There are many factors related to the variations of TEC, and the changes of TEC caused by earthquake only occupy a small portion. Therefore, it is vital how to exclude the ionospheric interference of non-seismic factors accurately in the process of seismic ionospheric anomaly extraction. This study constructed a TEC non-seismic dynamic background field considering the influence of solar and geomagnetic activities. Firstly, the TEC components of half-year cycle and annual cycle are extracted by wavelet decomposition. Then, it establishes a regression model between TEC in which periodic factors are removed and solar activity index, geomagnetic activity index with SVR method(support vector regression)in non-seismic period. Finally, based on the constructed model, the solar activity index and geomagnetic activity index is used to reconstruct aperiodic components of TEC in earthquake's period. From the reconstructed aperiodic components of TEC plus the half-year periodic components and annual periodic components of TEC in the same period, the non-seismic dynamic background field is obtained. Comparing the residuals relative to original TEC values in non-seismic dynamic background field and traditional sliding window background, there are apparent monthly periodic change and semi-annual periodic change in the residuals of sliding window background, which can have obvious impacts on the subsequent seismic ionospheric anomaly detection. In order to test the validity of seismic TEC anomaly detection based on the background field construction method, this paper investigated the long time series TEC anomalies near Wenchuan city(30°N, 100°E)from March 1 to September 26 in 2008. It is found that under the condition of non-seismic disturbance such as solar activity and geomagnetic activity, TEC abnormal disturbance is rarely detected by non-seismic dynamic background field method, when compared with the traditional sliding time-window method. And before the earthquake, more TEC anomalies were detected based on the proposed method, also, they were more intense than those extracted by sliding window method. Therefore, the TEC background field construction method based on SVR(support vector regression)has superiorities in both system errors elimination, which are caused by solar, geomagnetism, the non-seismic ionospheric disturbance events and periodic fluctuations of TEC, and in reducing the false alarm rate of seismic TEC anomaly. Moreover, it can also improve the seismic TEC anomaly detection ability. In addition, this paper analyzed the time-spatial distribution of TEC anomaly before three earthquakes on May 12, August 21 and August 30, 2008. They were mainly negative abnormal perturbations and often distributed on the equatorial side of epicenter.

Based on the finite element models, in this paper, the impact of Japan 3·11 earthquake on the south-central section of Tanlu Fault zone is discussed in the view of displacement field. From the displacement magnitude acquired by the numerical simulation of Japan 3·11 earthquake, this earthquake has a certain impact on Tanlu Fault zone in mainland China area. The simulation results show that the coseismic horizontal displacement near the Tanlu Fault zone caused by Japan 3·11 earthquake has reached millimeter to centimeter level. On the east side of the fault zone, the maximum displacement value in Northeast China reaches to 30 millimeters, and the maximum displacement value in Yantai, Weihai and other places of North China also reaches to 12 millimeters. On the west side of the fault zone, the maximum displacement value in Northeast China reaches to 20 millimeters, and the maximum displacement value in North China is only 8 millimeters. The displacement value difference is very obvious in both sides of Tanlu Fault zone. In addition, the simulation results also show that the displacement values in the east-west direction is much bigger than the one in the south-north direction of the Tanlu Fault zone and nearby areas; especially the displacement direction is basically near to east-west direction in North China. Meanwhile, bigger NS-direction displacement appeared in Northeast China, and the maximum displacement value even reaches to 8 millimeters. Therefore, the impact of Japan 3·11 earthquake on this area is more complex. Considering the actual delay effect of crustal medium to stress transfer, the impact of Japan 3·11 earthquake on the Tanlu Fault zone will have a delay, so the follow-up effect will stay for a certain time. Although the displacement value in eastern North China(Tanlu Fault zone and its nearby area)is smaller than the one in Northeast China, the impact of Japan 3·11 earthquake still cannot be ignored. Considering that many magnitude 4 or 5 earthquakes have occurred in Kenli, Changdao, Rongcheng, Laizhou in Shandong Province and in the Rudong, Gaoyou in Jiangsu Province since 2011, it cannot be excluded the possibility that a larger earthquake will happen in this area in the future. Thus, due attention has to be paid to this.

The volcanic ash cloud is mainly composed of volcanic ash debris and gases. The adequate mixture of the two can form acidic aerosols. It not only causes the major global climate and environmental changes, but also seriously threatens the aviation safety. Remote sensing can quickly and accurately obtain the information of the surface's and the atmosphere's changes; therefore it is playing an important role in the monitoring of volcanic activity. In recent years, with the advancement of sensor technology, the thermal infrared remote sensing technology has become an important means of detecting the volcanic ash cloud. However, due to the large amount of spectral bands and data, the remote sensing data have pretty strong band correlation and obvious information redundancy problem, all of which have decreased to a certain degree the detecting accuracy of volcanic ash cloud. Therefore, it is necessary to introduce new data processing methods into the volcanic ash cloud remote sensing detection field. Principal component analysis(PCA)can compress a large number of complex information effectively into a few principal components; as a result, it is widely applied in the data compression and hyperspectral remote sensing field. Independent component analysis(ICA)is a recently developed new data processing method which can linearly decompose the observed data into mutually dependent components, and achieve the decorrelation and redundancy elimination of remote sensing data; so it has certain potential in volcanic ash cloud detection. A remote sensing detecting algorithm of volcanic ash cloud, which uses ICA method, is proposed after the exploration of the physics and chemical properties of volcanic ash cloud. This paper takes the MODIS remote sensing image of Iceland's Eyjafjallajokull volcanic ash cloud on April 19, 2010 as data source. It uses ICA in volcanic ash cloud detection on the basis of the principal component analysis(PCA)processing of MODIS image, and gives comparison among these following parties: the detected results, the relevant research results, United States Geological Survey(USGS)standard spectral database and SO₂ concentration distribution. The results show that: ICA can successfully obtain the information of the volcanic ash cloud from MODIS image; the detected volcanic ash cloud has a good consistency with the USGS standard spectral database and the SO₂ concentration distribution, thus, it can obtain pretty good detection results.

A large volcanic eruption can produce large amounts of volcanic ash,water vapor and heat,and form the volcanic ash cloud. The volcanic ash cloud is mainly composed of volcanic ash debris in diameter less than 2mm and gases including SO₂, H₂S,CO₂,the mixture of the two can form acidic aerosols which can stay in the atmosphere for a long time. It not only destructs the balance of earth's surface solar radiation and causes the depletion of the ozone layer,the greenhouse effect,air pollution,acid rain,anomalies of air temperature and precipitation,and other major global climate and environmental changes,but also damages and corrodes the structure of an aircraft,reduces the visibility and jams the radio communication system. The most serious problem is that the volcanic ash debris particles are capable of cooling and adhering to the aircraft engine blades after high-temperature melting,resulting in the flameout of aircraft engine. Under the background of globalization and the boom of air-transport industry,the volcanic ash cloud is a serious threat to aviation safety. Remote sensing technology can quickly and accurately obtain the information of the surface's and the atmosphere's changes,therefore it is playing an important role in monitoring volcanic activity. In recent years,with the advancement of sensor technology,the thermal infrared remote sensing technology has become an important means of monitoring the volcanic ash cloud. Currently,there have been a variety of remote sensors for volcanic ash cloud monitoring. Meanwhile,based on that,a series of volcanic ash cloud monitoring algorithms have also been developed for different remote sensors. However,most of the volcanic ash cloud monitoring algorithms have limitations of a low accuracy and a narrow scope. This paper tries to conduct a more comprehensive overview of the different types of remote sensors and the different algorithms for volcanic ash cloud monitoring. First,the damage of volcanic ash cloud to the natural environment and aviation safety is expounded; secondly,with the volcanic ash cloud remote sensing monitoring platform,this study systematically introduces the remote sensing sensor types of volcanic ash cloud remote sensing monitoring and the development situation of monitoring methods,and focuses on the moderate resolution imaging spectradiometer(MODIS)sensor which has better volcanic ash cloud monitoring effect; then,the research foundation of volcanic ash cloud remote sensing monitoring in China is reviewed; and finally the prospect and thoughts of their further development are discussed.

With the fast development of city,to acquire and update urban fundamental data is more and more important.After the high-resolution satellite systems were put into operation,people attach more importance to the real-time,precise and authentic aerial imagery.The high-resolution aerial photographs are applied in more and more fields,and detecting buildings information from these photographs will be the important means of acquiring and updating urban fundamental information.Based on the relationship between the buildings shadow and the buildings real height of aerial imagery,the methods to extract the buildings height from their cast shadow are described in this article and the computer technology of detecting automatically buildings information from aerial imagery is realized.Taking the aerial imagery of Baoshan District of Shanghai as example,the autoptical data was compared with the computational data,and the result shows that the high-resolution imaging has a whopping potential of acquiring urban fundamental data.