Coseismic water level changes which may have been induced by the Wenchuan MS8.0 earthquake and its 15 larger aftershocks (MS≥?5.4) have been observed at Tangshan well. We analyze the correlation between coseismic parameters (maximum amplitude, duration, coseismic step and the time when the coseismic reach its maximum amplitude) and earthquake parameters (magnitude, well-epicenter distance and depth), and then compare the time when the coseismic oscillation reaches its maximum amplitude with the seismogram from Douhe seismic station which is about 16.3 km away from Tangshan well. The analysis indicates that magnitude is the main factor influencing the induced coseismic water level changes, and that the well-epicenter distance and depth have less influence. MS magnitude has the strongest correlation with the coseismic water level changes comparing to MW and ML magnitudes. There exists strong correlation between the maximum amplitude, step size and the oscillation duration. The water level oscillation and step are both caused by dynamic strain sourcing from seismic waves. Most of the times when the oscillations reach their maximum amplitudes are between S and Rayleigh waves. The coseismic water level changes are due to the co-effect of seismic waves and hydro-geological environments. 相似文献
The Pingluo area, as an experimental study area in Yinchuan, has been subjected to major environmental degradation due to soil salinization problems. Soil salinization is one of the main problems of land degradation in arid and semiarid regions. In the present study, remote sensing was integrated with mathematical modeling to evaluate soil salinization adequately. To detect soil salinization, soil water content and electrical conductivity of soil samples were analyzed. The reflectance of soil samples was measured using a spectrometer (SR-3500) with 1024 bands. Indices of soil salinity, vegetation and drought were analyzed using Landsat images over the study area. Based on Landsat images, physicochemical analysis, reflectance of sensitive bands for soil salinization and environmental indices, canopy response salinity index (CRSI), perpendicular drought index (PDI) and enhanced normalized difference vegetation index (ENDVI), a new model was established for simulation and prediction of soil salinization in the study area. Correlation analyses and multiple regression methods were used to construct an accurate model. The results showed that green, blue and near-infrared light was significantly correlated with soil salinity and that the spectral parameters improved this correlation significantly. Therefore, the model was more effective when combining spectral parameters with sensitive bands with modeling. After mathematical transformation of soil reflectance, the correlations of bands sensitive to soil salinization were 0.739 and 0.7 for electrical conductivity and water content, respectively. After transformation of vegetation reflectance, the correlation coefficient of soil salinity became 0.577. After inversion of the model based on soil hyperspectral and water content, the significance became 0.871 and 0.726, respectively, which can be used to predict soil salinity and water content. The spectral soil salinity model had a coefficient of 0.739 for soil salinity prediction. Among the salinity indices, the CRSI was selected as the most significant, with R2 of 0.571, whereas the R2 for PDI reached only 0.484. Among the vegetation indices, the ENDVI had the highest response to soil salinity, with R2 of 0.577. After scale conversion, the correlation percentages between CRSI and measured soil salinity and between ENDVI and measured soil salinity increased to 16.2% and 8.5%, respectively. Following the correlation between PDI and soil water content, the percentage of correlation increased to 11.6%. The integration of hyperspectral remote sensing, ground methods and an inversion method for salinity is a very important and effective technique for rapid and nondestructive monitoring of soil salinization.