粒子滤波非线性AVO反演方法

提出一种新的AVO非线性反演方法,即利用粒子滤波器来求解AVO非线性贝叶斯反演问题,利用随机带权样本逼近后验概率.论文首先论述了粒子滤波器的基本原理,包括状态转移模型与观测模型,权值预测与更新,重要性密度采样等粒子滤波器应用中的关键技术.然后建立了适合于AVO反演的粒子滤波器状态转移模型和观测模型.最后,利用该方法分别对模型数据和实际资料进行了反演计算.反演结果表明,该方法具有较好的稳定性,在AVO反演中具有的一定的应用潜力.同时对地球物理反演的其他问题求解也提供了一条新的途径.     

The SEEK filter method for data assimilation in oceanography: a synthesis

This paper reviews the developments of the singular evolutive extended Kalman (SEEK) filter method used for data assimilation in oceanography, since the original paper by Pham et al. (J Mar Syst 16:3–4, 323–340 1998a). First, a short review is presented of the context of data assimilation in oceanography and of the variety of numerical ocean codes and configurations in which the SEEK filter has been implemented using different data sets for assimilation. Then, the paper provides an exposition of the different versions of the SEEK filter developed during the past 10 years and discusses their relative merits for scientific or operational implementations. A classification of the algorithmic variants is proposed, and several possible improvements of the generic methodology are mentioned in the perspective of new assimilation challenges.     

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.     

Modeling of the river ecological status with macrophytes using artificial neural networks

Biomonitoring methods based on macrophytes have been used mandatorily in the assessment of freshwaters since the implementation of the Water Framework Directive (WFD). The Macrophyte Index for Rivers (MIR) was developed in Poland for the monitoring of running waters under the WFD requirements. This index shows the degree of river degradation under the influence of water pollutants, especially nutrients. The aim of the present study was to determine the relationship between the MIR and various hydrochemical parameters using artificial neural networks (ANNs). Physico-chemical parameters of water (monthly results for the whole year), which were derived from 147 lowland river survey sites, all located in Poland, were applied to model the MIR values. Water quality variables were determined over three timeframes: the annual average; the average for the vegetation period; and the average for the summer period. Quality of the networks was assessed using coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE). The best modeling quality was obtained for yearly average values of water quality parameters. The quality statistics were: R² = 0.722, NSE = 0.721 and RMSE = 0.056 (training dataset); R² = 0.555, NSE = 0.533 and RMSE = 0.101 (validation dataset); R² = 0.650. NSE = 0.600 and RMSE = 0.089 (testing dataset). This indicates that macrophytes reflect the whole year impact of pollution, whereas summer.     

Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

The evaluation of surface water resources is a necessary input to solving water management problems. Neural network models have been trained to predict monthly runoff for the Tirso basin, located in Sardinia (Italy) at the S. Chiara section. Monthly time series data were available for 69 years and are characterized by non-stationarity and seasonal irregularity, which is typical of a Mediterranean weather regime. This paper investigates the effects of data preprocessing on model performance using continuous and discrete wavelet transforms and data partitioning. The results showed that networks trained with pre-processed data performed better than networks trained on undecomposed, noisy raw signals. In particular, the best results were obtained using the data partitioning technique.     

基于卡尔曼滤波的自然电场数据时序反演

自然电场法常用于环境与工程等领域的监测作业,但各时刻观测数据往往单独反演解释.为了充分利用时序数据间的关联信息,提高监测数据的反演解释可靠性,提出基于卡尔曼滤波的自然电场监测数据时序反演方法.根据达西定律和阿尔奇公式建立污染物在孔隙介质中的运动扩散的动态地电模型,作为用于构建卡尔曼滤波的状态模型.而卡尔曼滤波的观测模型则通过常规的自然电场法正演获得.在建立状态模型和观测模型的基础上,构建起卡尔曼滤波递归,将地电模型演化信息与自然电场观测数据进行信息融合,实现自然电场监测数据的时序反演.加入噪声的自然电场模拟数据测试表明时序反演算法具有较好的鲁棒性,对噪声不敏感.沙槽物理实验监测数据的计算测试也同样证明时序反演能有效处理监测数据,实现对动态模型的准确重构.

     

Application of a data assimilation method via an ensemble Kalman filter to reactive urea hydrolysis transport modeling

With growing importance of water resources in the world, remediations of anthropogenic contaminations due to reactive solute transport become even more important. A good understanding of reactive rate parameters such as kinetic parameters is the key to accurately predicting reactive solute transport processes and designing corresponding remediation schemes. For modeling reactive solute transport, it is very difficult to estimate chemical reaction rate parameters due to complex processes of chemical reactions and limited available data. To find a method to get the reactive rate parameters for the reactive urea hydrolysis transport modeling and obtain more accurate prediction for the chemical concentrations, we developed a data assimilation method based on an ensemble Kalman filter (EnKF) method to calibrate reactive rate parameters for modeling urea hydrolysis transport in a synthetic one-dimensional column at laboratory scale and to update modeling prediction. We applied a constrained EnKF method to pose constraints to the updated reactive rate parameters and the predicted solute concentrations based on their physical meanings after the data assimilation calibration. From the study results we concluded that we could efficiently improve the chemical reactive rate parameters with the data assimilation method via the EnKF, and at the same time we could improve solute concentration prediction. The more data we assimilated, the more accurate the reactive rate parameters and concentration prediction. The filter divergence problem was also solved in this study.     

Data assimilation methods for estimating a heterogeneous conductivity field by assimilating transient solute transport data via ensemble Kalman filter

An ensemble Kalman filter (EnKF) is developed to identify a hydraulic conductivity distribution in a heterogeneous medium by assimilating solute concentration measurements of solute transport in the field with a steady‐state flow. A synthetic case with the mixed Neumann/Dirichlet boundary conditions is designed to investigate the capacity of the data assimilation methods to identify a conductivity distribution. The developed method is demonstrated in 2‐D transient solute transport with two different initial instant solute injection areas. The influences of the observation error and model error on the updated results are considered in this study. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating solute concentration measurements. The larger area of the initial distribution and the more observed data obtained, the better the calculation results. When the standard deviation of the observation error varies from 1% to 30% of the solute concentration measurements, the simulated results by the data assimilation method do not change much, which indicates that assimilation results are not very sensitive to the standard deviation of the observation error in this study. When the inflation factor is more than 1.0 to enlarge the model error by increasing the forecast error covariance matrix, the updated results of the hydraulic conductivity by the data assimilation method are not good at all. Copyright © 2012 John Wiley & Sons, Ltd.     

利用多分辨率小波网络方法进行伪井反演

利用小波网络,对地震资料和小波多尺度分解属性资料,进行了虚井声波时差反演.讨论了利用地震信号的小波分解和网络学习、训练的理论方法.为了提高反演分辨率和精度，研究了信号分段相似小波时—频分析技术方法.在对对这些技术方法综合研究和算法的实现设计以后，对实际资料进行了反演计算，并对反演结果进行验证.取得了很好的效果.     

太湖流域典型滨湖河网水动力与水质时空异质性

基于中国太湖梅梁湾东部的无锡市滨湖区河网29个监测点在丰水期、平水期和枯水期的流速和水质监测数据,将河网分为梁溪河、曹王泾、骂蠡港、城市河网南区以及城市河网北区5个区域,对流速和典型水质指标的时空异质性进行分析,结合主成分分析和相关性分析,得到各区域水动力与水质现状及其成因.结果显示:梁溪河和曹王泾的水质条件和水动力条件较好,多数水质因子与流速表现出了强相关性;骂蠡港的水质和流速区域变化明显,表现弱相关性;城市河网北区和南区的流速较缓,河道污染负荷较大,流速与水质因子之间的相关性较低.通过在滨湖河网开展流速和水质的野外监测,分析流速对于河网水环境的实际效果,验证不同水质指标与流速之间的响应关系,为滨湖河网区水质保护和科学的水污染治理技术提供基础支撑.     

Mineral composition and particle size distribution of river sediment and loess in the middle and lower Yellow River

Hyperconcentrated flows often occur in the middle and lower Yellow River(MLYR)and its tributaries,within which the main sediment source originates from the Loess Plateau of China due to serious water erosion.Little is known about the properties of river sediment that is transported by hyperconcentrated flows,particularly with respect to the mineral composition and size distribution.Samples of sediment and loess were collected in the northern,middle,and southern Loess Plateau and the mainstream and tributaries of the MLYR.A total of 18 loess samples and 24 river sediment samples were analyzed to determine their sediment size distribution and mineral composition.The bottom loess samples reflected the original sedimentary features of the Loess Plateau,and the median particle size reduced,and the clay content increased from the north to the south of the study region.The surface loess has been weathered under the action of wind and rainfall,and the clay particle content in the surface loess samples was higher than that in the undisturbed bottom loess.Erosion of the surface soil due to rainfall and surface runoff means that fine particles(mostly clay)have been washed away.The median diameter of surface loess particles was a little larger than that of the bottom loess particles where water erosion dominates.The particle size became coarser with increasing distance from the estuary in the MLYR,which reflects depositional sorting in the river channel.Significant logarithmic relations were found between the median diameter of the sediment particles and the i)non-clay mineral content and ii)clay mineral content.Thus,clay and non-clay mineral compositions can be conveniently estimated from the particle size distribution.     

Improving hydrodynamic modeling of an estuary in a mixed tidal regime by grid refining and aligning

Water levels and flows in the Singapore coastal waters are driven by the complex interactions of the Indian and Pacific Ocean tides, seasonal monsoon-driven contributions and also forced by local winds. The Singapore Regional Model was developed to simulate hydrodynamics in the Strait of Singapore which produces representative sea level variation in this region. However, resolution and alignment of the grid system of the model with respect to depth contours in some of its subregions, i.e., the Johor Estuary area require further improvement. For this, the grid system of the model was modified and compared the simulated results with field measurements. The computed flow velocities agreed better with field observations when the grid resolution was increased. However, improving the alignment of the grid with the channel boundary (with a much lower increase in grid resolution) provided a substantially larger improvement of the model performance. The grid modification greatly influenced the computed salinity in the estuary, while water levels are slightly affected. Further analysis of model results showed a pronounced ebb tidal asymmetry generated by the O1–K1–M2 tidal constituents in the estuary.     

Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter

Land data assimilation (DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations. Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus, improve the accuracy of state variables. It can also reduce uncertainties in the model by estimating some model parameters simultaneously. Among the various DA methods, the particle filter is free from the constraints of linear models and Gaussian error distributions, and can be applicable to any nonlinear and non-Gaussian state-space model; therefore, its importance in land data assimilation research has increased. In this study, a DA scheme was developed based on the residual resampling particle filter. Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously. Finally, to verify the scheme, a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona. The results show that the scheme can improve the accuracy of soil moisture estimations significantly. In addition, the three hydraulic parameters were also well estimated, demonstrating the effectiveness of the DA scheme.     

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.     

Detecting and attributing drought-induced changes in catchment hydrological behaviours in a southeastern Australia catchment using a data assimilation method

It is widely recognized that multi-year drought can induce changes in catchment hydrological behaviours. However, at present, our understanding about multi-year drought-induced changes in catchment hydrological behaviours and its driving factors at the process level is still very limited. This study proposed a new approach using a data assimilation technique with a process-based hydrological model to detect multi-year drought-induced changes in catchment hydrological behaviours and to identify driving factors for the changes in an unimpaired Australian catchment (Wee Jasper) which experienced prolonged drought from 1997 to 2009. Modelling experiments demonstrated that the multi-year drought caused a significant change in the catchment rainfall-runoff relationship, indicated by significant step changes in the estimated time-variant hydrological parameters SC (indicating catchment active water storage capacity) and C (reflecting catchment evapotranspiration dynamics), whose average values increased 23.4% and 10.2%, respectively, due to drought. The change in the rainfall-runoff relationship identified by the data assimilation method is consistent with that arrived at by a statistical examination. The proposed method provides insights about the drivers of the changes in the rainfall-runoff relationship at the processes level. Increasing catchment water storage capacity and decreasing ratio of rainfall to soil moisture for supplying actual evapotranspiration during drought are the main driving factors for the catchment behaviours change in the Wee Jasper catchment in terms of model structure. And they are related to decrease in catchment groundwater level and deep soil moisture. The proposed new method can be used as an effective technique for detecting both the change of hydrological behaviours induced by prolonged drought and its driving factors at the process level.     

Google Earth as a data source for investigating river forms and processes: Discriminating river types using form-based process indicators

Google Earth provides potential for exploiting an enormous reservoir of freely-available remotely sensed data to support river science and management. In this paper, we consider how the platform can support investigation of river physical forms and processes by developing an empirically-based reach-scale classification of semi-natural European single thread to transitional rivers. Using strict reach and image selection criteria, we identified 194 reaches of 68 rivers for analysis. Measurements of channel dimensions and counts of in-channel and floodplain features, standardised for reach length and channel width where necessary, were used to derive a series of geomorphologically-relevant process indicators. A suite of multivariate analyses were then applied to this data set, resulting in the discrimination of five river types: laterally stable, laterally active sinuous-meandering; transitional (near-braided); bedrock; and cascade/step dominated. The results of the classification were tested by examining the characteristics and distribution of the river classes in relation to known independent controls of river form including reach-scale energy and valley confinement conditions. Our results show that if methods of data extraction are carefully developed, physically meaningful river reach discrimination can be achieved using Google Earth. Although there are limits to the types of information that can be extracted such that field investigations cannot always be avoided, there is enormous potential to mine Google Earth across different space and time scales, supporting the assembly of large, reliable data sets relevant to river forms and processes in a very cost-effective way. © 2019 John Wiley & Sons, Ltd.     

Implementing of the JPWSPC method in RIV1H for unsteady flow modeling in general river networks

RIV1H is the stand-alone hydraulic program of CE-QUAL-RIV1, a longitudinal hydraulic and water quality model developed by U.S. Army Corps of Engineers Waterways Experiment Station. RIV1H solves the Saint-Venant equations using the widely accepted four-point implicit Preissmann scheme, and the resulting nonlinear equations are solved using the Newton-Raphson method. RIV1H is capable of simulating multiple branches, and in-stream hydraulic control structures. It treats tributary networks using a double sweep algorithm based on upstream ordering of the branches. It treats the control structures following a downstream solution order, which also is based on the upstream ordering of the branches. Since an upstream ordering cannot be achieved for looped networks, RIV1H is only applicable to non-looped tributary networks. In the current study, the junction-point water stage prediction and correction (JPWSPC) method is extended to take into account the control structures and the method is used to improve the RIV1H model, enabling it to be applied to both non-looped and looped networks with in-stream hydraulic control structures. The JPWSPC method makes the linear equation system for each segment complete while maintaining the banded property, thus the system can be independently and efficiently solved. It has the advantages to be efficient, robust, and very suitable for parallel computing. The improved RIV1H model was tested using two idealized networks and the results demonstrated the success of the improvement.     

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.     

Erosion probability model of base-soil particle migration into a granular filter under local flow

To avoid soil erosion, lters often are installed in the downstream cross section of dams. However, the probability of piping failure and the lter effect both are related to the soil pore network. Previous erosion probability models for a base soil- lter system do not consider the effect of local ow. Therefore, in this study, an improved erosion probability model is established, in which the deviation between the main ow direction of the soil and the local ow direction in the lter was considered based on a previous model. The improved model was validated by numerical simulation. The erosion probability was found to affect the reasonable evaluation of lter effects and the optimal selection of lter thickness. The controlling con- striction size in a granular lter was increased to Dc* Dc5 with a lter thickness of 59 Dm.