面向目标自适应三维大地电磁正演模拟

本文将面向目标的自适应算法应用于三维大地电磁数值模拟.使用基于非结构网格的矢量有限单元法对起伏地表大地电磁正演模拟问题进行求解.使用利用垂向电流密度在物性界面上的连续性对后验误差进行估算的算法指导网格优化.由于全局自适应算法针对观测点优化网格的能力较差,本文通过求解正演问题的对偶问题计算后验误差的加权系数,并对相关加权系数进行改进,从而实现了面向目标的自适应算法.与传统基于结构化网格的电磁正演算法相比,采用非结构网格能够更好地拟合起伏地表和地下不规则异常体.由于使用了面向目标的自适应算法,本文能够使用更少的网格达到较高的计算精度.通过对比本文模拟结果与半空间响应和全局自适应算法计算结果,并通过对比使用改进前和改进后加权系数得到的网格剖分结果验证了本文算法的有效性.

     

面向目标自适应有限元法的带地形三维大地电磁各向异性正演模拟

传统三维大地电磁各向异性模拟均是基于规则六面体网格,计算精度有限且较难拟合复杂地质条件.本文采用面向目标自适应非结构矢量有限元法,对三维大地电磁各向异性介质进行模拟.首先从电场双旋度方程出发,利用伽辽金方法建立变分方程;然后利用电流密度连续性条件构建适合大地电磁各向异性问题的加权后验误差估计方法,实现面向目标的网格自适应正演;最后通过典型算例分析各向异性对网格自适应和大地电磁响应的影响特征以及各向异性的识别方法.本文算法能够高精度地拟合起伏地表和任意各向异性介质,适用于分析复杂地电条件大地电磁响应特征,为提高大地电磁资料解释水平提供了理论基础.

     

On-line estimation of unit hydrographs using the wavelet-based LMS algorithm / Estimation en ligne des hydrogrammes unitaires grâce à l'algorithme des moindres carrés moyens à base d'ondelettes

Abstract

This study applies the discrete wavelet transform (DWT) to decompose the unit hydrograph, thereby generating parsimonious reparameterizations of the unit hydrograph. A model compression method is then employed to significantly compress the unit hydrograph requiring that fewer coefficients be estimated. Moreover, a wavelet-based linearly constrained least mean squares (WLCLMS) algorithm is also used to estimate on-line the wavelet coefficients of the unit hydrograph. The updated wavelet coefficients of the unit hydrograph, convoluted with effective rainfall input in the wavelet domain, allow for accurate prediction of one-step-ahead runoff in the time domain. The proposed approach allows the unit hydrographs to vary in time and accurately predicts runoff from a basin in Taiwan, thus making it highly promising for flood forecasting.     

A method for absorption compensation based on adaptive molecular decomposition

In this paper, we present a new method for seismic stratigraphic absorption compensation based on the adaptive molecular decomposition. Using this method, we can remove most of the effects resulting from wavelets truncation and interference which usually exist in the common time-frequency absorption compensation method. Based on the assumption that the amplitude spectrum of the source wavelet is smooth, we first construct a set of adaptive Gabor frames based on the time-variant properties of the seismic signal to transform the signal into the time-frequency domain and then extract the slowly varying component (the wavelet’s time-varying amplitude spectrum) in each window in the time-frequency domain. Then we invert the absorption compensation filter parameters with an objective function defined using the correlation coefficients in each window to get the corresponding compensation filters. Finally, we use these filters to compensate the time-frequency spectrum in each window and then transform the time-frequency spectrum to the time domain to obtain the absorption-compensated signal. By using adaptive molecular decomposition, this method can adapt to isolated and overlapped seismic signals from the complex layers in the inhomogeneous viscoelastic medium. The viability of the method is verified by synthetic and real data sets.     

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

ABSTRACT

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R² = 0.88; NS = 0.88; RMSE = 142.30 (m³/s); MAE = 88.94 (m³/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R² = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m³/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.     

黑河中游荒漠绿洲农业适应气候变化技术研究

以我国第二大内陆河流域——黑河流域的中游荒漠绿洲为例,基于长期定位试验成果,研究了绿洲农业适应气候变化的技术及其潜力。结果表明,在田间水平上,垄沟灌溉种植、主栽作物与伴生植物混播种植、优化水肥管理、建立枣粮复合系统是绿洲农业适应气候变化的有效技术;在绿洲水平上,调整农业种植结构、加大农田林网规格和减少农田林网的灌溉次数、降低防风固沙体系中高耗水树种——杨树的比例等技术是应对气候变化的重要途径。综合评价结果表明,在黑河中游绿洲,通过推广上述技术,在不降低绿洲农业产值和不影响绿洲生态系统稳定性的前提下,初步估算每年可节水2.96×108m3,抵消了气温上升大约1℃所带来的蒸散发消耗量。     

微电网逆变器的自适应滑模控制策略研究

恒功率负载的输入阻抗具有负增量阻抗特性,其与前级电源的相互作用将对微电网系统的稳定运行产生重大影响.因此,研究带有恒功率负载的微电网稳定性具有实际意义.本文提出了一种改进的滑模控制方法,将其用于改善微电网系统的大信号稳定.首先在系统建模中考虑了系统的参数不确定性及外部干扰,使控制具有较好的鲁棒性;其次在滑模控制中引入自适应算法,对总扰动上界进行了估计,提高了控制的准确性;最后通过Matlab／Simulink建立了微电网模型,通过仿真验证了所提出策略的有效性.     

基于小波变换系数自适应阈值法在语音去噪中的应用

在软阈值去噪算法基础上,根据小波变换后信号与噪音具有的奇异性,提出无偏风险自适应阈值估计算法,并应用于语音信号的去噪处理,实验结果表明该算法的最小均方误差低于其他的去噪算法。