基于相关迭代的非因果匹配滤波器多次波压制方法

针对常规多次波压制(SRME)方法存在的缺陷进行了改进.首先在多次波模型道预测阶段,采用数据相关和迭代更新的策略预测多次波模型道,该方法降低了SRME方法采用数据空间褶积对输入数据要求严格的限制,提高了对近偏移距缺失和空间假频数据的适应性.在匹配相减阶段,本文设计了一种非因果非平稳的匹配滤波器,该滤波器可以对整道多次波模型进行处理,而且当预测多次波模型道滞后于实际多次波时,也能够对多次波模型道进行很好的匹配.模型数据和实际数据试算证明该方法在多次波模型道预测和匹配相减阶段的优越性.     

波动方程偏移速度建模:一种直接反演方法

基于炮域波动方程叠前深度偏移所产出的角道集,发展了不需要多次迭代的波动方程偏移速度建模方法.文中分析了炮域波动方程偏移生成角道集的方法,给出了非均匀介质中角道集同相轴曲率与速度误差的定量关系,发展了基于同相轴曲率的速度模型直接更新算法.在初始模型较合理的情况下,应用一次炮域波动方程偏移计算,即可得到较准确的速度模型.这...     

基于CSP道集的速度分析与建模

对于低信噪比地震资料,现行的叠前偏移方法应用效果欠佳的一个重要原因就是,目前的各种速度分析方法在信噪比太低时都不能建立准确的速度模型.共散射点(CSP)道集聚集了所有可能的散射能量,具有更高的覆盖次数和更大的偏移距范围.因此,对于低信噪比资料,基于CSP道集的速度分析具有一定的优越性.本文首先简单介绍了CSP道集的形成过程和基于CSP道集的速度分析方法的基本原理,提出了一种从CMP道集到CSP道集的快速映射方法,并用一个加权系数消除了映射噪音,最后用模型和实际资料验证了基于CSP道集的速度分析与建模方法的可行性和实用性.     

基于模型道的转换波时变静校正

由于转换横波传播路径的非对称性,使得转换横波的静校正与纵波有很大的不同.目前大多数静校正方法都是基于地表一致性假设提出的,而抽取转换波CCP道集的方法通常会使炮检关系破碎,同一道中不同时间的转换波数据对应不同的炮点与接收点位置.为此,提出了一种基于模型道的时变静校正方法.该方法以模型道波峰为基准将道集分为不同的时窗,各时窗中各采样点按一定准则分别进行时移校正.最后,给出了该方法在模型数据以及和顺二维三分量转换横波静校正处理中的应用效果,表明时变静校正在解决转换波静校正时具有一定的技术优势,提高了地震资料的信噪比,取得了较好的效果.     

基于三维角道集的多分量层析速度反演方法在胜利地区的应用

目前的基于高斯束角道集的层析速度反演方法都是基于二维的角道集,观测系统中炮点线和检波点线在同一条直线上,不能适用于炮点线和检波点线不在同一条直线上的三维观测系统.本文在分析二维高斯束角道集的实现方法的基础上,提出了一种利用二维角道集的算法实现三维角道集生成的方法,提出了一种基于三维高斯束角道集的层析速度反演方法,并通过模型和实际资料的试算验证了本文提出的基于三维高斯束角道集的层析速度反演方法的有效性和正确性.     

频谱代换无拉伸动校正方法研究

动校正拉伸是地震资料处理的一个基本问题,解决拉伸问题的处理方法是切除.现代地震数据大多为长排列采集,动校正拉伸更为严重.依据褶积模型和Fourier变换的基本性质,本文给出频谱代换无拉伸动校正方法.算法实现就是将CMP道集变换到频率域,取参考道的相位谱替换其它偏移距道的相位,同时保持其振幅谱不变,再做Fourier反变换就得到动校正后的地震剖面.通过其实现过程可知该方法不需要地下介质的速度信息,算法可完全自动实现,且具有较高的计算效率.频谱代换无拉伸动校正可适用于任何偏移距的地震资料,而且还可有效保持地震资料的AVO效应.理论模拟数据及其叠加结果显示频谱代换法的有效性和实用性,同时该方法具有较强的抗随机噪音能力.     

输出道成像方式的共反射面元叠加方法I——理论

共反射面元（Common Reflection Surface）叠加是一种独立于宏观速度模型的零偏移距成像方法，该方法属于典型的克希霍夫型成像方法. 根据成像方式的不同，克希霍夫型成像方法可以分为两大类：输出道成像方式和输入道成像方式. 考察共反射面元叠加方法，它属于输入道成像方式. 本文基于理论模型数据，实现了输出道成缘方式的CRS叠加方法. 相比传统的输入道成像方式，它具有能够保证大偏移距反射信息的成像精度和计算效率较高的优点，而且更加容易推广到三维情形.     

基于倾角-偏移距域道集的绕射波成像

地震绕射波是地下非连续性地质体的地震响应,绕射波成像对地下断层、尖灭和小尺度绕射体的识别具有重要的意义.在倾角域共成像点道集中,反射波同相轴表现为一条下凸曲线,能量主要集中在菲涅耳带内,绕射波能量则比较发散.由于倾角域菲涅耳带随偏移距变化而存在差异,因此本文提出一种在倾角-偏移距域道集中精确估计菲涅耳带的方法,在各偏移距的倾角域共成像点道集中实现菲涅耳带的精确切除,从而压制反射波.在倾角-偏移距域道集中还可以分别实现绕射波增强,绕射波同相轴相位校正,因此能量弱的绕射波可以清晰地成像.在倾角域共成像点道集中,反射波同相轴的最低点对应于菲涅耳带估计所用的倾角,因此本文提出一种在倾角域共成像点道集中直接自动拾取倾角场的方法.理论与实际资料试算验证了本文绕射波成像方法的有效性.     

基于多道卷积信号盲分离的多次波自适应相减方法

本文将多次波自适应相减问题表示为一个多道卷积信号的盲分离问题.利用2D卷积核来表示预测多次波和实际多次波之间的差异,并采用分离出的一次波信号的非高斯性最大化作为优化目标,我们提出一种基于多道卷积信号盲分离的多次波自适应相减算法.为了求解上述非线性优化问题,所提方法将其转化为一个迭代线性优化问题,采用迭代最小二乘方法加以实现.由于采用了多道卷积信号盲分离模型,所提方法能够适应预测和真实多次波之间在时间及空间上的变化.通过对简单模型数据、Pluto数据和实际数据进行处理,验证了所提算法的有效性.     

多尺度奇异性检测及其在地震相分析中的应用

本文从地震道的奇异属性出发,利用连续小波变换求取地震道的小波变换模极大值曲线,并沿此曲线提取地震道的小波变换系数,称之为小波变换模极大值连线振幅.此属性不仅可以代表信号本身,而且可以最大程度的区别于相邻道,并且具有地震道多尺度的特征,即兼具时频域的特征.由此结合自组织神经网络,我们提出了一种新的地震相分析方法.通过模型合成地震记录实验分析,证明此方法是可行的,且对地质层位的解释误差具有一定的容许度.最后,将此方法应用于了实际资料,取得了良好的效果.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

Modelling evapotranspiration using discrete wavelet transform and neural networks

This study combines wavelet transforms and feed‐forward neural network methods for reference evapotranspiration estimation. The climatic data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. For wavelet and neural network (WNN) model, the input data was decomposed into wavelet sub‐time series by wavelet transformation. Later, the new series (reconstructed series) are produced by adding the available wavelet components and these reconstructed series are used as the input of the WNN model. This phase is pre‐processing of raw data and the main different of the WNN model. The performance of the WNN model was compared with classical neural networks approach [artificial neural network (ANN)], multi‐linear regression and Hargreaves empirical method. This study shows that the wavelet transforms and neural network methods could be applied successfully for evapotranspiration modelling from climatic data. Copyright © 2009 John Wiley & Sons, Ltd.     

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.     

Short-term prediction of influent flow in wastewater treatment plant

Predicting influent flow is important in the management of a wastewater treatment plant (WWTP). Because influent flow includes municipal sewage and rainfall runoff, it exhibits nonlinear spatial and temporal behavior and therefore makes it difficult to model. In this paper, a neural network approach is used to predict influent flow in the WWTP. The model inputs include historical influent data collected at a local WWTP, rainfall data and radar reflectivity data collected by the local weather station. A static multi-layer perceptron neural network performs well for the current time prediction but a time lag occurs and increases with the time horizon. A dynamic neural network with an online corrector is proposed to solve the time lag problem and increase the prediction accuracy for longer time horizons. The computational results show that the proposed neural network accurately predicts the influent flow for time horizons up to 300 min.     

A wavelet based approach for combining the outputs of different rainfall–runoff models

The rainfall–runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall–runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall–runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall–runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance.     

Multiscale island injection genetic algorithms for groundwater remediation

Genetic algorithms have been shown to be powerful tools for solving a wide variety of water resources optimization problems. Applying these approaches to complex, large-scale water resources applications can be difficult due to computational limitations, especially when a numerical model is needed to evaluate different solutions. This problem is particularly acute for solving field-scale groundwater remediation design problems, where fine spatial grids are often needed for accuracy. Finer grids usually improve the accuracy of the solutions, but they are also computationally expensive. In this paper we present multiscale island injection genetic algorithms (IIGAs), in which the optimization algorithms have different multiscale populations working on different islands (groups of processors) and periodically exchanging information. This new approach is tested using a field-scale pump-and-treat design problem at the Umatilla Army Depot in Oregon, USA. The performance of several variations of this approach is compared with the results of a simple genetic algorithm. The new approach found the same solution as much as 81% faster than the simple genetic algorithm and 9–53% faster than other previously formulated multiscale strategies. These findings indicate substantial promise for multiscale IIGA approaches to improve solution of complex water resources applications at the field scale.     

A comparison of aeromagnetic levelling techniques with an introduction to median levelling

Aeromagnetic data collected in areas with severe diurnal magnetic variations (auroral zones) are difficult to level. This paper describes levelling of an aeromagnetic survey where such conditions prevail, and where sophisticated levelling techniques are needed. Corrections based on piecewise low‐order polynomial functions are often used to minimize mis‐ties in aeromagnetic data. We review this technique and describe similar mis‐tie fitting methods based on low‐pass filter levelling, tensioned B‐spline levelling and median levelling. It is demonstrated that polynomial levelling, low‐pass filter levelling and tensioned B‐spline levelling depend on the careful editing of outlying mis‐ties to avoid the introduction of false anomalies. These three techniques are equally efficient at removing level errors. Median levelling also removes level errors efficiently, but it is more robust in the sense that mis‐tie editing is not required. This is due to the inherent noise‐removal capabilities of the median filter. After mis‐tie editing, the total field anomalies of the other three techniques closely resemble the unedited median‐levelled total field anomaly.     

A hybrid neural network model for typhoon-rainfall forecasting

A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach.     

Salt diapirs without well defined boundaries – a feasibility study of semi‐automatic detection

Prestack depth imaging of seismic data in complex areas such as salt structures requires extensive velocity model updating. In many cases, salt boundaries can be difficult to identify due to lack of seismic reflectivity. Traditional amplitude based segmentation methods do not properly tackle this problem, resulting in extensive manual editing. This paper presents a selection of seismic attributes that can reveal texture differences between the salt diapirs and the surrounding geology as opposed to amplitude‐sensitive attributes that are used in case of well defined boundaries. The approach consists of first extracting selected texture attributes, then using these attributes to train a classifier to estimate the probability that each pixel in the data set belongs to one of the following classes: near‐horizontal layering, highly‐dipping areas and the inside of the salt that appears more like a low amplitude area with small variations in texture. To find the border between the inside of the salt and the highly‐dipping surroundings, the posterior probability of the class salt is input to a graph‐cut algorithm that produces a smooth, continuous border. An in‐line seismic section and a timeslice from a 3D North Sea data set were employed to test the proposed approach. Comparisons between the automatically segmented salt contours and the corresponding contours as provided by an experienced interpreter showed a high degree of similarity.     

Comparison of LLR,MLP, Elman,NNARX and ANFIS Models—with a case study in solar radiation estimation

Despite the widespread application of nonlinear mathematical models, comparative studies of different models are still a huge task for modellers. This is because a large number of trial and error processes are needed to develop each model, so the workload will be multiplied into an unmanageable level if many types of models are involved. This study presents an efficient approach by using the Gamma test (GT) to select the input variables and the training data length, so that the trial and error workload can be greatly reduced. The methodology is tested in estimating solar radiation at the Brue catchment, UK. Several nonlinear models have been developed efficiently with the aid of the GT, including local linear regression, multi-layer perceptron (MLP), Elman neural network, neural network auto-regressive model with exogenous inputs (NNARX) and adaptive neuro-fuzzy inference system (ANFIS). This work is only feasible within the time and resources constraint, due to the GT in reducing huge workload of the trial and error process.