马鞍山地震台人工爆破与天然地震记录特征差异

鉴于马鞍山地区矿山较多,常有人工爆破发生,为了快速识别事件性质,分析了人工爆破与天然地震在P波初动方向、纵横波振幅比、衰减速度、频谱、发震时间等方面的差异。     

Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of the Narmada River,India

Most of the studies on Artificial Neural Network (ANN) models remain restricted to smaller rivers and catchments. In this paper, an attempt has been made to correlate variability of sediment loads with rainfall and runoff through the application of the Back Propagation Neural Network (BPNN) algorithm for a large tropical river. The algorithm and simulation are done through MATLAB environment. The methodology comprised of a collection of data on rainfall, water discharge, and sediment discharge for the Narmada River at various locations (along with time variables) and application to develop a threelayer BPNN model for the prediction of sediment discharges. For training and validation purposes a set of 549 data points for the monsoon (16 June-15 November) period of three consecutive years (1996–1998) was used. For testing purposes, the BPNN model was further trained using a set of 732 data points of monsoon season of four years (2006–07 to 2009–10) at nine stations. The model was tested by predicting daily sediment load for the monsoon season of the year 2010–11. To evaluate the performance of the BPNN model, errors were calculated by comparing the actual and predicted loads. The validation and testing results obtained at all these locations are tabulated and discussed. Results obtained from the model application are robust and encouraging not only for the sub-basins but also for the entire basin. These results suggest that the proposed model is capable of predicting the daily sediment load even at downstream locations, which show nonlinearity in the transportation process. Overall, the proposed model with further training might be useful in the prediction of sediment discharges for large river basins.     

大岗山水库地震台网水库诱发地震常规定位方法对比

采用MSDP软件中的单纯型、HypoSat和LocSat定位方法,从震源深度及残差等方面,对比分析2014年大岗山水库区域内123次M_L≥2.0地震的定位结果,选择适用于水库诱发地震的最佳定位方法,并以库区17次定点爆破事件为例,验证最佳定位方法的可靠性。结果认为,LocSat定位方法更适用于水库诱发地震。     

Artificial neural network modeling of cross-shore profile on sand beaches: The coast of the province of Valencia (Spain)

The paper describes the training, validation, testing, and application of models of artificial neural networks (ANN) for computing the cross-shore beach profile of the sand beaches of the province of Valencia (Spain). Sixty ANN models were generated by modifying both the input variables as the number of neurons in the hidden layer. The input variables consist of wave data and sedimentological data. To select and evaluate the performance of the optimal model, the following parameters were used: R², absolute error, mean absolute percentage error, and percentage relative error. Finally, the results are compared with the numerical model proposed by Aragonés et al. (2016b Aragonés, L., Y. Villacampa, F. J. Navarro-González, and I. López. 2016b. Numerical modelling of the equilibrium profile in Valencia (Spain). Ocean Engineering, 123:164–73. doi:10.1016/j.oceaneng.2016.07.036[Crossref], [Web of Science ®] , [Google Scholar]) for the equilibrium profile in the study area. The results show a mean absolute error of 0.21?m compared to 0.33?m Aragones’ model, significantly improving the results of the numerical model in the bar area around de Valencia Port. In addition, when comparing the results with other methods currently used (Dean’s or Vellinga formulation), the errors of these compared to ANN are of the order of 167 and 1538% higher, respectively.     

坝基开挖产生的爆破松弛带厚度确定

爆破开挖会产生一定范围的爆破松弛带,其岩体工程特性会降低.确定爆破松弛带的厚度具有重要的工程意义,在此主要介绍用纵波速等方法确定爆破松弛带厚度.     

Generation of Time History Compatible with Multi-Damping Response Spectra

Based on the method developed by M. Hirasawa and M. Watabe (1992), the authors deduced the related mathematical formulas. Taking an artificial ground motion compatible with single-damping as the original time history, a small corrected time history was calculated by solving linear equations using a singular value decomposition method. We performed iteration to obtain the simulated earthquake motion compatible with multi-damping within a certain accuracy. The influences of selective matching frequencies are discussed preliminarily. Although the current criterion still has no explicit demand on different numbers of matching frequencies for various damping factors, it should be taken into account in practice.     

额济纳旗人工绿洲生态建设试验研究

根据额济纳旗弱水下游近河戈壁区的立地条件和气候特点进行合理布局,利用春季河水浇灌补充地下水位,采取开沟积沙保墒,选用乡土树种,乔灌结合,封造结合等严格的管理技术措施,１５ａ造林１３００ｈｍ２以上,年经济效益１４０多万元。通过边营造边观测取得数据,从防风固沙,改善土壤结构,增加植被覆盖度、生物产量以及社会效益等方面进行分析研究。结果表明,近河戈壁是可以逆转为绿洲的,这为综合整治居延绿洲环境提供了科学依据。     

长沙电力学院3号教学楼人工挖孔灌注桩工程地质问题及其处理措施

长沙电力学院 3号教学楼人工挖孔桩基的施工条件极差。经灵活采用多种经济实用的措施处理 ,成功地解决了流砂、流泥、管涌、坍塌、护壁吊烂与整体失稳等技术难题。     

地震活动增强方式及其复杂性

分析了地震活动增强特征参数W1与相应其它参数的关系以及在一些6级左右地震前W1值的动态图像变化。结果表明中强以上地震前的地震活动“增强”通常表现为在强度上的增高、时间和空间上的丛集以及强度增高与时空丛集同时出现3种方式。一些6级左右地震前W1值动态图像变化表明地震过程往往表现为多应力集中区相互作用和影响的演变过程。     

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.