Hydrothermal vent incidence was once thought to be proportional to the spreading rate of the mid-ocean ridges (MORs). However, more and more studies have shown that the ultraslow-spreading ridges (e.g., Southwest Indian Ridge (SWIR)) have a relatively higher incidence of hydrothermal venting fields. The Qiaoyue Seamount (52.1°E) is located at the southern side of segment #25 of the SWIR, to the west of the Gallieni transform fault. The Chinese Dayang cruises conducted eight preliminary deep-towed surveys of hydrothermal activity in the area during 2009 and 2018. Here, through comprehensive analyses of the video and photos obtained by the deep-towed platforms, rock samples, and water column turbidity anomalies, a high-temperature, ultramafic-hosted hydrothermal system is predicted on the northern flank of the Qiaoyue Seamount. We propose that this hydrothermal system is most likely to be driven by gabboric intrusions. Efficient hydrothermal circulation channels appear against a backdrop of high rock permeability related to the detachment fault. 相似文献
Natural Hazards - This research selected the Qipan gully as a study area for field investigation. The vulnerability of the population to debris flows in Qipan gully was assessed. Several valuation... 相似文献
ABSTRACTThe spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns. 相似文献
This article explored China's urban employment dynamics with particular focus on the city size effect.Big data derived from the largest recruitment website were used to ex-amine the direct and indirect impacts of city size on employment demand by using mediating and moderating models.We also investigated the roles of the government and location fac-tors which have seldom been considered in literature.Results showed that the concentration degree of new jobs is higher than that of stock employment and population across cities,implying a path dependency mechanism of job creation and employment expansion.Mean-while,numerous job posts in inland central cities are probably a symptom of more even dis-tribution of employment in future China.Econometric models further verified the significant correlation between city size and job creation.Moreover,industrial diversity,fixed asset in-vestment,and spatial location have heterogeneous effects on employment demand in cities of different sizes and different levels of administration.These results can not only deepen our understanding of the crucial role of city size in urban employment growth but also demon-strate the future trend of labor and population geography of China.Policy implications are then proposed for job creation in cities of China and other developing countries. 相似文献
With the depletion of mineral resources on land, seafloor massive sulfide deposits have the potential to become as important for exploration, development and mining as those on land. However, it is difficult to investigate the ocean environment where seafloor massive sulfide deposits are located. Thus, improving prospecting efficiency by reducing the exploration search space through mineral prospectivity mapping (MPM) is desirable. MPM has been used in the exploration for seafloor deposits on regional scales, e.g., the Mid-Atlantic Ridge and Arctic Ridge. However, studies of MPM on ultraslow-spreading ridges on segment scales to aid exploration for seafloor massive sulfide have not been carried out to date. Here, data of water depth, geology and hydrothermal plume anomalies were analyzed and the weights-of-evidence method was used to study the metallogenic regularity and to predict the potential area for seafloor massive sulfide exploration in 48.7°–50.5° E segments on the ultraslow spreading Southwest Indian Ridge. Based on spatial analysis, 11 predictive maps were selected to establish a mineral potential model. Weight values indicate that the location of seafloor massive sulfide deposits is correlated mainly with mode-E faults and oceanic crust thickness in the study area, which correspond with documented ore-controlling factors on other studied ultraslow-spreading ridges. In addition, the detachment fault and ridge axis, which reflect the deep hydrothermal circulation channel and magmatic activities, also play an important role. Based on the posterior probability values, 3 level A, 2 level B and 2 level C areas were identified as targets for further study. The MPM results were helpful for narrowing the search space and have implications for investigating and evaluating seafloor massive sulfide resources in the study area and on other ultraslow-spreading ridges.