查看更多>>摘要：Landslides are widespread geomorphological phenomena with complex mechanisms that have caused extensive causalities and property damage worldwide.The scale and frequency of land-slides are presently increasing owing to the warming effects of climate change,which further increases the associated safety risks.In this study,the relationship between historical landslides and environmen-tal variables in the Hanjiang River Basin was determined and an optimized model was used to con-strain the relative contribution of variables and best spatial response curve.The optimal MaxEnt mod-el was used to predict the current distribution of landslides and influence of future rainfall changes on the landslide susceptibility.The results indicate that environmental variables in the study area statisti-cally correlate with landslide events over the past 20 years.The MaxEnt model evaluation was applied to landslide hazards in the Hanjiang River Basin based on current climate change scenarios.The re-sults indicate that 25.9％of the study area is classified as a high-risk area.The main environmental variables that affect the distribution of landslides include altitude,slope,normalized difference vegeta-tion index,annual precipitation,distance from rivers,and distance from roads,with a cumulative con-tribution rate of approximately 90％.The annual rainfall in the Hanjiang River Basin will continue to increase under future climate warming scenarios.Increased rainfall will further increase the extent of high-and medium-risk areas in the basin,especially when following the RCP8.5 climate prediction,which is expected to increase the high-risk area by 10.7％by 2070.Furthermore,high landslide risk ar-eas in the basin will migrate to high-altitude areas in the future,which poses new challenges for the pre-vention and control of landslide risks.This study demonstrates the usefulness of the MaxEnt model as a tool for landslide susceptibility prediction in the Hanjiang River Basin caused by global warming and yields robust prediction results.This approach therefore provides an important reference for river ba-sin management and disaster reduction and prevention.The study on landslide risks also supports the hypothesis that global climate change will further enhance the frequency and intensity of landslide ac-tivity throughout the course of the 21st Century.
查看更多>>摘要：Topography can strongly affect ground motion,and studies of the quantification of hill surfaces'topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite ele-ment method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing ele-vation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables'combinations which are convenient to achieve are pro-posed in this paper for the prediction of amplification factors of PGA and response spectra,respective-ly.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra's amplification factors.One input variables'combi-nation can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.
查看更多>>摘要：The exploration and development of tight sandstone gas reservoirs are controlled by high-quality river channel sand bodies on a large scale in Sichuan Basin.In order to improve the accu-racy of sand body prediction and characterization,Multi-component exploration technology research has been carried out in Northwest Sichuan Basin.First,based on the array acoustic logging data,a for-ward modeling has been established to analyze the seismic response characteristics of the PS-wave data and P-wave data.The result shows that the response characteristics of the P-wave and PS-wave to the sand bodies with different impedance are different.And then through the analysis of logging data,the effectiveness of the forward modeling has been proved.When the sandstone velocity is close to the sur-rounding rocks,the P-wave performs as a weak reflection,which may lead to reduce the identification range of the sand bodies.However,the PS-wave exhibits strong reflection,which can identify this type of sand bodies.Finally,by comparing and explaining the PS-wave data and P-wave data,and integrat-ing their attributes,the prediction accuracy of sand bodies is improved.Compared with the interpreta-tion of a single P-wave,the results can significantly expand the distribution range of sand bodies,lay-ing a foundation for improving the production capacity of single wells and reserve submission.
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