Hydrodynamic dispersion during absorption of water by soil: 2. Immobile water model

The assumptions and analysis of Part 1 are extended to the three model soil-water profiles having different immobile-water contents up to 0.08 m³ m^?3. Concentration profiles in the mobile zone obtained with a constant dispersion coefficient are compared with those calculated with the same dispersion coefficient and no immobile water. The average concentration profiles were calculated from the concentration of the solution in both the mobile and immobile phases. Increasing the immobile-water content decreased and shifted the average concentration profiles further into the soil. The values of the dispersion coefficient calculated with the immobile-water fraction ignored, were found to be water content dependent and many times greater than that of the input dispersion coefficient.     

Pressure Energy Diffusion Rates in the Wall-Wake Region of Immobile Solid Sphere in Open Channel Flows-Numerical Simulation and Experimental Study

Water Resources - Present study elucidates the near-bed pressure energy diffusion rates in the wall-wake region of flowpast a stationary solid sphere in open channel. Vertical distributions of...     

数据自相关多次波偏移成像

在常规偏移方法中一般都需要压制地震数据中的多次波,仅利用一次波信息成像,把自由表面反射的多次波视为噪声,但是在多次波中也包含着地下结构信息,应该将其充分利用到成像中来.事实上,已经有不少成像方法试图利用多次波信息,但是大部分方法都需要对多次波进行预测.本文提出了基于傅里叶有限差分偏移算子的数据自相关偏移方法.在这种偏移方法中,对含有一次波和多次波的地震数据,分别进行下行和上行延拓,然后直接利用常规的互相关成像条件成像.由于波场延拓采用了傅里叶有限差分算子,其计算效率高,能够很好地对复杂介质中的地震数据进行延拓.在数值试验中,使用了一个含散射点的三层模型和Marmousi模型.合成数据测试结果表明,这种方法可以对更大范围的地下构造成像,比常规的只利用一次波的傅里叶有限差分法照明度更好,并且在浅层可以提供更高的分辨率.我们提出的数据自相关策略易于实现且避免了繁杂的多次波预测,这对于复杂地下构造成像可能有着重大意义.     

Magnetotelluric data inversion with seismic data constraint

Introduction The research on the structure and physical property of ancient hidden hill, igneous rocks and basement is relatively difficult by using seismic data only. If we combine seismic data, magneto-telluric (MT) data and geophysical data together, better results can be obtained for the above problem. A number of geophysicists at home and abroad, such as CHEN and WANG (1990), Siri-punvarapor and Egbert (2000) have tried many methods to solve the problem by the inversion of seismic da…     

一种数据归档机制的实现

介绍了国际数据中心自动数据服务的方式,研究了国家数据中心数据处理过程中请求和获取国际数据中心非连续数据的方法,在此基础上设计了向国际数据中心获取数据的邮件数据请求代理程序,该程序可通过解析邮件,存储邮件数据请求者与请求数据的逻辑关系,实现了邮件数据的请求代理,在国家数据中心每日数据请求总量有限制的条件下,实现了数据的共享,满足对国内授权用户的需求,经过实际运行考验,证明程序稳定可靠,达到了设计目标。     

Super-resolution data assimilation

Ocean Dynamics - Increasing model resolution can improve the performance of a data assimilation system because it reduces model error, the system can more optimally use high-resolution...     

基于大数据技术的地震数据处理新思路

现代地震观测积累了大量、种类繁多的地震前兆数据和震后数据,但数据中隐含的地震规律及趋势无法用经典的算例、数学公式、物理公式进行定量解释、分析和预测,需要探索新方法、新技术。案例表明,大数据分析具有预测事物发展趋势、改变传统观念和发现新事物的功能,有助于从错误信息中挖掘有价值信息。本文由此提出基于大数据技术处理地震数据的新思路,对地震监测预报新思路进行探索;通过在大数据平台上基于地震目录的余震预测研究,给出大数据技术在地震数据处理方面的一个应用实例,验证该思路的可行性。     

Nonlinear data processing method for the signal enhancement of GPR data

An alternative data processing procedure is proposed in this paper for the purpose of enhancing the signal/noise (S/N) ratio of ground penetrating radar (GPR) data. The processing methodology is achieved by performing the logarithmic transform in conjunction with the ensemble empirical mode decomposition (EEMD), a new nonlinear data analysis method in signal processing. The synthetic model study and field example indicate that the logarithmic transform is effective in alleviating the attenuation problem. Additionally, the spectrogram obtained from Hilbert-Huang transform (HHT) shows that the decomposition sensitivity of the EEMD method is greatly improved with the aid of the logarithmic transform. This new method allows us to extract the signal components from noisy GPR data efficiently. The success of this study suggests a possible nonlinear analysis application in future GPR investigation, particularly in the filter design and gain correction.     

An automatic method for data processing of seismic data in tunneling

We present a new method for the prediction of the discontinuities and lithological variations ahead of the tunnel face. The automatic procedure is applied to data collected by seismic reflection surveys, with the sources and sensors located along the tunnel. The method allows: i) to estimate an average value of the wave velocity; ii) to detect the discontinuities for each source point; and iii) to analyze and plot the number of superposing estimates for each node of the domain. The final result can be interpreted as the probability to detect a discontinuity at a certain distance from the tunnel face. The method automatically estimates the peaks in the seismograms that can be related to a reflection. On the base of this process, the method only requires the source–receiver geometry and the data acquisition parameters. The procedure has been tested on synthetic and real data coming from a seismic survey on a tunnel under construction. The results indicate that the method runs very fast and it is reliable in the identification of lithological changes and discontinuities, up to more than 100 m ahead of the tunnel face.     

从瞬变电磁测深数据到平面电磁波场数据的等效转换

中心回线源装置的瞬变电磁场是一种涡流场,在地下主要以扩散形式传播.平面电磁波在地下介质中传播时具有反射、折射等电磁学特性,为了借鉴平面电磁波场测深成熟的解释方法,文中开展了从瞬变电磁涡流场测深数据到平面波场数据的快速等效转换的研究.通过大量的理论模型的正演计算、曲线对比,误差统计,对瞬变电磁场、平面波场在地下传播机制特性的分析,建立了一种由瞬变电磁测深视电阻率数据向平面波场测深视电阻率数据转换的时间-频率等效对应关系.利用这一关系式,把扩散场数据快速转换成等效平面波场测深数据. 为进一步进行瞬变电磁拟平面波成像解释打下理论基础.     

连续缺失地震数据的高阶流式预测滤波插值方法

由于观测系统实施以及经济因素的限制,采集到的勘探地震数据在空间方向上总是不规则分布的,并且往往会出现数据的大范围连续缺失情况.许多后续地震数据处理方法（例如：多次波压制和波动方程偏移等）都需要空间上规则分布的数据.插值技术是一种解决地震数据缺失问题的有效手段,但是传统的数据插值方法在进行连续缺失数据重建时往往会出现失效的情况,尤其在处理非平稳地震同相轴时精度不高,并且大多数的方法需要迭代计算,在处理高维大规模数据时效率较低.针对连续缺失地震数据的快速插值问题,本文提出了一种非迭代的时空域高阶流式预测滤波插值方法,通过使用高阶限制条件来提高连续缺失数据的滤波器估计精度,提高局部约束条件的稳定性,改善低阶流式计算由于滤波器系数无法连续更新所造成的插值失效情况.同时,空间非因果滤波器和蛇形插值路径的设计方案可以有效减小大范围连续缺失数据和数据边界对于预测滤波器的计算误差,本方法能够有效处理包括近炮检距缺失情况在内的连续缺失数据插值重建.通过与工业标准傅里叶凸集投影（POCS）方法进行比较,理论模型和实际数据处理结果表明,本文提出的高阶流式预测滤波插值方法对高维连续缺失地震数据有较好的重建效果,在插值精度和计算效率两个方面有更好的平衡性.

     

A platform for Kirchhoff data mapping in scalar models of data acquisition

Kirchhoff data mapping (KDM) is a procedure for transforming data from a given input source/receiver configuration and background earth model to data corresponding to a different output source/receiver configuration and background model. The generalization of NMO/DMO, datuming and offset continuation are three examples of KDM applications. This paper describes a 'platform' for KDM for scalar wavefields. The word, platform, indicates that no calculations are carried out in this paper that would adapt the derived formula to any one of a list of KDMs that are presented in the text. Platform formulae are presented in 3D and in 2.5D. For the latter, the validity of the platform equation is verified — within the constraints of high-frequency asymptotics — by applying it to a Kirchhoff approximate representation of the upward scattered data from a single reflector and for an arbitrary source/receiver configuration. The KDM formalism is shown to map this Kirchhoff model data in the input source/receiver configuration to Kirchhoff data in the output source/receiver configuration, with one exception. The method does not map the reflection coefficient. Thus, we verify that, asymptotically, the ray theoretical geometrical spreading effects due to propagation and reflection (including reflector curvature) are mapped by this formalism, consistent with the input and output modelling parameters, while the input reflection coefficient is preserved. In this sense, this is a 'true-amplitude' formalism. As with earlier Kirchhoff inversion, a slight modification of the kernel of KDM provides alternative integral operators for estimating the specular reflection angle, both in the input configuration and in the output configuration, thereby providing a basis for amplitude-versus-angle analysis of the data.     

Terracing potential field data

Terracing is an operator that is applied to potential field data to produce regions of constant field amplitude that are separated by sharp boundaries, as an aid to interpretation. When applied to map data, the boundaries are defined by the zero contour of the 2D Laplacian derivative operator. An improved method is described here that defines the boundaries by the zero contour of the profile curvature. This approach gives superior results because the 2D Laplacian operator is composed only of derivatives in the EW and NS directions, while the profile curvature uses the curvature in the 'uphill' direction at each point, whatever that direction may be. The method is demonstrated on gravity data from South Africa. Source code in Matlab format is available from the authors on request.     

Processing of magnetotelluric data

The processing of magnetotelluric data involves concepts from electromagnetic theory, time series analysis and linear systems theory for reducing natural electric and magnetic field variations recorded at the earth's surface to forms suitable for studying the electrical properties of the earth's interior.The electromagnetic field relations lead to either a scalar transfer impedance which couples an electric component to an orthogonal magnetic component at the surface of a plane-layered earth, or a tensor transfer impedance which couples each electric component to both magnetic components in the vicinity of a lateral inhomogeneity.A number of time series spectral analysis methods can be used for estimating the complex spectral coefficients of the various field quantities. These in turn are used for estimating the nature of the transfer function or tensor impedance. For two dimensional situations, the tensor impedance can be rotated to determine the principal directions of the electrical structure.In general for real data, estimates of the apparent resistivity are more stable when calculated from the tensor elements rather than from simple orthogonal field ratios (Cagniard estimates), even when the fields are measured in the principal coordinates.     

Functional data clustering using K-means and random projection with applications to climatological data

In this paper, an efficient pattern recognition method for functional data is introduced. The proposed method works based on reproducing kernel Hilbert space (RKHS), random projection and K-means algorithm. First, the infinite dimensional data are projected onto RKHS, then they are projected iteratively onto some spaces with increasing dimension via random projection. K-means algorithm is applied to the projected data, and its solution is used to start K-means on the projected data in the next spaces. We implement the proposed algorithm on some simulated and climatological datasets and compare the obtained results with those achieved by K-means clustering using a single random projection and classical K-means. The proposed algorithm presents better results based on mean square distance (MSD) and Rand index as we have expected. Furthermore, a new kernel based on a wavelet function is used that gives a suitable reconstruction of curves, and the results are satisfactory.