汶川8.0级地震前定点形变异常特征

对汶川8.0级地震震中周围800 km范围内近50个定点形变台站资料作小波变换处理,发现从震前3个月开始,30个台站资料处理结果显示有异常出现.距震中200 km以内的台站,其异常信息主要集中在小波分解的细节部分第6层;距震中200-550 km范围内的台站,其异常主要集中在第7层或第8层;距震中550-650 km范围内的台站,其异常主要集中在第8层或第9层.表明异常信息的频率随着各台站距离震中的远近呈现由低到高的特征.     

零炮检距剖面与小波剖面制作系统

讨论了利用多项式拟合和小波变换制作零炮检距剖面和小波剖面的方法,并用VisualC++语言开发了基于Windows操作平台下的地震资料零炮检距剖面与小波剖面制作系统。利用最佳拟合算法与最佳小波基函数,使用制作系统对煤田实际地震资料进行了处理,取得了满意的地质效果。      

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling

In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic and real-world streamflow data from two catchments with different hydrologic regimes.     

Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeler from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniformly over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter combinations corresponding to the chosen eigenvectors are multiplied to obtain the pilot point values. The model can thus be transformed from having many-pilot-point parameters to having a few super parameters that can be estimated by nonlinear regression on the basis of the available observations. (This technique can be used for any highly parameterized groundwater model, not only for models parameterized by the pilot point method.)     

小波变换方法在辽宁跨断层形变资料分析中的应用

运用二进小波变换方法,分析了辽宁省跨断层观测资料不同尺度的小波变换的细节信号变换特征。研究结果表明,不同尺度小波变换分解的细节信号显示出不同的时变特征,可以识别和提取原始测值中不易分辨的震前异常。当尺度为3时的细节信号的年周期特征被打破,或者当尺度为3时的细节信号变化超出2倍均方差时,测点周围250km左右范围内发生中强地震的可能性很大。     

Wavelet analysis of lightning return stroke

In this investigation, time series consisting of electric field waveforms of 15 positive return strokes and 10 negative return strokes were analyzed. The data came from a summer thunderstorm in March in a range of about 200 km around São José dos Campos, São Paulo. The electric field recording system consisted of a flat plate antenna with a decay time constant of 260 μs and a sample rate of 800,000 samples per second. The bandwidth observed was up to 100 kHz and the recording system was synchronized with GPS time and located at São José dos Campos. Wavelet analysis of the electric field waveforms was done in order to investigate the behavior of the return stroke spectrum in time. The return stroke was suggested to be divided into two portions: initial stage and overshoot (for far return stroke) and initial stage and ramp (for close return stroke). The return stroke power spectrum was observed to be distributed in a frequency range with the peak value also distributed in a fraction of this range. Power peaks for ramps are stronger than power peaks for initial stage and overshoot. Finally, it was observed how powerful the wavelet is in the analysis of lightning.     

Robust LS-SVM regression for ore grade estimation in a seafloor hydrothermal sulphide deposit

Due to the geological complexities of ore body formation and limited borehole sampling, this paper proposes a robust weighted least square support vectormachine (LS-SVM) regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each in complete sample. Then, a weighted K-nearest neighbor (WKNN) algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimization (PSO) algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by randomdivision. Coupled simulated annealing (CSA) and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight (IDW), ordinary kriging (OK), and back propagation (BP) neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the othermethods, and has strong predictive and generalization ability.     

测井声波时差反演重构技术研究及应用

利用测井声波时差、自然电位和伽玛重构声波时差,分析了以上3种曲线结果对地质体响应方面的机制差异,给出了这些曲线的深度相对移动校正的方法。研究了多条测井曲线去噪的协方差矩阵特征向量滤波方法。在此基础上,利用小波多尺度分解技术进行声波时差曲线重构。在分析这3种测井曲线不同尺度小波分解结果的信号和噪音特点后,利用相邻分解尺度相关滤波技术,对各种曲线的小尺度(高频)分解结果进行滤波处理。为保证重构声波曲线的真实性,深入分析了多曲线、多尺度分解结果的冗余性(相关性)问题;利用特征值技术,对高频多尺度分解分量进行了非相关(正交)分析,最后实现了声波时差曲线的重构,并对重构结果与钻井岩心录井资料进行了对比验证,重构后的声波曲线在区分砂、泥岩性分辨率方面具有明显的提高     

用小波变换方法提取地下流体观测异常信息

首先对小波分析方法作了简单介绍,然后运用该方法,对1992～2002年上海地区9口水氡观测井的资料进行了6阶小波变换,提取的地震异常信息与实际地震有较好的对应关系;对1983年山东荷泽5.9级地震和1998河南清丰4.0级地震前聊古1井的氡观测资料进行了小波分析,发现1～6阶小波系数均出现异常;对1999年山东苍山5.2级地震前枣庄十里泉井的水汞观测资料进行了小波分析,发现1～6阶小波系数也出现异常.因此,运用小波分析方法可以有效地提取地震前兆异常信息.     

Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting

The accuracy of the wavelet regression (WR) model in monthly streamflow forecasting is investigated in the study. The WR model is improved combining the two methods—the discrete wavelet transform (DWT) model and the linear regression (LR) model—for 1‐month‐ahead streamflow forecasting. In the first part of the study, the results of the WR model are compared with those of the single LR model. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in Eastern Black Sea region of Turkey are used in the study. The comparison results reveal that the WR model could increase the forecast accuracy of the LR model. In the second part of the study, the accuracy of the WR model is compared with those of the artificial neural networks (ANN) and auto‐regressive (AR) models. On the basis of the results, the WR is found to be better than the ANN and AR models in monthly streamflow forecasting. Copyright © 2009 John Wiley & Sons, Ltd.