利用小波分析重力的长期变化

运用小波滤波方法估算Chandler和周年项的潮汐因子.本文分析了四个台站(Brussels, Boulder, Membach以及Strasbourg)的观测记录,运用合成潮方法得到重力残差后,用Daubechies小波带通滤波器滤波残差,得到256～512 d时间尺度上的序列,根据标准差最小原则确定观测极潮周年和Chandler项的周期,然后利用最小二乘法估算它们的潮汐因子,同时给出未经模型改正的周年重力.由于高阶Daubechies小波构造的滤波器具有良好的频率响应,且能压制信号中的高阶异常成分,使滤波的信号更加光滑,因此计算结果具有更小的均方差,更加可靠.     

Improvement of edge effect of the wavelet time–frequency spectrum: application to the length-of-day series

 The edge effect in the wavelet time–frequency spectrum of a time series is treated. The time series is first extended on both ends by applying a non-linear model, namely the leap-step time series analysis (LSTSA) model, prior to the wavelet transform. The results of a series of simulation experiments and an application to the observed length-of-day (LOD) series demonstrate that the edge effect is effectively reduced this way. Thus, the application of the LSTSA model improves the wavelet time–frequency spectrum, especially enhancing the ability to detect the low-frequency signals. Received: 15 September 1998 / Accepted: 4 October 1999     

地磁场交变分量的地震前兆探索

阐述了阿拉木图预报试验场地磁动态观测时间序列中异常识别方法应用的主要成果.以往所利用稳定地磁场中异常探索的方法,在北天山条件下按构造磁效应未能得到积极的结果.在许多情况下,地磁场谱特性的相关函数、子波分析及异常和噪音的分位量纲计算都能识别作为地震前兆的中、短期异常.     

辽宁地区第一对流层顶高度变化特征分析

本文利用趋势分析、突变分析以及小波分析等方法对近42 a辽宁南部（大连）和北部（沈阳）第一对流层顶高度变化特征进行分析和比较,结果表明：近42 a,辽宁第一对流层顶高度除冬季以外其它三季和年呈下降趋势,南部地区的降幅明显大于北部;多年均值的年变化表现为在夏季最高、春秋季次之、冬季最低,各个季节南部高度值普遍高于北部;年际变化幅度在夏季最大,除冬季外,南部大于北部;发生气候突变的时段基本上都在1970年代中期前后;周期变化特征时空差异较大。     

Experiment and Simulation of Large Capacity Air-guns in Deep Structure Exploration

The air-gun source has important applications as a new, environmentally, green active source in regional scale deep exploration. In the past, the air gun source was used mainly in small-scale, high-resolution shallow oil exploration, but has a lack of adequate research in deep exploration. In order to study the selection of work parameters and field conditions of the air gun source in deep exploration, this paper does the following work: ① analyze the characteristics of the air gun source using air gun experiments; ② simulate the air gun signal and air gun-array signal based on the theory of free bubble oscillation to analyze the influence of bubble oscillation and study the wavelet energy and spectrum characteristics needed in deep exploration; ③ on the basis of theoretical simulation, study the influence of work parameters, such as air-gun capacity, work stress and depth on air gun signal and analyze the influence of air-gun array inspired moment and spacing of different air guns on air gun-array signals; and ④ study energy reflection and transmission coefficients for different underwater interfaces, which is very useful for choosing suitable field conditions.     

基于物理小波的频谱分解方法及应用研究

在地震资料频谱分解中,采用匹配地震子波的物理小波,依据地震信号的特征,用振幅、能量衰减率、能量延迟时间及地震子波的中心频率等四类参数构造基本小波,把地震信号分解在小波域,高频分量能够得到精细的刻画.本文以物理小波变换为工具, 给出了该变换中的核函数的选择方法,进而提出了基于物理小波变换的频谱成像方法.我们将此方法用于海上某油田河流相储层的描述,并与常规软件中的小波变换频谱成像结果进行了对比, 结果表明,本文提出的方法更能精细地刻画地质事件.     

Wavelet approximation of earthquake strong ground motion-goodness of fit for a database in terms of predicting nonlinear structural response

Reduced dimensionality representation of strong ground motion records as a superposition of a relatively small number of pulses is studied. Such representation is obtained by the expansion of velocity in orthogonal wavelet series using the Fast Wavelet Transform, and approximation by only the largest energy terms in the series. The Coiflet 5 wavelet family is used, which is orthogonal, smooth and nearly symmetric. The goodness of the approximation is examined for the EQUINFOS for USA Part I database, as representative of a large variety of strong motion records (it consists of 494 three-component records from 106 earthquakes recorded in the western US between 1933 and 1984). The goodness of fit is measured in terms of closeness of predicting several input and output characteristics of a nonlinear oscillator representing a structure: (i) energy of the input ground motion (proportional to integral of velocity squared), (ii) the peak input power, and (iii) the time of collapse of a bi-linear oscillator (considering also collapse due to dynamic instability). The results show very high degree of correlation of these characteristics as estimated from the actual record and from its approximations, even for small number of pulses (relative to the number that would represent the ground velocity exactly). Such reduced representation of strong ground motion is useful for extracting such pulses from strong motion records to study their nature, and for development of new algorithms for the synthesis of artificial earthquake strong motion records.     

阿图什6．7级地震近场地倾斜群体异常小波分析

利用小波分析独特的时频分析特性,对1996年阿图什6.7级地震前近场多台地倾斜连续观测资料进行了多尺度分解分析.在地震前震中附近的地倾斜台站几乎都接收到了相同频段的异常信号,周期为几天到十几天,异常信号出现在各个台站的时间也大致相同,主要集中在震前3～5个月,震前异常具有从外围向震中发展且幅度明显增强-向外围扩散-发震的时空演化特征.这些异常可能与1996年阿图什6.7级地震的孕育过程有关.     

小波变换在数字化水位观测资料分析中的应用

小波变换是近年兴起的信号处理方法,具有良好的时频局部化特征。它不但可以准确地描述瞬变信号的局部特征,还可以通过多尺度变换,从不同频带上观察信号的特征。利用小波变换的以上特征将其运用于数字化水位观测资料不同频率潮汐波的分离、同震水震波及震前水位长周期“前驱波”的识别、干扰与同震效应的识别等方面,都取得了较好的效果。     

Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis

In this study, a new method of stand-alone short-term spring snowmelt river flood forecasting was developed based on wavelet and cross-wavelet analysis. Wavelet and cross-wavelet analysis were used to decompose flow and meteorological time series data and to develop wavelet based constituent components which were then used to forecast floods 1, 2, and 6 days ahead. The newly developed wavelet forecasting method (WT) was compared to multiple linear regression analysis (MLR), autoregressive integrated moving average analysis (ARIMA), and artificial neural network analysis (ANN) for forecasting daily stream flows with lead-times equal to 1, 2, and 6 days. This comparison was done using data from the Rideau River watershed in Ontario, Canada. Numerical analysis was performed on daily maximum stream flow data from the Rideau River station and on meteorological data (rainfall, snowfall, and snow on ground) from the Ottawa Airport weather station. Data from 1970 to 1997 were used to train the models while data from 1998 to 2001 were used to test the models. The most significant finding of this research was that it was demonstrated that the proposed wavelet based forecasting method can be used with great accuracy as a stand-alone forecasting method for 1 and 2 days lead-time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year-to-year, and that there is a relatively stable phase shift between the flow and meteorological time series. The best forecasting model for 1 day lead-time was a wavelet analysis model. In testing, it had the lowest RMSE value (13.8229), the highest R² value (0.9753), and the highest EI value (0.9744). The best forecasting model for 2 days lead-time was also a wavelet analysis model. In testing, it had the lowest RMSE value (31.7985), the highest R² value (0.8461), and the second highest EI value (0.8410). It was also shown that the proposed wavelet based forecasting method is not particularly accurate for longer lead-time forecasting such as 6 days, with the ANN method providing more accurate results. The best forecasting model for 6 days lead-time was an ANN model, with the wavelet model not performing as well. In testing, the wavelet model had an RMSE of 57.6917, an R² of 0.4835, and an EI of 0.4366.