Interrelation between seismicity parameters and delimiting potential seismic sources in a seismic statistical region and its influence on seismic risk estimation

In the paper, we have discovered the abnormal area distribution features of maximum variation values of ground motion parameter uncertainty with different probabilities of exceedance in 50 years within the range of 100°-120°E, 29°-42°N for the purpose to solve the problem that abnormal areas of maximum variation values of ground motion parameter uncertainties emerge in a certain cities and towns caused by seismicity parameter uncertainty in a seismic statistical region in an inhomogeneous distribution model that considers tempo-spatial nonuniformity of seismic activity. And we have also approached the interrelation between the risk estimation uncertainty of a site caused by seismicity parameter uncertainty in a seismic statistical region and the delimitation of potential sources, as well as the reasons for forming abnormal areas. The results from the research indicate that the seismicity parameter uncertainty has unequal influence on the uncertainty of risk estimation at each site in a statistical region in the inhomogeneous distribution model, which relates to the scheme for delimiting potential sources. Abnormal areas of maximum variation values of ground motion parameter uncertainty often emerge in the potential sources of Mu greater than or equal 8 (Mu is upper limit of a potential source) and their vicinity. However, this kind of influence is equal in the homogeneous distribution model. The uncertainty of risk estimation of each site depends on its seat. Generally speaking, the sites located in the middle part of a statistical region are only related to the seismicity parameter uncertainty of the region, while the sites situated in or near the juncture of two or three statistical regions might be subject to the synthetic influences of seismicity parameter uncertainties of several statistical regions.

An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction

To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.

Interrelation among several important links in seismic risk analysis

In order to further reveal the interrelation among division of seismic statistical regions, delimitation of potential seismic sources and estimation of seismicity parameters, we select 21 representative sites located in different places within the range of 100°-120°E, 29°-42°N to study the influences of seismicity parameter uncertainties of statistical regions on seismic risk estimations of these sites in the inhomogeneous and homogeneous distribution models. Combining the results from this study and previous ones, we can see that different schemes for dividing seismic statistical regions can change the seismic data in a statistical region. The uncertain data and additional uncertainty in selecting time intervals for seismic statistics will result in uncertainty of seismicity parameters estimation in a statistical region. For the homogeneous model, the larger the variation of this uncertainty is, the greater the uncertain influence on the seismic risk estimation of a site will be, which means that the division of seismic statistical regions makes a major contribution. In a seismic statistical region, the delimitation of potential sources and variant weight assignment of spatial distribution functions can raise the estimated values of ground motion parameters in the place where great earthquake might occur and its vicinity. In these places, the influence of uncertainty in potential source delimitation is very obvious, especially on the absolute magnitude of ground motion parameters （e.g., intensity）, which means that the link of potential source delimitation makes a major effect. Generally speaking, the link of potential source delimitation affects mainly the sites located in the potential sources with the highest and second-high upper-limit earthquake magnitudes or in the vicinity of those with the highest upper-limit magnitude. While for the sites located in the potential sources with low upper-imit magnitudes, the uncertainty influence of statistical region division is larger than that of potential source delimitation.

Texture Image Segmentation Based on Nonlinear Diffusion

A texture image segmentation based on nonlinear diffusion is presented. The scale of texture can be measured during the process of nonlinear diffusion. A smooth 5-channel vector image with edge preserved, which is composed of intensity, scale and orientation of texture image, can be achieved by coupled nonlinear diffusion. A multi-channel statistical region active contour is employed to segment this vector image. The method can be seen as a kind of unsupervised segmentation because parameters are not sensitive to different texture images. Experimental results show its high efficiency in the semiautomatic extraction of texture image.

The Study of Spatial Distribution of Land Use in the Yellow River Delta in China

In this paper, Land at 8 OLI image data from 2013 to 2017 was interpreted by visual interpretation combined with supervised classification to extract the information of land use distribution in the Yellow River Delta in China. The characteristics of Land Use Cover Change (LUCC) in the past five years which were analyzed by land use spatial analysis method based on buffer zone were discussed to obtain the land use index trend of five land types in buffer zones at different distance. The land use transfer maps of 2013 and 2017 were made by using the geo-information mapping method. The spatial-temporal change rules and the development process of land use in the Yellow River Delta during the past five years were analyzed. According to the analysis results, the comprehensive index of land use degree in the study area was in the middle level. The land use transfer maps were mainly consisted of farmland and grassland. The change rate of bare land to vegetation was 20.21% and that of vegetation to bare land was 14.15%. This study can provide effective basis for the scientific management of land and rational guidance for planning in this area.

STATISTICAL ANALYSIS OF CYCLONE TRACKS IN WESTERN ANTARCTIC REGION

The paper shows the statistical analysis of cyclone tracks that have influence on the western Antarctic region.Based on the conditions of cyclone movement and its impact upon the weather,cyclone tracks are classified into three categories,i.e.,the track moving towards the northern tip of the Antarctic Peninsula, southern track,and northern track. Moreover,in this paper,the frequency distributions of cyclone tracks,the major tracks with higher frequencies,the original region of Antarctic cyclones and the seasonal features of Antarctic cyclones have been analyzed.The results show that there are higher cyclogeneses in summer,whereas relatively fewer cycloge- neses in winter,and cyclone numbers in transitional seasons are close to the climatological average.The analysis also shows that the moving velocity of Antarctic cyclone is about the same in winter and summer. It obviously speed up during the transitional season.