake and deformations occurred, while the segment off the Ibaraki Prefecture - where the rupture process was rapid -released lesser seismic energy, resulted in lesser compaction and deformations of sedimentary layers and thus to a tsunami of smaller height. Because of the complexity of the rupturing process, the extent of additional uplift due to the buckling of the sediments in the tsunami generation area of the fault is difficult to estimate. However, based on the present evaluation, it is concluded that the greater tsunamigenic efficiency and destructiveness of the 2011 tsunami along Honshu's coastlines was caused by vertical crustal displacements of more than 10 meters due to up-thrust faulting, and that lateral compression and folding of sediments contributed additional uplift of about 7 meters along the leading edge of the accretionary prism of the overriding tectonic plate - as it thrusted in an east-southeast direction.

In this paper, we examine pre-earthquake ionospheric anomalies in time series and perform a statistical test by using total electron content (TEC) derived from global ionosphere maps (GIM). The normalized GIM-TEC (GIM-TEC∗), which is computed based on 15 days backward running mean of GIM-TEC, have been investigated for minimizing possible confounding effects of consecutive earthquakes and identify the abnormal signals. Meanwhile, to reduce the effect of strong geomagnetic activities such as geomagnetic storms, a criterion for removing the GIM-TEC data have been adapted; that is Dst index exceeds −60nT. Temporal variations of GIM-TEC∗ for large and destructive earthquakes in Japan have been studied; which are the 2004 mid-Niigata Prefecture Earthquake (M6.8), its aftershock (M6.1), the 2007 offshore mid-Niigata Earthquake (M6.8), and the 2008 Iwate-Miyagi Nairiku Earthquake (M7.2). Although there are some positive and negative TEC anomalies before and after the four earthquakes, there is a tendency that positive TEC anomalies appear 1–5 days before all the above earthquakes even in the quiet geomagnetic condition. Superposed epoch analysis has been performed for statistical analysis of TEC anomalies associated with M ≥ 6.0 Earthquakes during the 12-year period of May 1998–May 2010. The statistical result indicates the significance of the positive TEC anomalies 1–5 days before earthquakes within 1000 km from the epicenter around Japan.

The Ms6.2 Yunnan Dayao earthquake occurred on July 21, 2003, followed by a major Ms6.1 earthquake about 88 days later in the same region. The hypocenters are almost coincidence in the same place. Using the P wave first motion polarities of the two aftershocks sequences recorded by temporary stations and the improved version of grid points test method by Yu et al, we study the stress field in the aftershocks zone and get the two stress field directions in Dayao region. Using the stress value calculation method by Wan, we get the underground stress value in the focal area. The results show that the pre-seismic shear stress is larger than that of the post-seismic, and their difference corresponds to the stress drop approximately. The underground compressive stress is very large, but the differential stress is small. Before the main shock occurred on October 16th, the maximum principal stress is 166.27 MPa, intermediate principal stress is 158.71MPa and the minimum principal stress is 150.97 MPa. The normal stress and shear stress projected on fault plane are 157.25 MPa, 7.40 MPa respectively before the main shock occurred on October 16th. The normal and shear stress projected on fault plane are 158.81 MPa, 0.15 MPa respectively after the main shock occurred on October 16th.

Topside-ionospheric parameters recorded by the Instruments: Sonde de Langmuir (ISL), Analyseur de Plasma (IAP) and Capteur Electrique (ICE) onboard the DEMETER spacecraft during 85 days spanning the M8.0 Wenchuan Earthquake on 12 May, 2008 were investigated statistically. The influence of geomagnetic activity was removed from the records through confining the data investigated to periods characterized by (Dst ≥ −30 nT, Kp ≤ 3+, AE ≤ 200 nT). Spatiotemporal intensity distributions and two-dimensional maps of topside-ionospheric parameters measured in the earthquake preparation area were analyzed. From 7 May 2008 (5 days before the M8.0 earthquake), electron and ion densities began to decrease at the 670 km altitude of the spacecraft while, simultaneously, the electron temperature and the vertical electric field started to increase. On 9 May and 10 May these parameters attained their respective minimum/maximum values and they decreased/increased again on the day of the earthquake itself. Also, on 13 and 15 May fluctuations in these seismic-sensitive parameters, which may have been related to aftershocks, were recorded and they reverted to their normal values on 19 May, 2008. Thereafter, during the Qingchuan M6.4 Earthquake on 25 May, 2008 a sequence of similar variations occurred at about the same location (1.6° north and 2.0° east of Wenchuan). The fact that the regional plasma density decreased and the electron temperature increased is interpreted to indicate a sinking of the ionosphere above the epicentral area. The observed increase in the local earthward vertical electric field would cause ionospheric ions to flow downward and simultaneously shift eastward due to E×B drift. This can explain the occurrence of top-level ionosphere subsidence and also the fact that the center of this subsidence was not located directly above the epicenter but, rather, displaced to the south-east of it. Although in situ spacecraft measurements are typically constrained with respect to providing measurements at high spatiotemporal resolution, in the light of the ionospheric plasma density variations that preceded the Wenchuan earthquake by five days, it is suggested that remote monitoring of the global ionospheric plasma might in future provide a useful means to detect ionospheric precursors of seismic activity.

In this paper, we define the angle between P, B, and T axes of focal mechanisms and three stress axes of tectonic stress field as the consistent parameter, to research the dynamic changes of stress field in earthquake preparation area before great earthquake. We mainly studied the change of the consistent parameter before the 2010 Maule, Chile earthquake using the CMT solutions. The result shows that the consistent parameter decrease before the earthquake, and the area of the lower consistent parameter in coincided with the distribution of aftershocks. It should be a phenomenon of predictive significance. When the accumulated earthquake examples are sufficient, uniform judgment criteria and prediction principles can be stipulated.

This paper reports a preliminary study of underwater shock wave application for modification and possible control of embryonic cell differentiation and proliferation. Underwater shock waves have been of interest for various scientific, industrial, and medical applications. Shock waves have been successfully applied for disintegration of kidney stones in Urology and for bone formation in Orthopedics surgery. In the present study shock waves were generated by electric discharge using a magnetic pulse compression circuit (MPC) and tungsten point to point electrodes. Uniformly formed shock waves were used. Pressure profiles of shock waves were measured by a fiber optic probe hydrophone (FOPH). Underwater shock waves were applied to medaka egg embryos through an experimental setup. A variety range of number of shock waves from 1 to 50 shots and with different overpressures from 15 to 45 MPa was applied. The effects of shock waves on the embryo growth were observed by a fluorescent microscope for three weeks. Propidium iodide (PI) was used to investigate the effects of shock wave on the cell membrane. After shock wave application, a very clear modification was observed; the extents of damage were increased by increasing the shock wave numbers.

On 9 January, 2008, the Mw 6.4 earthquake occurred in Gaize, central Tibet of China, then the largest aftershock Mw 5.9 followed on 16th of January. Firstly, we derive the co-seismic deformation field with Synthetic Aperture Radar. The results show an asymmetric deformation pattern both mainshock and aftershock with normal faulting on a NE-striking fault plane. Then we invent for the geometry and the slip-distribution on thedouble-fault models. The result shows that the inverted hypocenter location of Mw 6.4 mainshock and Mw 5.9 aftershock are at 85.30±0.03°E/32.44±0.03°N and 85.27±0.01°E/32.46±0.02°N with strikes of 213.31° and 201°, and with dips of 56° and 54°, respectively. The mainshock upper side is about 2.9km from ground surface, the average slip is 0.74m and the maximum slip is about 2.32m occurring at depth of 7.92km under ground surface, and the average rake over the slipping area is −65.4°, the released scalar moment is 2.19×1018 Nm; The aftershock upper side is about 1.6km from ground surface, the average slip is 0.87m and the maximum slip is about 1.12m occurring at depth of 4.13km under ground surface, and the average rake over the slipping area is −75.0°, the released scalar moment is 1.41×1018 Nm.

This paper presents an online Earthquake Disaster Simulator (EDS) designed and developed based on active database technology. This web-based tool allows users to inject the epicenter data of an earthquake with parameters such as magnitude and quake duration in order to get a list of the areas affected based on the distance from the epicenter, and then estimate damages caused by the quake event, including both structural destruction and human death and injuries. An earthquake damage evaluation model is established that considers major factors of both the quake itself and the impacted geographical area. The tool also supports the damage assessment caused by aftershocks in which the degradation of structure's resistance to earthquake is factored in.

To detect and evaluate the earthquake damage of a 9-story reinforcement concrete building in the main shock of Miyagi-ken Earthquake, Sep. 15, 1998, the parameters of the building are identified and analyzed by using off-line system identification method from the structural strong motion records in the foreshock and aftershock before and after the main shock event. The results show that the structural parameters can be identified accurately from the seismic response data in small earthquakes during which the structure can be assumed as the time-invariant system. By comparing the parameters identified from the small earthquakes before and after the main shock, the structural damage in the main shock can be detected and evaluated primarily. It is found that the damage occurrence can be detected from the decreasing of the natural frequency and equivalent stiffness of the building, whereas the damping ratios are not suitable to detect the damage because the variations of damping ratios of the building are irregular after damaged in the main shock. The results can be referenced by the structural earthquake damage detection, diagnosis, and related work.

The time-invariant modal parameters of a 5-story building are identified from the seismic response time histories in 3 earthquakes by using off-line system identification method ARX model, and all the parameters are compared to find out the parameter changes and the seismic damage information primarily. The results show that the modal parameters of the structure can be identified accurately and easily by using the ARX method, and the structural parameters of the building before and after damage can be obtained from the two small earthquake response data before and after the main shock. The parameters identified from the data in the main shock are similar to the results from the data in the aftershock, but different with the results from the data in the small earthquake just before the main shock. It is found that the modal frequencies and equivalent stiffness decrease but the damping ratios increase after the main shock. From the results, it can be concluded that the building are damaged in the main shock. The results can be referenced by the seismic damage detection, diagnosis, and related tasks.

With geometric fractal methods, the aftershock spatial-temporal fractal dimension of Wenchuan earthquake was calculated, and the variation rule of seismic b-value were compared with the results, which proved that the aftershock spatial-temporal fractal dimensions were shown with the overall decreasing trend in Wenchuan earthquake, which was the same to the variation rule of seismic b-value; strong aftershocks mainly occurred in the descending stage and the low valley area of the spatial-temporal fractal and b-value curve. This variation of spatial-temporal fractal was considered to represent the clustering variation of seismic spatial-temporal, reflecting the process of destruction and energy release with the tectonic stress action of earth crust rock mass.

The entire world has been affected by Earthquakes inducing catastrophic consequences. On 12 May 2008, an earthquake with magnitude 7.9 on the Richter scale occurred in the Wenchuan area of the Sichuan Province in China. This disaster associated with subsequent aftershocks caused many avalanches, landslides, debris flows, collapses, and quake lakes; and induced many unstable slopes. This work proposes a methodology using a Data Mining approach and Geographic Information Systems (GIS) to predict these mass movements through their association with the main and aftershock epicenters, geologic faults, riverbed and topography. A dataset comprising more than 3,800 mass movements has been analyzed. Some models to predict the location of the mass movements induced by the seismic events were developed. These predictive models could be used for the responsible Chinese authorities as a relevant tool in identifying risk areas and rescuing survivors during a similar event in the future.

Authors in this paper mainly employ Ne (electron density), and Te (electron temperature) data of Langmuir Probe Instrument (ISL) onboard DEMETER, to study the variations of electron density and electron temperature associated with strong earthquakes. Tonga Mw 7.9 earthquake case study shows that revisited orbits of 09768_1 and 09782_1 (before and after shock, respectively) within 4 months data before earthquake and 2 months data after earthquake changed significantly before earthquake. Te data recorded by 09768_1 and its revisited orbits before earthquake were lower about 1000K to 1500K than recorded after earthquake, whereas Ne data of the same orbits not occurred such distinctive changes. Ne data of 09782_1 and its revisited orbits experienced an increase from 20000cm−3 to 30000cm−3. To further understand the features of the pre-earthquake ionospheric anomalies, we examine the temporal and spatial evolution of electron density within the area of 2000km near epicenter from 1st March to 1st July 2006 and from 1st March to 1st July from 29st April 2005 to 2nd May 2005 respectively. Results show that about the 5 days ago before earthquake the electron density dropped to a relative lower level comparing to the normal days without earthquakes.

The D-InSAR technology is used to acquire four strips of post-seismic surface deformation of Wenchuan M8 earthquake of 2008 from the ALOS/PALSAR satellite data of Japan. The result covers the Yingxiu town, Wenchuan, Shifang, Maoxian county, Beichuan, Pingwu and Qingchuan county in Sichuan Province. Some post-deformation characteristics is showed in the final result. Around Yingxiu town, the epicenter of main shock, a area of uplift is present with small amplitude 0–5cm at the northern wall of the causative fault, while relative subsidence took place with the amplitude 0–15cm on the other wall. Near Shifang county, there are slightly arranged concentric fringes, reflecting another subsidence area with amplitude 0–20cm. Around the Beichuan and Anxian county, there exists a relatively small uplift of an strip area with 0–5cm along the causative fault on its both side. A small area of dense concentric fringes(indicated by a rectangle box) appeared in 30km northeast of Qingchuan, which coincide with the locations of the aftershocks Ms6.1 of 5 Aug. 2008. On the lower wall, we can see another large uplift area of sparse concentric fringes located in more than 100km southeast of Qingchuan, because it is far from the causative fault, it's impossible so large deformation took place, and it should attribute to atmospheric component. the deformation characteristics of the whole area is complicated generally.

This paper firstly presents the Multi-LOS DInSAR measurement result of the coseismic deformation field caused by the Gaize Ms6.9 mainshock and Ms6.0 aftershock in Tibet, China, and then obtain the 3D deformation field based on the 3D resolving mode. The characteristic analysis of coseismic deformation field shows the rupture of mainshock is majorly normal, left-lateral striking with a little rotation; and the aftershock is typical normal rupture nature. The mainshock and aftershock had induced the east and west rupture(maybe buried) successively, and produced the east and west two subsiding centers

The Wenchuan earthquake was a destructive earthquake that measured at 7.9 Mw occurred at 14∶28∶01.42 CST on May 12, 2008 in Sichuan province of China, which induced large quantities of secondary mountain disasters such as collapse, landslides, debris flows and barrier lakes. These disasters occurred and more seriously formed disaster chains causing great destruction, and costing many lives after both the earthquake and thousands of aftershocks. To gather a proper understanding of disaster and formed chains, it is necessary to consider disaster chains for the dilapidated areas. In this paper, we focused on dynamically monitoring and analyzing the disaster chains patterns, spatial distribution and trying to choose the best time to cut the chains for preventing the prospective disasters. The multi-sensor remote sensing imageries before and after earthquakes were used to discover the disasters induced by earthquake and aftershocks, then to acquire the distributions of these disasters and rapidly analysis and assess the disaster chains to prevent possible disaster consequences.

Rigid-frame tied-arch bridges have become one of widely-used bridges due to the good performances. However, the research on its seismic performance, especially on the seismic response of its structure under strong after-shock, is not sufficient. The strong after-shock time-history which obtained through statistical laws was used as input earthquake motion in the paper. The structure-damaging index from “first exceed” and “cumulative damage” destruction were completely considered to verify the damage status of structures. The seismic response of a typical rigid-frame tied-arch bridge under strong after-shock was analyzed in this paper. The results show that the load of strong after-shock intensifies the structure damage, which is important to researches and analysis of the seismic performance for key bridges.

For the problem that the prediction accuracy of real-valued attribute data is not high, a modeling method named PR-KNN (Polynomial Regression and K Nearest Neighbor) is proposed, which is based on combination of KNN (K Nearest Neighbor) algorithm and Polynomial Regression model. Firstly, K nearest decision attribute values in training samples are selected by using KNN algorithm. Secondly, these K nearest decision attribute values are modeled by using Polynomial Regression method. And this method is applied to aftershock prediction. Experimental data are the sequence data of aftershocks with magnitude greater than or equal to 4.0 from Wenchuan earthquake. Comparing with traditional KNN regression algorithm and Distance-Weighted KNN regression algorithm, experimental results show that the maximum relative error predicted by PR-KNN reduces by 6.012% and 7.751% respectively, and maximum absolute error reduces by 0.367 and 0.473 respectively.