In this paper,the dispersion curves of the Rayleigh wave and Love wave were extracted from the seismic noise records of 25 broadband stations of the Fujian Seismic Network, and inverted for the lithosphere velocity structure. Furthermore,the velocity model was verified by the seismic explosion observations. Our results indicate that the resolution of the lithosphere velocity structure obtained by this method is good in the shallow part,but in the deep part,inversion accuracy for the wave velocity structure is low,which is caused mainly by the small inter-station distance chosen in the paper. Thus the wave dispersion curves have high accuracy in the short-period part,but the warp of the wave dispersion curve in long-period part is large. Considering the results from both the noise inversion and the traditional inversion,we finally present a new velocity model,and the theoretical travel time calculated with the new model matches the explosion travel time very well. 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.
