基于小波分析的基桩内部应力监测数据异常辨识

小波分析在时频域具有良好的局部化特征,采用小波分解方法可简单、快捷地计算出数据序列的奇异性指数,以检出数据序列中的随机突变信号。通过试验数据验证,奇异性指数对随机突变信号的检出是正确有效的。根据多传感器监测系统中突变信号的分布规律,进行异常属性自动辨识。研究说明基于小波分析的异常属性识别是一种新颖有效的方法。     

混沌序列WA-ELM耦合模型在滑坡位移预测中的应用

针对滑坡位移序列的混沌特性和传统时间序列预测模型的不足,提出了一种基于混沌时间序列的小波分解-极限学习机(WA-ELM)滑坡位移预测模型。该模型以滑坡位移序列混沌特性分析为基础,应用小波分析将位移序列分解为具有不同频率特征的分量,对各特征分量分别进行相空间重构并应用极限学习机进行预测,最后将各特征分量预测值叠加,得到原始位移序列的预测值。以三峡库区八字门滑坡为例,并与小波分析-支持向量机(WA-SVM)以及单独ELM模型进行对比研究。结果表明,基于混沌时间序列的WA-ELM模型预测精度较高且具有较好的通用性与稳定性,是一种有效的滑坡位移预测方法。     

基于经验模态分解法与小波变换的长周期大地电磁信号去噪方法

针对大地电磁信号具有非线性、非平稳和非最小相位的特点,提出了一种基于经验模态分解法结合小波变换的联合信号去噪方式,将时间序列信号通过经验模态分解,利用连续均方误差准则确定原始信号能量转折点,进而再使用小波阈值去噪法对剩余固有模态函数分量进行去噪,最后重构出消噪信号。通过对实测信号处理前后结果的对比,表明了本方法能够有效地应用于信号时域去噪。     

基于Wavelet-ANFIS和MODIS地表温度产品的青藏高原0 cm土壤温度估算方法

0 cm土壤温度是冻土模型的上边界条件, 连续的、 高质量的青藏高原0 cm土壤温度数据是进行准确冻土模拟的必要条件. 然而受复杂下垫面的影响, 遥感手段无法获取可靠的0 cm土壤温度. 利用自适应网络模糊推理系统(ANFIS)结合青藏高原实测资料建立遥感地表温度产品(LST)与0 cm土壤温度的关系, 以实现通过LST估算青藏高原逐日0 cm土壤温度. 研究了ANFIS的各种参数组合, 发现筛选合适的小波函数、 小波窗口、 小波层数建立起来的Wavelet-ANFIS模型能较准确实现估算0 cm土壤温度的目的. 验证表明, 估算结果与气象站点实测0 cm土壤温度绝对误差在2 K以下, 相关系数0.98以上. 考虑到原始MODIS LST误差在0~2 K之间, 该方法可以获取较为理想的0 cm土壤温度, 为冻土模型提供准确的上边界输入.     

三参数小波及其在薄互层地震资料分析中的应用

在薄互层地震资料分析中,以三参数小波为分析小波,针对薄互层地震信号的特点,恰当地选取三参数小波中的参数,使其不仅适合于分析包含慢变频率和振幅分量的信号,而且也适合于包含快变分量的信号;该方法既能刻画薄互层的沉积旋回,同时也能用于研究薄互层内部的局部结构。通过模型和实际资料算例验证了其有效性,在进一步完善后有望成为岩性勘探中的有效方法。      

小波模变换技术在探地雷达资料中的应用

通过研究探地雷达信号的小波变换的模特征点的变化规律和特征,揭示了探地雷达信号奇异点和其小波变换模极值的关系。通过这些关系的研究,对小波各级分解的模特征点进行变频域处理。最后对处理后的地质雷达信号,利用非线性最小二乘方法求解李氏指数,从而进行地质体边界的检测。无论从理论模型的论证还是实际资料的处理都取得了较好的结果。     

重力异常小波多分辨分析分解阶次的确定

分解阶次的确定是重力异常小波多分辨分析中的基本问题之一.以塔里木及天山地区布格重力异常数据为例, 通过讨论信号长度、小波母函数的支撑长度与分解阶次的关系, 以及对比分解结果与大地水准面异常的特征, 提出了分解阶次的确定方法.发现对于该重力异常数据, 使用bior3.5小波, 5阶小波分解细节可以避免小波母函数特征过多的干扰, 其结果与相应大地水准面异常特征比较吻合, 而6阶小波分解细节结果却与相应大地水准面异常特征存在较大差异.从信号处理和重力异常的地球物理意义两方面表明, 恰当的小波分解阶次为5阶, 更高阶次的分解结果并不合理.所使用的方法对重力异常小波分解阶次的确定是有效和易行的, 为小波分析在重力场数据处理中的应用, 进行了新的有益探索.      

基于变分模态分解的机器学习模型择优风速预测系统

精准的风速预报对风力发电系统具有重要意义,但风速信号自身固有的随机性使其波动复杂且不可控,以往的研究采用单一或固定的组合模型很难把握风速序列的特征.提出一种基于分解的机器学习模型择优风速预测系统,采用变分模态分解算法降低原始风速序列的复杂度.进而利用模糊神经网络、非线性自回归神经网络、Elman神经网络、反向传播神经网络和自回归差分移动平均模型构成机器学习模型择优系统,分别对子序列的验证集进行预测,通过均方根误差等性能指数选择其最优模型,提高了整体模型的预测精度.试验采用宁夏地区4个站点的实测风速数据,仿真实验结果表明,所提模型相比于单模型以及较新的深度学习组合模型,具有更高的预测精度.     

基于局部均值分解的地震信号时频分解方法

在分析地震资料时,因吸收和衰减等原因,地震信号往往呈现出非稳态性。时频分解方法能将地震信号分解成多个稳态的子成分,从而为描述和分析地震信号的属性提供了便利,如短时傅里叶变换、小波分析、经验模式分解(EMD)等方法。本文引入了一种新的时频分析方法——局部均值分解(LMD)。该方法将地震信号按其时频属性分解成多个乘积分量信号(PFs),较EMD分解所得的固有模态函数(IMFs)保留了更多的局部信息,同时模态混叠效应更少。对模型数据和实际数据的处理结果验证了LMD方法能够合理地分解地震信号,更准确地描述地震资料中不同时间尺度的构造信息,为进一步的地震数据处理和解释提供参考。     

基于EMD去噪方法研究及其在地质勘探中的应用

论述了EMD分解的基本原理,研究了利用EMD分解进行信号去噪的方法.EMD把信号按照不同的特征尺度分解为不同频带的IMF分量,将含有噪声的高频IMF分量剔除,选择低频或者指定频带的IMF进行信号重构,即可达到去噪的目的.仿真信号与实测数据的处理结果都表明,该方法不但有效地去除信号中的确定性噪声和随机噪声,而且尽可能地保持了有效信号,减少了信号损失,提高了数据处理的准确性.     

基于离散小波变换的油气检测技术

小波变换的时频双重局部化特性，是处理非平稳地震信号的有效方法。尝试利用离散小波变换提取地震道信号的特征参数。并根据提取的小波参数，寻求其变化规律，从而直接进行油气检测。首先，在选择不同的小波母函数的基础上，对给定的地震道数据进行小波特征参数提取；最后对小波参数值进行分析、比较以优选油气识别的小波特征参数；然后进行小波特征参数的综合；再根据已知井位的油气属性，确定归类门槛值。通过研究认为，该方法具有可行性。     

Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE?>?0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.     

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)

Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteorological drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R ²) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet.     

基于小波变换和GALSSVM的边坡位移预测

边坡变形是一个受多种因素综合作用的复杂非线性动力学演化过程,用现有的物理模型来解决边坡变形的预测问题有很大难度。大量的研究工作表明,用实测的边坡位移时间序列来预测边坡未来变形更为准确,而将多种方法组合起来进行预测成为研究的主要趋势。在此基础上,建立了一种基于小波变换和进化最小二乘支持向量机(GALSSVM)的边坡位移预测模型。首先利用小波变换将边坡时间序列分解为低频分量和高频分量,然后利用互信息法和伪近邻法得到各分量的时间延迟和嵌入维数并进行相空间重构,再根据各个相空间的特点建立相应的GALSSVM预测模型,最后把各分量的预测结果进行小波重构,重构后的结果即为最终的边坡位移预测结果。对丹巴滑坡预测研究表明,这种新的预测模型具有较高的预测精度,可以应用于实际工程     

Dynamic Model Re-construction and Corresponding Abnormal Characteristics Anatomy of Subtropical High based on Actual DataTime SeriesSamples

Aiming at the abnormal activity of West Pacific Subtropical High(WPSH) inducing the serious flood disaster in 1998, and considering the complex seasonal activity of WPSH in summer, based on the actual geopotential height field time series observational data in 1998, a non-linear dynamic model of WPSH was reconstructed by using the idea of dynamic system retrieval and genetic algorithms. Combined with the synchronous actual weather process, the corresponding dynamic characteristics and variation mechanism of WPSH, such as the equilibrium state destabilization and bifurcation of the WPSH system, were analyzed and discussed. The research showed that due to the advantages of global optimization and parallel calculation of GA, a nonlinear dynamic model could be reasonably and quickly reconstructed, and the reconstructed nonlinear dynamic model of WPSH derived from the actual abnormal case data could be used for describing objectively and explaining reasonably the basic characteristics and the abnormal activity process of WPSH. The dynamic discussion showed that the configuration and diversification of WPSH system equilibriums, such as bifurcation, catastrophe had a good matching with the actual medium short period abnormal activity of WPSH, such as northward jumping and southward droping, as well as double-ridge line pattern. The modality of WPSH geopotential height field induced by multi-equilibriums destabilization was more complex than that of by single-equilibriums bifurcation, and their exhibition patterns were more different. With the forcing parameters increaseing, closing with and exceeding critical value, the WPSH system equilibrium state destabilization or bifurcation would occur. Especially, when the equilibrium state jump/drop from low/high to the high/low value, corresponding WPSH system behaved as a leap northward /fall southward, moreover, when the stable equilibriums state from 1 to 2, corresponds with the pattern of “double-ridges” phenomenon of WPSH, contrariwise, “double-ridges” phenomenon would disappear. A meaningful technique route was provided to the mechanism research and dynamic characteristic discussion for the complex weather system of hardly modeling such as WPSH.     

基于奇异值分解和小波阈值方法的地震资料处理方法

奇异值分解是一种基于代数特征值的提取方法,小波变换是一种时间频率域的去噪方法,两者在去噪方面各有特点。将奇异值分解和小波阈值去噪的方法有机地结合起来,用于消除地震勘探资料中的随机噪声。仿真实验显示对于较低信噪比资料仍有很好的处理效果。     

基于小波包能量谱的建（构）筑物爆破地震安全评估

基于现场实测爆破振动数据,采用小波包分析技术对爆破振动信号进行了时频特征分析。根据小波包变换的分层分解关系,推导出爆破振动信号不同频带的小波包频带能量,小波包频带能量能同时反映爆破振动3要素（振动的强度、频率和持续时间）的作用影响。基于小波包能量谱,获得了爆破振动信号不同频带能量的分布特征,根据受控结构体对爆破振动动态响应特性,首次建立了能考虑爆破振动3要素以及受控建（构）筑物本身的动态响应特性（固有频率和阻尼比）等因素综合的安全判据--响应能量判据,并用工程实例验证了该判据的可行性和可靠性。该判据较之现行的速度-频率安全判据来说,能准确地描述爆破振动对受控建（构）筑物的影响程度,更能全面地评估建（构）筑物爆破地震效应。     

Evaluations and Ensemble Approaches of Western-Pacific Subtropical High and South-Asian High Ensemble Forecasting in GRAPES-GEPS

Based on the operational standard indices, the prediction skills of the Western-Pacific Subtropical High (WPSH) and South-Asian High (SAH) using 2019 real-time forecasts derived from the Global Ensemble Prediction System of GRAPES (GRAPES-GEPS) in China Meteorological Administration (CMA) Numerical Prediction Center were evaluated and the effects of different ensemble approaches on the prediction skills of WPSH and SAH indices were further investigated in this study. The results show that for WPSH, the GRAPES-GEPS has its highest prediction skill for the ridge line index, considerably high skill for the intensity and area indices, but relatively low skill for the western boundary index, and for SAH, it has comparatively high skill for the intensity and center latitude indices, but relatively lower skill for the center longitude index. Prediction errors of the GRAPES-GEPS for the WPSH forecasts are featured by the weaker intensity and area and the more eastward center position, compared with the observation, which can be effectively reduced by employing the maximum/minimum approach from ensemble members, relative to the ensemble mean approach. By direct comparison, prediction errors of the GRAPES-GEPS for the SAH forecasts are featured by the weaker intensity and the more southward center position, which tends to be slightly reduced using the ensemble mean approach. Finally, for the extreme forecast, the maximum approach provides superior performance for both WPSH and SAH than the ensemble mean approach, which can be validated in terms of the two extreme cases. These results clearly indicate that the maximum approach could better improve the GRAPES-GEPS performance for the extreme forecasting of the two primary circulation patterns than the traditional ensemble mean approach.     

Application of neural network and ANFIS model for earthquake occurrence in Iran

This study examined the spatial-temporal variations in seismicity parameters for the September 10^th, 2008 Qeshm earthquake in south Iran. To this aim, artificial neural networks and Adaptive Neural Fuzzy Inference System (ANFIS) were applied. The supervised Radial Basis Function (RBF) network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems. The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern. The data preprocessing that included normalization and Principal Component Analysis (PCA) techniques was led before the data was fed into the RBF network and ANFIS model. Although the accuracy of RBF network and ANFIS model could be evaluated rather similar, the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock. A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches. The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areas.     

基于小波变换的分频技术在碳酸盐岩储层预测中的应用

塔河油田奥陶系海相碳酸盐岩储层埋藏深,地震反射信号弱,且风化壳多为杂乱反射,储集体类型复杂多样,有裂缝、溶洞、孔洞等类型,纵横向非均质性强,勘探难度大,属世界级难题。针对上述难题,这里引入基于小波变换的地震分频技术,首先针对该区实际地质情况设计模型,检验了该方法的可行性;然后对该区碳酸盐岩储层进行了预测,预测结果与实钻数据相一致,由此可见,分频解释技术可以有效刻划碳酸盐岩储层空间分布特征。研究表明,经分频处理后的地震数据,其解释分辨率高于常规地震主频所能达到的分辨能力,不需要建立假设的模型,尊重原始地震数据,减少了人为因素干扰带来的假象。该项技术在确定油藏边界、储层预测方面具有独特的优势。