Past researchers have anticipated the occurrence of a great earthquake in the central Himalayas in the near future.This may cause serious damage in the Kathmandu Valley,which sits on an ancient lake bed zone,with lacu...Past researchers have anticipated the occurrence of a great earthquake in the central Himalayas in the near future.This may cause serious damage in the Kathmandu Valley,which sits on an ancient lake bed zone,with lacustrine sediments of more than 500 m depth.In this study,the predominant frequency of ground motion is evaluated using the Horizontal-to-Vertical (H/V) spectral ratio technique and recordings of ambient noise.The results of the H/V ratio show two peaks in about 20 percent of the locations,which are distributed mainly in and around the center and northern part of the Kathmandu Valley.The predominant frequencies vary from 0.5 Hz to 8.9 Hz in the study area,whereas the second resonance fiequency varies from 4 Hz to 6 Hz in the center and northern part of the valley.This indicates that the center and northern part of the valley have a wide range of resonance frequency due to two levels of impedance contrast- one may be from the surface layer and the other may be from the layer undemeath.These two levels of resonance indicate the importance of considering the effects of surface and lower layers during the planning and designing of infrastructures in the Kathmandu Valley.展开更多
Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarp...Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.展开更多
文摘Past researchers have anticipated the occurrence of a great earthquake in the central Himalayas in the near future.This may cause serious damage in the Kathmandu Valley,which sits on an ancient lake bed zone,with lacustrine sediments of more than 500 m depth.In this study,the predominant frequency of ground motion is evaluated using the Horizontal-to-Vertical (H/V) spectral ratio technique and recordings of ambient noise.The results of the H/V ratio show two peaks in about 20 percent of the locations,which are distributed mainly in and around the center and northern part of the Kathmandu Valley.The predominant frequencies vary from 0.5 Hz to 8.9 Hz in the study area,whereas the second resonance fiequency varies from 4 Hz to 6 Hz in the center and northern part of the valley.This indicates that the center and northern part of the valley have a wide range of resonance frequency due to two levels of impedance contrast- one may be from the surface layer and the other may be from the layer undemeath.These two levels of resonance indicate the importance of considering the effects of surface and lower layers during the planning and designing of infrastructures in the Kathmandu Valley.
文摘Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.
