The dehydration melting of the natural rock at high pressure is important to investigating the magma formation in the earth’s interior. Since the 1970s, a lot of geological scientists have paid more atten- tion to the dehydration melting of the natural rock[1―5]. Previous experiments of dehydration melting and observations of fieldwork argued that the dehy- dration melting of the rock was probably the most important fashion for the melting of the lower crust rock[6―12]. The genesis of most … 相似文献
A numerical study for estimating the tidal open boundary conditions of a shelf current modrl from tb coastal tidal observations
is presented. The method is based on the optimal control/adjoint method. A lrast square fitting of the model state to simulated
data is used. Two ideal domains and coastlines are considered. Using the IAP shallow. water model and its adjoint model, some
identical twin experiments are carried out to test efficiency and lirnilsd of the method. The results show that the adjoint
method can efficiently estimate the open boundary conditions well for gulf/bay like domains. The adjoint method seems to have
great potential to improve the accuracy of tide and shelf current modeling in coastal regions.
Project supported hy the National Natural Science Fuundation of China (Grant No. 49376256) 相似文献
Crushed rock subgrade, as one of the roadbed-cooling methods, has been widely used in the Qinghai-Tibet Railway. Much
attention has been paid on the cooling effect of crushed rock; however, the mechanical properties of crushed rock are
somehow neglected. Based on the discrete element method, biaxial compression test condition for crushed rock is compiled
in FISH language in PFC2D, and the natural shape of crushed rock is simulated with super particle "cluster". The effect
of particle size, crushed rock strength and confining pressure level on overall mechanical properties of the crushed
rock aggregate are respectively analyzed. Results show that crushed rock of large particle size plays an essential framework
role, which is mainly responsible for the deformation of crushed rock aggregate. The strength of gravel has a great
influence on overall mechanical properties which means that strength attenuation caused by the freeze thaw cycles cannot
be ignored. The stress-strain curves can be divided into two stages including shear contraction and shear expansion at
different confining pressures. 相似文献
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.