Earthquake-induced landslides, as an important secondary geological disaster, typically occurring during or shortly after an earthquake, have the characteristics of large quantity and scale, wide distribution, complex mechanism, serious casualties and economic losses, and long-duration post-earthquake effect. Rapidly and accurately obtaining the spatial distribution and potential hazard assessment of coseismic landslide following an earthquake is critical for emergency rescue and resettlement planning. Currently, the most commonly-used coseismic landslide hazard assessment methods include the data-driven machine learning methods and the Newmark method based on mechanics mechanism. The 2022 M_W5.8 Lushan earthquake provides a valuable window for us to carry out rapid emergence assessment of earthquake-induced landslides with different evaluation models. In this study, a new generation of China's earthquake landslide hazard model(hereinafter referred to as Xu₂₀₁₉ model)and a simplified Newmark model are used to carry out the rapid landslide assessment of Lushan event. The Xu₂₀₁₉ model selects 9 earthquake-induced landslide inventories around China as training samples and uses a total of 13 influencing factors such as elevation, relative elevation, slope angle, and aspect, and etc. to generate a near real-time evaluation model for coseismic landslides based on the LR method. The model can rapidly assess coseismic landslides towards a single earthquake event according to the actual PGA distribution. For Newmark model, the cumulative displacement(Dn)is calculated by the critical acceleration(a_c)and PGA maps. For the landslide inventory of this earthquake event, we completed the landslide inventory covering the entire affected area based on high-resolution optical satellite images(Planet)with 3m resolution acquired on 6 July 2022. Based on the coseismic landslide inventory including 2 352 landslides with an area of 5.51km², the accuracy and applicability of the two models are estimated. The results show that the landslide area calculated based on Xu₂₀₁₉ model is 5.07km², which is very close to the actual landslide area, and the predicted area calculated based on Newmark model reaches 21.3km². From the perspective of the spatial distribution of the prediction results, the distribution of the predicted high failure probabilities of the two models is roughly same, with the high probability values mainly located on the left side of the seismogenic fault. However, the difference lies in the low probability predictions of the northwest region of Baoxing county by the Xu₂₀₁₉ model. A zoomed-in view of a specific area comparing the spatial distribution of predicted landslide probabilities with the landslide abundance area shows that most actual landslide are concentrated in the medium to high failure probability areas predicted by the Xu₂₀₁₉ model, with only a few sporadic events occurring in the low probability zone. On the other hand, the Newmark model primarily identifies high instability probability regions in steep slope areas, which correspond closely to the actual landslide and collapse occurrences. However, the predicted hazard level of the northwest region i.e. the landslide highly developed area is obviously low by Xu₂₀₁₉ model, while the prediction result based on Newmark model for the southwest region is obviously overestimated. In terms of the LR model, the prediction results are very close to the actual landslide distribution, and the majority of the landslides are essentially located in areas with a high failure probability, indicating that the model has a relatively high prediction accuracy. The ROC curve is used to assess the model's accuracy. The results suggest that the model based on Xu₂₀₁₉ outperforms the Newmark model, with a prediction accuracy of 0.77, while the prediction accuracy of the Newmark model is 0.74. Overall, both two models have good practicability in the rapid evaluation of cosesimic landslide. However, the Newmark model needs multi parameter input, and these parameters themselves and the way of human acquisition are uncertain, which results in that the model evaluation is greatly affected by subjectivity.

Seismic landslide is a kind of natural disaster in which the slope is unstable and slips under the action of earthquake. Unlike landslides triggered by factors such as rainfall, strong earthquakes in mountainous areas tend to trigger a large number of landslides over a wide area, which can cause more casualties and economic property losses than the earthquake itself in many cases. Moreover, the occurrence of earthquake-induced landslides is characterized by abruptness and concealment, so it is difficult to spot monitoring and prevention. In order to reduce the loss of earthquake-induced landslide disaster, scientists have developed a variety of prediction and evaluation methods for earthquake landslide hazard based on different theories and models through long-term research. The M_S7.4 earthquake, which occurred at 2:04 a.m. on 22 May 2021 in Maduo, Qinghai(34.59°N, 98.34°E), provided an opportunity to test the validity of the different models. On the one hand, based on the simplified Newmark displacement model, the susceptibility of seismic landslide in Maduo earthquake area is calculated. Furthermore, the seismic landslide risk is evaluated by combining with the seismic intensity distribution map after Maduo earthquake. On the other hand, based on the discrimination analysis method, the empirical model obtained from the Niigata earthquake in Japan is used to predict the earthquake landslide in Maduo earthquake area. The research results show that: Based on the rapid assessment of earthquake-induced landslide risk by simplified Newmark displacement model, the potential high-risk areas are mainly concentrated in the intensity area of Ⅷ, Ⅸ and Ⅹ which are greatly affected by the intensity of ground motion. On the whole, with the weakening of the impact of ground motion, the landslide risk decreases gradually, this is in good agreement with the actual situation. As an empirical model, discrimination analysis method is relatively dependent on a specific environment. When it is used out of its own environment, it is necessary to verify the universality of empirical formula, re-understand the relationship between various impact factors, and adjust the weight of each factor. The difference between the two methods in the prediction results is mainly in the seismic intensity Ⅵ region. In the areas with intensity VII and above, the risk zoning obtained by the two methods is generally consistent. Due to the differences in the research models adopted by the two methods, there are some differences in the distribution of seismic landslide hazard areas with different risk levels in the prediction results, especially in the Ⅵ intensity region. Intensity Ⅵ region is wide with more mountainous areas, and steep slopes are distributed in most of the areas. As a result, the discriminant analysis results in this area are more influenced by slope and curvature value, so there are more highly dangerous areas in the prediction results. However, the simplified Newmark method is greatly affected by the ground motion. Because this region is far away from the epicenter and the impact of ground motion is weak, so the main prediction results of this region show more low risk areas. However, in the intensity Ⅶ and above areas, the risk zoning of the two methods was generally consistent, and the prediction effect was good. In general, it can be seen from the prediction results that these two methods reflect their effectiveness to some extent. However, due to the different factors and fewer constraints, there are some differences in the results. In the seismic landslide risk assessment based on the discriminant analysis method, objective and complete landslide samples need to be fully analyzed, which is also a problem faced by the prediction method based on empirical model. As a physical model, Newmark model does not depend on the specific environment, although it has the problem in accuracy of input parameters, it is more objective and reasonable in the calculation results. In this paper, a simple evaluation and analysis of the Maduo earthquake was conducted based on the Newmark model method, which only considered the impact of slope itself and ground motion, but did not take into account hydrological factors, human activities, geomorphic factors and other conditions. Meanwhile, the Newmark evaluation method needs to obtain relatively clear rock-soil physico-mechanical properties and ground motion parameters, but it is difficult to obtain accurate data of each slope in practice, so there are still defects and deficiencies in regional risk assessment using this model. Compared with other traditional prediction methods based on statistical analysis, the physical meaning of this method is clearer, and it has irreplaceable advantages in combination with ground motion parameters. As a qualitative method, the discriminant analysis method uses the empirical formula derived from other earthquake cases to predict landslides. Engineering geological conditions are different in different earthquake regions, so the controlling factors of earthquake-induced landslide are not the same and the influence weight of each factor is different to some extent. Both qualitative and quantitative methods have their own advantages and disadvantages in the study of regional seismic landslide hazard prediction. It would take a long time to achieve accurate prediction of earthquake landslides.

Strong earthquakes can not only trigger a large number of co-seismic landslides in mountainous areas, but also have an important impact on the development level of geological hazards in the disaster area. Usually, geological hazards caused by strong earthquakes will significantly increase and continue for a considerable period of time before they recover to the pre-earthquake level. Therefore, studying the evolution characteristics of landslides triggered by earthquake is particularly important for the prevention of geological disaster. In this paper, a 66km² region in Yingxiu near the epicenter of the 2008 M_S8.0 Wenchuan earthquake, which was strongly disturbed by the earthquake, was investigated. Firstly, one high-resolution satellite image before the earthquake(April, 2005)and five high-resolution satellite images after the earthquake(June, 2008; April, 2011; April, 2013; May, 2015; May, 2017)were used to interpret and catalog multi-temporal landslide inventories. Secondly, seven primary factors were analyzed in the GIS platform, including elevation, slope, aspect, curvature, stratum, lithology, and the distance from the nearest water system and the distance from seismogenic faults. Finally, the evolution of the landslide triggered by earthquake in this region was analyzed by comparing the landslide activity intensity in different periods, using the methods of correlation analysis, regression analysis, and single-factor statistical analysis. It was found that the total area of landslides in the study region decreased sharply from 2008 to 2017, with the area of the co-seismic landslide reducing from 21.41km² to 1.33km². This indicates that the magnitude of the landslides has recovered or is close to the pre-earthquake level. Moreover, correlation analysis shows that the elevation has a strong positive correlation with the distance from the nearest water system, and a weak positive correlation with the area. Meanwhile, there is a weak negative correlation between the distance from the nearest water system and the distance from seismogenic faults. Overall, the degree of landslide activity in the study region decreased over time, as well as the number of reactivated landslides and new landslides. The region where the area of earthquake triggered landslides decreased mainly concentrated at an elevation of 1 000m to 2 100m, a slope of 30° to 55°, an aspect of 40° to 180°, and a curvature of -2 to 2. In addition, the lithology of the Pengguan complex in the Yingxiu study region is more conducive to the occurrence of landslides, while the sedimentary rock is more conducive to the landslide recovery. When the distance from the nearest water system is more than 1 600m, the effect of the water system on the landslides gradually decreases. Also, the landslides triggered by Wenchuan earthquake in this area have the characteristics of the hanging wall effect, which means, the number of landslides in the northwestern region is much higher than that in the southeast side.

Landslides and rock falls along the highway are common geological hazards in Southwest China. As an influencing factor on potential landslides behavior, roads or distance to roads have been successfully used in landslide susceptibility assessments in mountainous area. However, the relationship between the road-cut and the slope stability is not clear. Therefore, we performed two-dimensional slope stability calculation using the general limit equilibrium (GLE)method incorporated in the software SLOPE/W of GeoStudio for stability analysis of slopes. Our studies show that the man-made roads influence on the slope stability mainly exists in two ways:One is to create a new steep slope, which will result in rock falls and shallow landslides along the roads; the other is to influence the stability of the original slope, which will result in comparatively huge landslides. For the latter, our simulation study reveals that the road location, namely at which part of a natural slope to construct a road is important for the slope stability. For a natural slope with a potential slip surface, if a road is constructed at or near the slope toe where the potential slip surface surpasses, it will greatly degrade the slope's factor of safety (Fs) and make the slope unstable; however, if a rode-cut is near the top of the slope, it will increase the slope's Fs and make the slope more stable. The safety location is different for different slope angle, steeper slope needs a higher location for a safety road-cut in comparison with gentle slopes. Moreover, the slope stability decreases when loading a seismic force and it varies with the slope angle. Firstly, the Fs decreases when the slope angle increasing, and when the slope angle reaches 45°, the Fs then becomes greater with the slope angle increasing.

On August 3, 2014, an M_W6.5 earthquake occurred in Ludian County, Yunnan Province, which triggered significant landslides and caused serious ground damages and casualties. Compared with the existing events of earthquake-triggered landslides, the spatial distribution of co-seismic landslides during the Ludian earthquake showed a special pattern. The relationship between the co-seismic landslides and the epicenter or the known faults is not obvious, and the maximum landslide density doesn't appear in the area near the epicenter. Peak ground acceleration (PGA), which usually is used to judge the limit boundary of co-seismic landslide distribution, cannot explain this distribution pattern. Instead of correlating geological and topographic factors with the co-seismic landslide distribution pattern, this study focuses on analyzing the influence of seismic landslide susceptibility on the co-seismic distribution. Seismic landslide susceptibility comes from a calculation of critical acceleration values using a simplified Newmark block model analysis and represents slope stability under seismic loading. Both DEM (SRTM 90m)and geological map (1 ︰ 200^￣￣000)are used as inputs to calculate critical acceleration values. Results show that the most susceptible slopes with the smallest critical accelerations are generally concentrated along the banks of rivers. The stable slopes, which have the larger critical accelerations and are comparably stable, are in the places adjacent to the epicenter. Comparison of the distribution of slope stability and the real landslides triggered by the 2014 M_W6.1 Ludian earthquake shows a good spatial correlation, meaning seismic landslide susceptibility controls the co-seismic landslide distributions to a certain degree. Moreover, our study provides a plausible explanation on the special distribution pattern of Ludian earthquake triggered landslides. Also the paper discusses the advantages of using the seismic landslide susceptibility as a basic map, which will offer an additional tool that can be used to assist in post-disaster response activities as well as seismic landslides hazards zonation.

Although the landslides triggered during earthquake events are common phenomena in the southwest China, the occurrence of the Hongshiyan landslide triggered by the M_S6.5 Ludian earthquake in 2014 is attractive for its giant volume which exceeds ten million cubic meters. The Hongshiyan landslide formed a quake lake and inundated a village. Based on the geological and geomophological data obtained through the immediate field investigation after the earthquake, we build the Hongshiyan slope model and at the same time, we apply numerical simulation to study the landslide formation. Result indicates that the Hongshiyan slope was at safe conditions with the Factor of safety (Fs) value greater than 1, but the ground seismic motion during the Ludian earthquake lowered its Fs to a value smaller than 1, which resulted in the occurrence of the landslide. Moreover, this study shows that an existing slip surface is important for generating a giant landslide, and steep slopes without existing slip surfaces are likely to generate shallow landslides with normal volumes.

Strong earthquake-induced landslides in mountainous region often cause serious damages to buildings, transportation route, lifeline engineering etc. It is one of the major reasons causing significant casualties and economic losses of property. For such serious and wide range disasters, doing classification for earthquake-induced landslide hazard zones is one of the effective methods for reducing losses. This paper, based on former studies, takes Lushan earthquake on April 20, 2013 as a sample and selects Lushan County, Baoxing County and the surrounding area, which were influenced seriously by landslide, as a study area. This paper takes comprehensive indexes method to classify earthquake-induced landslide hazard zones for this area. There are 5 impact factors, including lithology, slope, seismic intensity, distances from faults and distances from drainages, are selected and weight is determined for each factor by AHP (the Analytic Hierarchy Process). The study area then is classified into 4 levels of hazards zones by comprehensive indexes method to indicate possibilities of triggering landslide in region under the suffering from given seismic intensities. The paper makes a comparison between the existing landslide sites map and the earthquake-induced landslide hazard zoning map. The results show a relatively high match between the two maps (about 77% landslide sites are in the higher hazard zones and highest hazard zones). This research will provide a reference for emergency response of earthquake-induced landslide disaster, prediction of earthquake-induced landslides in mountainous area and prevention of landslide disaster.

More and more earthquake-triggered landslides show the hanging wall effects on the spatial distribution, however, the effects of seismic fault mechanics on landslides development are not clear at present time. Based on a simplified fault model, the knowledge of seismic waves reflecting between the ground surface and the fault plane is applied to explain the reasons why landslides are asymmetrically distributed on the hanging wall side during earthquakes caused by thrust faults. Moreover, this paper discusses the relationship between fault movement directions and the initial slope aspects based on the data of large landslides which occurred during the Wenchuan earthquake. This study shows that fault dip angle can affect landslides distribution, namely with the increasing of fault dip angle, the width of the region subjected to the landslides on the hanging wall side decreases. Also, the slopes whose aspects are in the similar directions as the initial movement direction of seismic fault are prone to failure. The knowledge obtained in this study will help to improve the accuracy in regional earthquake-triggered landslide susceptibility assessment and promote the research on the large landslides mechanism, major influencing factors as well as the possible landslide scales.

As a kind of secondary disasters caused by strong earthquakes,earthquake-triggered landslide has drawn much attention in the world because of severe hazards it causes. The 2013 Lushan,China,earthquake triggered lots of landslides and provided an opportunity to test various kinds of methods which have been used in earthquake triggered landslides assessment. Based on the high-resolution satellite images and aerial photos,we preliminarily interpret landslides in the damaged region. It is found that almost all of the landslides took place in the area with seismic intensity above Ⅶ.Spatially,the triggered landslides are controlled by the causative faults in their distribution and mainly concentrate around the epicenter. Based on the Newmark's method model,critical acceleration a_c is used to predict potential landslides. Comparing with the landslides occurrences in the study area,the result of our calculation proves that Newmark's model is effective in seismic hazards analysis. Also,the landslide affected area is estimated by several methods and the difference between them is discussed.

In order to study the genetic mechanism of the geohazards induced by rainfall-induced landslide and seismic landslide,the influence of the factors such as the texture of the slope,shape of the slope,vibration intensity,critical rainfall amount,on the slope failure is discussed,and the main models and processes of the slope failure under rainfall and earthquake are investigated,by the means of taking on-site simulation test for rainfall-induced landslide and seismic landslide. For rainfall-induced landslide,the critical rainfall amounts are determined for slopes of different angles. It is found that the critical rainfall amount and the slope angle follow logarithmic distribution. For seismic landslide,the slope failure extent is proportional to the angle of the slope,duration of vibration and the vibration intensity. The relation between landslide slope gradient and earthquake failure time is analyzed according to experimental data. It has been found by statistical analysis of landslides induced by the Wenchuan earthquake that the possibility of occurrence of landslide is greater in the slope with gradient greater than 30 degrees when the earthquake magnitude is above 4.The results of simulation experiment substantially meet the actual situation.

On May 12,2008,a huge earthquake(M_S=8.0)named Wenchuan earthquake hit Sichuan Province in Southwest China and triggered thousands of landslides.Post-seismic investigation and analysis discovered some characteristics of the landslides' spatial distribution.Landslides occurred unevenly on both sides of Longmenshan Fault zone,which are reverse faults and responsible for the Wenchuan earthquake.The majority of the landslides are distributed on the hanging wall of Longmenshan central fault,while only 12%of the total occurring on the footwall.The higher density of landslide is located at both ends of the Longmenshan Faults as well as the middle section.Statistical studies also show the occurrence of landslides has close relationship with ground motion,slope gradient and rock properties and so on.The geological background and evolution history of Wenchuan earthquake region have controlled the local geological and topographical setting and affected the landslide distribution when the great earthquake took place.Due to the exceptionally high topographic gradient and geological features such as loose soil and fragile rocks,this region is notoriously prone to landslide.

Earthquakes induced by reservoirs always cause more serious damage to reservoir dam due to its epicenter location and destroying effect.Reservoir induced earthquakes have different characteristics from the natural ones,their hypocenters are always shallow,less than ten kilometers,and the magnitude is small too.Till now the biggest earthquake induced by reservoir is the MS6.1 earthquake in Xinfengjiang reservoir in 1962.The paper analyzes the effects of different factors related to reservoir induced earthquake based on GIS technology and lots of reservoir induced earthquakes data from the world.The primary results show that reservoir's local geological and hydrogeological settings have closer relations with induced earthquake than regional geological and seismic activity setting does.The reservoirs with dams higher than 50m or reservoir volume greater than 5?1010 m3 are prone to induced earthquake.Moreover,the reservoir region,where limestone,carbonatite,karst landform,or joints and high permeable structure are developed,will have a higher probability to induce earthquake.

As of today,our understanding of the reservoir induced earthquakes is very limited and its study is still at an exploratory stage.The large amount of natural occurrences of reservoir induced earthquakes provide us an essential and invaluable database for in-depth study on this issue.It will certainly expedite the study if one can centralize and manage these data scientifically and efficiently.We introduce in this paper a reservoir induced earthquake database management system,which is based on Visual Studio 2008 development platform and C++ programming language.As it relies on Object Oriented Design(OOD) and Object Oriented Analysis(OOA),the system not only has a user-friendly interface and is easy to operate,but also has good maintainability,scalability and upgradability.The system supports a wide range of data formats for input and output.It is convenient for users to search, query,retrieve and update data.This reservoir induced earthquake database management system is undoubtedly a great research tool for studying the reservoir induced earthquakes.

Sediment related disasters,such as debris flow,landslide,slope failure etc.,always cause tremendous damages to lives and properties of people,deteriorate living environments and block the economic development.How to prevent such destructive sediment disasters and mitigate the damages is still a big problem facing the human beings.Japan is a country suffering many kinds of natural disasters,and amongst them,sediment related disasters occupy lots of the losing.From long time ago,Japanese government began to take measures against sediment related disasters,controlling erosion and sediment.Today,these elaborate and costly works are called Sabo works.Sabo is an effort to prevent sediment related disasters,realize the symbiosis between the nature and humans and renew vitality of the area.Due to different purposes,there are many kinds of Sabo works,and they are practically effective.For example,the 2004 Niigata Earthquake caused serious sediment related disasters,but by taking the correct countermeasures,this region has been renewed same as before the earthquake.Almost the same as Japan,China is also a place where many natural disasters are overwhelming,such as the big Wenchuan earthquake on 12 May,2008 which caused numerous landslides and collapse and damaged this region severely.Now we are facing the reconstruction and have to solve many problems relating to the sediment disaster.For this purpose,this paper introduces applications of SABO works in Japan.Through this,the author sincerely hopes it would be useful for the reconstruction works in the damaged area,and it would be helpful for sediment related disaster mitigation in China.

A destructive earthquake occurred around Changde,Hunan Province,south-central China in 1631.The previous research of this earthquake yielded 4 different epicenter locations and isoseismal intensity maps.The authors replotted the isoseismals of this event based on checking historical earthquake records,in which the intensity value of the innermost isoseismal is Ⅷ.We concluded that the depth of this earthquake is from 15km to 18km.The basic considerations of our conclusion are as follows:a.This earthquake occurred in an area of lacustrine and fluvial deposits,with the magnitude of M 6¹/2;b.The geometrical center of the innermost isoseismal is the epicenter;c.The depth of epicenter is about 15 to 18km,which is based on the statistical relation between magnitude,depth of the earthquake source and epicenter intensity as well as Xie's statistical result.Finally,the authors discussed the influences of different ground conditions on the textual research and identification of historical data.

In 1556, a strong earthquake with magnitude 8¹/₄ occurred in Huaxian, Shaanxi Province. The earthquake destroyed many places in Shaanxi Province and its adjacent regions, and also affected above 100 counties in Gansu Province, Hebei Province, Shandong Province and etc. According to historical documents, an earthquake was also recorded in Yueyang district, about 700km away from the Huaxian earthquake epicenter on the same day. Was it an isolated event or influenced by the Huaxian earthquake? It is a controversial problem.A lot of historical earthquake records of Yueyang district and its adjacent regions have been collected and studied, including the border area between Hunan Province and Hubei Province. The records for the 1556 AD Yueyang earthquake in “Annals of Yuezhou” in Longqing time of Ming Dynasty were judged to be more accurate than other historical documents. Earthquake recorded in 1556 AD in Yueyang district was a felt event with less strength. In analyzing historical materials, two principles for cases of earthquake records lacking have to be adopted: 1)Local annals to be used shall have other earthquake records. That means the annals writers paid attention to earthquake events; 2)Other types of disasters were recorded in the period of time when the earthquake occurred. That means there were no other kinds of historical disaster events influencing the records of earthquake.There were no earthquake records in counties like Tongshan, Tongchen, Puxi, Chongyang, Gongan, Shishou in the 34th year of Jiajing reign (1556 AD). It can be concluded that the earthquake records in Yueyang district in 1556 were not the effect of the Huaxian earthquake in Shaanxi Province in the same year. It was an isolated event. At last, according to records of local annals, the earthquake epicenter and magnitude are determined.

As we know, the landslide caused by earthquake does not distribute at random but has its destinations. This means that there are some inner relationships between the distribution and the factors which affect the happening of the landslide. But due to the complex mechanism of the earthquake-induced landslide, it's difficult to describe the relationship clearly. For expressing the non-linear relationship, we create the artificial network with the Radial Basis Probabilistic Neural Network Algorithm by using the toolbox of MATLAB. At first, we select fault, river, rock, slope angle, earthquake intensity as the landslide affecting factors after studying the research results of other scholars, then use GIS to model the research area and grid it. From the cells, we select neural network training samples and testing samples. After repeated training, the neural network reaches its steadiness. Then we use this network to simulate the whole research area. In this paper, the studying area is between 98.5°～99.0°E, 24.2°～24.9°N, where 2 strong earthquakes took place in 1976 and caused many landslides. We selected two different sets of training samples from the research area, and by contrasting the result of RBPNN with the facts, we find that the predicted hazard area is generally in accordance with the fact in the distribution of earthquake-induced landslide. In conclusion, we think RBPNN is a useful method for the research of earthquake-induced landslide, especially for the regional earthquake-induced landslide zoning.

The Bohai Sea, located in the northern part of North China, is an inland basin about 73 thousands km². Tectonically, it is one of the most active areas in eastern China since Late Tertiary. Meanwhile, it is also an area rich in oil and gas deposits. Previous studies have shown that faults and tectonic stress are important for oil and gas migration and accumulation. Based on the in-depth study on the tectonics of the Bohai Sea area and in the light of the geological and evolutional history, the authors simulate the stress field of the area using 2-D finite element method. In the simulation, the studied area is assumed as a rigid block that has been divided into 1347 finite elements, 1339 nodes. The tectonic field stress is calculated using SAP5 procedure. Some significant results are achieved. In light of the calculations and the evolutional history of the area, we firstly divide the Bohai sea tectonic stress field into four regions, namely the east Liaoning Bay, the central Bohai Sea, the east Bohai Sea and the west Bohai Sea. The stress characteristics are different in different regions, e.g. the central Bohai Sea has the maximum average normal stress value and shear stress value compared with other regions,while its minimum principal stress is lower. Secondly, the stress distribution of the whole study area shows that the faulting in the west is more intense than that in the east since Late Tertiary. The faults in the east Bohai Sea region have a higher degree of development and gaping along the Tanlu Fault belt, while in the middle Bohai Sea area, the faults have a low degree of displacement and development.

Geographic Information System (GIS) technique provides a powerful tool for managing,manipulating,analyzing and displaying space related data set. The compilation of seismic zoning map requires a vast amount of spatially referenced data. Therefore,the application of GIS to the seismic zo￣ning work allows the users to take advantage of various functions of GIS into the seismic zoning work. This paper introduces briefly the procedure and results of GIS application in the compilation of the New Seismic Zoning Map of China (2001). The GIS Database of the New Seismic Zoning Map includes three sub-databases: 1. Sub-database for seismic environment and potential seismic sources,including: (1)Earthquake catalog; (2)Quaternary active faults; (3)Cenozoic basins; (4)Earthquake mechanism; (5)Geophysical anomalies and deep structure; (6)Seismotectonic provinces and zones; (7)Potential seismic sources. 2. Sub-database for earthquake ground motion and seismic attenuation,including: (1)Seismic intensity and isoseism; (2)Observed seismic intensity; (3)Strong ground motion. 3. Sub-database for the results of probability calculation of seismic parameters.