自适应Kalman滤波反褶积的快速实现方法

提出了以二进小波变换为基础的自适应Kalman滤波反褶积(AKFD)新方法,针对该方法的计算复杂程度,提出了一种快速实现方法.二进小波变换的AKFD抛弃了传统预测反褶积对信号平稳性的假设,克服了提高分辨率而信噪比明显降低的问题,具有很好的抗噪性能.在小波域进行的AKFD在压制假反射以及提高分辨率方面比时间域的AKFD好,克服了在时域内进行AKFD抬升低频成分的缺陷.利用二维地震数据的局部平稳性的假设提出了快速实现方法,通过分段求取自适应预测算子,分别于横向及纵向采用样条插值的方法进行插值,来减少求取自适应预测算子的计算量,达到快速实现的目的.经过大量实验表明计算速度提高数百倍,仍能保持原来的计算效果.     

自适应Kalman滤波反褶积的快速实现方法

提出了以二进小波变换为基础的自适应Kalman滤波反褶积(AKFD)新方法，针对该方法的计算复杂程度，提出了一种快速实现方法.二进小波变换的AKFD抛弃了传统预测反褶积对信号平稳性的假设，克服了提高分辨率而信噪比明显降低的问题，具有很好的抗噪性能.在小波域进行的AKFD在压制假反射以及提高分辨率方面比时间域的AKFD好，克服了在时域内进行AKFD抬升低频成分的缺陷.利用二维地震数据的局部平稳性的假设提出了快速实现方法，通过分段求取自适应预测算子，分别于横向及纵向采用样条插值的方法进行插值，来减少求取自适应预测算子的计算量，达到快速实现的目的.经过大量实验表明计算速度提高数百倍，仍能保持原来的计算效果.     

提高台站地震资料信噪比的自适应极化滤波

基于标准协方差极化滤波方法（SCM）,由于其物理意义明确、易于实现、计算效率较高,在多分量地震处理中发挥着重要的作用。但该方法的时窗长度选择完全依赖于经验判断,不可避免地会出现解释上的人为影响。基于此,对协方差矩阵的分析时窗进行改进,时窗的长度自适应于三分量地震记录的瞬时频率,实现了自适应协方差极化滤波方法（ACM）。模型数据及实际三分量台站地震数据处理结果表明,ACM对局部变化比较剧烈的信号更加敏感,极大提高了滤波精度。     

台站地震资料的时频域自适应极化分析和滤波

提出一种自适应协方差的时频域极化滤波方法。该方法在广义S变换时频方法的基础上,构造时频域自适应协方差矩阵,通过特征分析计算时频域瞬时极化参数,设计极化滤波器,实现多分量地震极化分析和滤波。其优势在于协方差矩阵的分析时窗的长度由多分量地震数据的瞬时频率确定,可以自适应于有效信号的周期,在每个时频点计算极化参数不需要进行插值处理;结合时间频率信息,解决在时间域或频率域波形或频率重叠的信号具有明显的直观性。模型数据及实际三分量台站地震数据处理结果表明,该极化滤波方法在台站地震资料分析和处理方面具有很好的直观性和较高的分辨率。     

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.     

层状结构地震波形反演方法的研究和应用

从反散射理论出发,利用微扰法建立关于层状结构模型波形反演的滤波反投影公式,并与非线性最小二乘法同时进行数值模拟.研究了波形反演计算的特点.应用——和法提供可靠的初值模型.波动与走时反演的联合应用减少了反演解的不确定性.最后,通过对实际资料的实验计算,描述了波形反演计算的实施过程.      

Time-domain identification of dynamic properties of layered soil by using extended Kalman filter and recorded seismic data

A novel time-domain identification technique is developed for the seismic response analysis of soil-structure interaction. A two-degree-of-freedom (2DOF) model with eight lumped parameters is adopted to model the frequency-dependent behavior of soils. For layered soil, the equivalent eight parameters of the 2DOF model arc identified by the extended Kalman filter (EKF) method using recorded seismic data. The polynomial approximations for derivation of state estimators are applied in the EKF procedure. A realistic identification example is given for the layered-soil of a building site in Anchorage, Alaska in the United States. Results of the example demonstrate the feasibility and practicality of the proposed identification technique. The 2DOF soil model and the identification technique can be used for nonlinear response analysis of soil-structure interaction in the time-domain for layered of complex soil conditions. The identified parameters can be stored in a database for use in other similar soil conditions. If a universal database that covers information related to most soil conditions is developed in the future, engineers could conveniently perform time history analyses of soil-structural interaction.     

Investigation of flow and transport processes at the MADE site using ensemble Kalman filter

In this work the ensemble Kalman filter (EnKF) is applied to investigate the flow and transport processes at the macro-dispersion experiment (MADE) site in Columbus, MS. The EnKF is a sequential data assimilation approach that adjusts the unknown model parameter values based on the observed data with time. The classic advection–dispersion (AD) and the dual-domain mass transfer (DDMT) models are employed to analyze the tritium plume during the second MADE tracer experiment. The hydraulic conductivity (K), longitudinal dispersivity in the AD model, and mass transfer rate coefficient and mobile porosity ratio in the DDMT model, are estimated in this investigation. Because of its sequential feature, the EnKF allows for the temporal scaling of transport parameters during the tritium concentration analysis. Inverse simulation results indicate that for the AD model to reproduce the extensive spatial spreading of the tritium observed in the field, the K in the downgradient area needs to be increased significantly. The estimated K in the AD model becomes an order of magnitude higher than the in situ flowmeter measurements over a large portion of media. On the other hand, the DDMT model gives an estimation of K that is much more comparable with the flowmeter values. In addition, the simulated concentrations by the DDMT model show a better agreement with the observed values. The root mean square (RMS) between the observed and simulated tritium plumes is 0.77 for the AD model and 0.45 for the DDMT model at 328 days. Unlike the AD model, which gives inconsistent K estimates at different times, the DDMT model is able to invert the K values that consistently reproduce the observed tritium concentrations through all times.     

Estimation of ecologically significant circulation features of the Columbia River estuary and plume using a reduced-dimension Kalman filter

A data assimilation method was used to estimate the variability of three ecologically significant features of the Columbia River estuary and plume: the size of the plume, the orientation of the plume, and the length of the salinity intrusion in the estuary. Our data assimilation method was based on a reduced-dimension Kalman filter that enables fast data assimilation of nonlinear dynamics in the estuary and plume. The assimilated data included measurements of salinity, temperature, and water levels at 13 stations in the estuary and at five moorings in the plume.     

基于多次波和自适应预测误差滤波器的地震近炮检距数据重建方法

地震数据采集过程中,受野外施工条件的制约,往往很难获得完整的地震波场,尤其近炮检距数据的缺失尤为严重.当前,很多地震数据处理方法的应用都依赖于近炮检距数据,如何对近炮检距的缺失数据进行重建,是一个重要的研究课题.本文通过多次波和一次波的互相关构建虚拟一次波,利用数据本身的波场信息,对缺失的近炮检距数据进行插值重建.由于通过多次波构建的一次波与真实的一次波存在振幅和相位方面的差异,提出通过基于非平稳自回归过程的自适应预测误差滤波器来表征虚拟一次波的能量谱,利用最小二乘反演方法重建近炮检距缺失数据,自适应预测误差滤波器通过求解正则化约束下的数学欠定问题来实现局部自适应特征.通过对Sigsbee2B模型和实际数据的测试结果表明新方法可以合理地重建复杂的近炮检距缺失数据.     

Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation

With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect.     

CryoSat-2测高数据与Landsat 8光学数据融合提取南极Amery冰架接地线

本文利用CryoSat-2测高数据与Landsat 8光学数据,针对南极Amery冰架区域开展接地线提取研究.首先通过Landsat 8光学影像三次Hermite多项式插值处理,在坡度分析的基础上利用表面曲率改进方法获取接地线特征点;同时对CryoSat-2测高数据进行坡度分析和表面曲率计算,通过沿轨梯度分析方法提取接地点;最后将Landsat 8与CryoSat-2数据获取的接地点进行最小二乘融合得到融合接地线.实验结果表明,融合结果利用高空间分辨率光学数据不仅能保证接地线提取精度,同时测高数据还能够弥补光学数据受云遮挡导致的数据空缺,保持接地线的完整性.与MOA产品比较可以看出,融合数据点与MOA接地线平均距离为367 m,标准差为601 m,所有数据点中距离小于1 km的点占总数的93.19%,与MOA产品具有较好一致.本文提出的融合算法可以实现空间连续的接地线提取结果,对后续研究南极物质平衡、冰流速计算等具有重要的意义.

     

CryoSat-2测高数据与Landsat 8光学数据融合提取南极Amery冰架接地线

本文利用CryoSat-2测高数据与Landsat 8光学数据,针对南极Amery冰架区域开展接地线提取研究.首先通过Landsat 8光学影像三次Hermite多项式插值处理,在坡度分析的基础上利用表面曲率改进方法获取接地线特征点;同时对CryoSat-2测高数据进行坡度分析和表面曲率计算,通过沿轨梯度分析方法提取接地点;最后将Landsat 8与CryoSat-2数据获取的接地点进行最小二乘融合得到融合接地线.实验结果表明,融合结果利用高空间分辨率光学数据不仅能保证接地线提取精度,同时测高数据还能够弥补光学数据受云遮挡导致的数据空缺,保持接地线的完整性.与MOA产品比较可以看出,融合数据点与MOA接地线平均距离为367 m,标准差为601 m,所有数据点中距离小于1 km的点占总数的93.19%,与MOA产品具有较好一致.本文提出的融合算法可以实现空间连续的接地线提取结果,对后续研究南极物质平衡、冰流速计算等具有重要的意义.     

一种新的基于卡尔曼滤波的地震记录同相轴跟踪方法及性能分析

在地震勘探领域中,卡尔曼滤波常用于地震信号的反褶积以提高地震勘探资料的分辨率和信噪比. 不同于此,本文建立一个新的卡尔曼滤波系统模型并利用卡尔曼滤波跟踪地震记录同相轴. 同相轴信息在对地下介质性质、界面的深度、界面的产状以及油气定性判别等方面具有极其重要的作用. 目前多数拾取地震同相轴的方法与地震波的运动规律结合较少.本文依据地震反射波运动规律构建了用于跟踪地震反射同相轴的卡尔曼滤波系统的状态方程和量测方程,并将炮检距、地震子波到达时和层速度等重要物理量融入所建方程,给出滤波模型和初始化方法,分析不同因素对该系统滤波性能的影响. 仿真实验表明,所提出的跟踪滤波系统能较好地拾取地震反射同相轴信息,为拾取地震同相轴提供了一条新途径.     

A multi‐temporal analysis of AMSR‐E data for flood and discharge monitoring during the 2008 flood in Iowa

The objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer 2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi‐temporal analysis of 37 GHz satellite imagery from the Advanced Microwave Scanning Radiometer (AMSR‐E) to calculate and detect anomalies in soil moisture and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of historical satellite records was adopted. A rating curve has been developed to assess the relationship between PRVI values and discharge observations downstream. A time‐lag term has been introduced and adjusted to account for the changing delay between PRVI and streamflow. Moreover, the Kalman filter has been used to update the rating curve parameters in near real time. The temporal variability of the b exponent in the rating curve formula shows that it converges toward a constant value. A consistent 21‐day time lag, very close to an estimate of the time of concentration, was obtained. The agreement between observed discharge downstream and estimated discharge with and without parameters adjustment was 65 and 95%, respectively. This demonstrates the interesting role that passive microwave can play in monitoring flooding and wetness conditions and estimating key hydrologic parameters. Copyright © 2011 John Wiley & Sons, Ltd.