航空重力梯度仪研究现状及发展趋势

简要介绍了重力梯度仪的发展历程,重点叙述了旋转加速度计航空重力梯度仪的工作原理及研发过程,系统调研了航空重力梯度仪的仪器现状,跟踪了航空重力梯度仪的研究动态.     

High-resolution regional gravity field model of Pakistan based on best residual terrain model (RTM) of topography and interpolation techniques

This study focuses on the development of absolute gravity model for Pakistan based on best possible residual terrain model of gravity using residual terrain modeling technique. The datasets used for the development of model are observed gravity, global gravity models, and Shuttle Radar Topographic Mission (SRTM30) elevation data. The residual terrain modeling technique has been used in the remove-restore procedure for smoothing the observed gravity field. Different topographic elevation models were tested in the model selection and one best possible model with minimum mean and standard deviation was selected for residual terrain effects. Least square collocation technique has been used for quality control and error estimates. The best possible covariance model was established from residual gravity for onward prediction of gravity anomalies at the earth surface for error and prediction analysis. The residual terrain effect of gravity, value of free air anomaly from EGM96, and observed free air anomaly are added to normal gravity to compute the absolute gravity at earth surface. The prediction of these parameters is made by employing Lagrange interpolation with least square adjustment. The results are compared with ～5% randomly selected data points not utilized for the development of covariance function and/or model development. Spline interpolation technique has also been used for the prediction of gravity field-related parameters. Lagrange interpolation exhibits relatively superior results over spline-based interpolation. This is as per expectation due to the reason that additional gridding for spline interpolation filters the signal part as well. This fact is evident from the results of spline interpolation of Grid-I and Grid-II with relatively better prediction results in Grid-I. This version of the model is capable of prediction having limiting error of 30 mGal. The predicted results show that 96.16% of prediction data falls within above-mentioned limit with Lagrange interpolation technique with least square adjustment for whole Pakistan area. The adverse effect of gridding is absent in case of Grid-I due to relatively flat areas and predicted data matches totally with control values for both spline as well as Lagrange interpolations. However, in case of Grid-II which includes high mountains of Himalaya, gridding effect is present and the accuracy of the predicted results falls to ～92%. The computed results have been compared with absolute values predicted using EGM96 and EGM2008 models as well. The gravity field recovered with PAKGM model is much better, i.e., ～ 96.16%, than both with EGM96 and EGM2008 which is about 85% only.     

高精度航空重力测量系统分项指标设计分析

为研制高精度航空重力仪,开展了高精度航空重力仪分项指标设计与分析,为高精度航空重力测量系统设计提供依据。笔者基于航空重力测量的数学模型,归纳出影响航空重力测量的主要因素;结合航空重力测量的相关理论公式,使用理论模型,推导出各影响因素的误差模型;开展了各项误差的分析研究,并通过设定合理的分项指标精度来有效地控制分项误差,确保航空重力测量精度优于0.6×10-5m/s2。     

A nonparametric view of generalized covariances for kriging

Fitting trend and error covariance structure iteratively leads to bias in the estimated error variogram. Use of generalized increments overcomes this bias. Certain generalized increments yield difference equations in the variogram which permit graphical checking of the model. These equations extend to the case where errors are intrinsic random functions of order k, k=1, 2, ..., and an unbiased nonparametric graphical approach for investigating the generalized covariance function is developed. Hence, parametric models for the generalized covariance produced by BLUEPACK-3D or other methods may be assessed. Methods are illustrated on a set of coal ash data and a set of soil pH data.     

基于多元线性回归的航空重力梯度测量自身梯度校正

航空重力梯度勘探中,载体自身产生的重力梯度效应对于超高精度的重力梯度仪而言是一种严重的干扰。由于载体结构复杂,用常规的载体建模并正演难以精确去除这种干扰。本文从统计学角度出发,用多元线性回归来处理自身梯度效应,不需要对载体模型做出任何假设与近似,用纯数据驱动的方式来校正自身梯度的干扰。回归诊断与模型仿真验证了这种校正方式有较高的准确性,并且当实际的转角落在地面标定的范围内时具备预测能力。     

Calibration of imperfect models to biased observations

The problem of assimilating biased and inaccurate observations into inadequate models of the physical systems from which the observations were taken is common in the petroleum and groundwater fields. When large amounts of data are assimilated without accounting for model error and observation bias, predictions tend to be both overconfident and incorrect. In this paper, we propose a workflow for calibration of imperfect models to biased observations that involves model construction, model calibration, model criticism and model improvement. Model criticism is based on computation of model diagnostics which provide an indication of the validity of assumptions. During the model improvement step, we advocate identification of additional physically motivated parameters based on examination of data mismatch after calibration and addition of bias correction terms. If model diagnostics indicates the presence of residual model error after parameters have been added, then we advocate estimation of a “total” observation error covariance matrix, whose purpose is to reduce weighting of observations that cannot be matched because of deficiency of the model. Although the target applications of this methodology are in the subsurface, we illustrate the approach with two simplified examples involving prediction of the future velocity of fall of a sphere from models calibrated to a short-time series of biased measurements with independent additive random noise. The models into which the data are assimilated contain model errors due to neglect of physical processes and neglect of uncertainty in parameters. In every case, the estimated total error covariance is larger than the true observation covariance implying that the observations need not be matched to the accuracy of the measuring instrument. Predictions are much improved when all model improvement steps were taken.     

Estimating observation error covariance matrix of seismic data from a perspective of image denoising

Estimating observation error covariance matrix properly is a key step towards successful seismic history matching. Typically, observation errors of seismic data are spatially correlated; therefore, the observation error covariance matrix is non-diagonal. Estimating such a non-diagonal covariance matrix is the focus of the current study. We decompose the estimation into two steps: (1) estimate observation errors and (2) construct covariance matrix based on the estimated observation errors. Our focus is on step (1), whereas at step (2) we use a procedure similar to that in Aanonsen et al. 2003. In Aanonsen et al. 2003, step (1) is carried out using a local moving average algorithm. By treating seismic data as an image, this algorithm can be interpreted as a discrete convolution between an image and a rectangular window function. Following the perspective of image processing, we consider three types of image denoising methods, namely, local moving average with different window functions (as an extension of the method in Aanonsen et al. 2003), non-local means denoising and wavelet denoising. The performance of these three algorithms is compared using both synthetic and field seismic data. It is found that, in our investigated cases, the wavelet denoising method leads to the best performance in most of the time.     

Multivariable spatial prediction

For spatial prediction, it has been usual to predict one variable at a time, with the predictor using data from the same type of variable (kriging) or using additional data from auxiliary variables (cokriging). Optimal predictors can be expressed in terms of covariance functions or variograms. In earth science applications, it is often desirable to predict the joint spatial abundance of variables. A review of cokriging shows that a new cross-variogram allows optimal prediction without any symmetry condition on the covariance function. A bivariate model shows that cokriging with previously used cross-variograms can result in inferior prediction. The simultaneous spatial prediction of several variables, based on the new cross-variogram, is then developed. Multivariable spatial prediction yields the mean-squared prediction error matrix, and so allows the construction of multivariate prediction regions. Relationships between cross-variograms, between single-variable and multivariable spatial prediction, and between generalized least squares estimation and spatial prediction are also given.     

发展中国航空物探技术有关问题的思考

本文概要介绍了中国航空物探(航磁、航空电磁、航空重力、航空伽马能谱)技术现状,近年来在仪器研制、测量技术、数据处理、解释方法与软件研发等方面的最新进展,分析了中国航空物探技术发展存在的主要问题.对中国航空物探技术的发展趋势进行了探讨.预测航空物探技术将向提高对探测目标的分辨能力和探测深度的方向发展,航空重力测量技术将得到广泛的应用.提出了以国家需求为导向,重点发展航磁多参数测量方法技术、航空重力测量和重力梯度测量技术、大探测深度时间域航空电磁测量技术,突破高温超导探测器研制的关键技术,发展航空重、磁、电的各种反演技术与三维可视化技术,建立物探仪器重点实验室,完善航空物探动态试验场,加快标准化建设,加快发展与航空物探相关行业(如通用航空)等建议.     

航空重力弱信号提取的卡尔曼滤波方法研究

航空重力测量以飞机为载体对重力场数据进行采集.由于气流、飞行状态及机体自身振动等因素的影响,航空重力测量原始数据含有大量的噪声,信噪比高达上万级分之一,因此从原始测量数据中获取弱小重力信号成为航空重力测量系统发展的一个技术难题.本文针对航空重力测量系统将通用卡尔曼滤波公式进行了适应性调整,建立了航空重力异常的数学模型,...     

Generalized cross-validation for covariance model selection

A weighted cross-validation technique known in the spline literature as generalized cross-validation (GCV), is proposed for covariance model selection and parameter estimation. Weights for prediction errors are selected to give more importance to a cluster of points than isolated points. Clustered points are estimated better by their neighbors and are more sensitive to model parameters. This rational weighting scheme also provides a simplifying significantly the computation of the cross-validation mean square error of prediction. With small- to medium-size datasets, GCV is performed in a global neighborhood. Optimization of usual isotropic models requires only a small number of matrix inversions. A small dataset and a simulation are used to compare performances of GCV to ordinary cross-validation (OCV) and least-squares filling (LS).     

On maximum likelihood estimation of parameters in incorrectly specified models of covariance for spatial data

The analysis of, and from, models of spatial data usually proceeds under the assumption, often implicit, that the correct model has been specified. However, any model identification procedures based on sample data are subject to error, and consequences of such errors then permeate subsequent analysis. Thus, an attempt to quantify some of these consequences is of interest. A standard framework for analysis is extended here, by introduction of information theory, to permit the study of effects of model misspecification on maximum likelihood estimators of parameters of model covariance. Asymptotically valid theoretical results are presented, and the relevance of these results to samples of finite sizes met in practice is assessed in a series of simulation experiments. The effect of model misspecification, and use of estimators of parameters of misspecified covariance models, on the practical problem of prediction at a previously unsampled location is considered briefly, and further areas for possible investigation are outlined.     

海陆过渡区空地重力数据融合及评价

海陆过渡区是海洋与陆地复合与交叉的地理单元,是资源与环境条件最为优越的地区,将航空重力与地面重力有效融合,形成连续的重力场资料,对海陆过渡区资源与环境评价具有重要意义。本文选择日照—连云港海陆过渡区为示范区,开展了航空重力数据与地面重力数据的融合研究。通过数据整理与对比分析,总结了数据间系统差的确定方法,提出了空地重力数据间一致性与差异性的评估方法,并分别采用剖面和平面两种形式对数据间的一致性和差异性进行了量化评估;在数据量化评估的基础上,采用平面均方根误差法分别对混合法与缝合法的数据拼接质量进行了定量评价,为数据融合方法的选择提供了定量依据。研究结果表明,日照—连云港海陆过渡区航空重力与地面重力数据间剖面上的一致系数为0. 81~0. 99,平面上的一致系数为0. 95,数据间一致性较高,具备较好的数据融合基础;对比试验表明,缝合法对于海陆过渡区空地重力数据融合效果更佳,采用缝合法融合后的重力数据与原始空地重力数据间的均方差分别为2. 63×10 -5 m/s 2 、0. 98×10 -5 m/s 2 ,取得了较好的融合效果。日照—连云港海陆过渡区空地重力数据的融合,为同类地区开展多源重力数据的融合提供了借鉴。     

航空重力梯度测量飞行姿态影响及误差校正

在石油和金属矿勘探中,相对于重力异常数据,航空重力梯度数据含有更多的高频信号成分,能更好的描述小的异常特征。为了得到地下异常体的真实重力梯度数据,需要全张量梯度测量数据中有效地去除飞机自身梯度值的影响。针对这一影响,提出线性的校正方法。考虑飞行姿态的改变引起的空间坐标系的变化,在校正过程中给出旋转矩阵和张量转换矩阵来实现坐标系的统一。通过模型实验分析了自身梯度值随姿态角变化情况,用实际梯度数据计算证明了该方法能够减小几个E的姿态误差,有效提高数据的精度。     

利用重力异常的小波模方法识别断层

利用小波特性可以提取航空重力异常的重要信息,笔者采用小波分析方法建立三维地质体模型,讨论了小波模与地质体边界的对应关系。以我国某海域航空重力资料为例,利用小波模极大值识别了断裂信息,并与航空布格重力异常及其方向导数识别的断裂进行对比分析。结果表明,利用小波模不仅可以识别断裂构造,在航空布格异常和方向导数识别模糊的地区效果更为明显。     

A multiresolution ensemble Kalman filter using the wavelet decomposition

We present a method of using classical wavelet-based multiresolution analysis to separate scales in model and observations during data assimilation with the ensemble Kalman filter. In many applications, the underlying physics of a phenomena involve the interaction of features at multiple scales. Blending of observational and model error across scales can result in large forecast inaccuracies since large errors at one scale are interpreted as inexact data at all scales due to the misrepresentation of observational error. Our method uses a partitioning of the range of the observation operator into separate observation scales. This naturally induces a transformation of the observation covariance and we put forward several algorithms to efficiently compute the transformed covariance. Another advantage of our multiresolution ensemble Kalman filter is that scales can be weighted independently to adjust each scale’s affect on the forecast. To demonstrate feasibility, we present applications to a one-dimensional Kuramoto-Sivashinsky (K–S) model with scale-dependent observation noise and an application involving the forecasting of solar photospheric flux. The solar flux application uses the Air Force Data Assimilative Photospheric Transport (ADAPT) model which has model and observation error exhibiting strong scale dependence. Results using our multiresolution ensemble Kalman filter show significant improvement in solar forecast error compared to traditional ensemble Kalman filtering.     

On the use of airborne gravimetry in gravity field modelling: Experiences from the AGMASCO project

Important areas of the earth are still not covered by accurate gravity measurements. The gravity field may be determined by using different techniques but airborne gravity surveying is becoming the most powerful tool available today.One of the main problems in airborne gravity is the separation of the vertical accelerations acting on the airborne platform from the natural gravity signal. With the advances in DGPS techniques new prospects arise for gravity field recovery which are of great importance for geodesy, geophysics oceanography and satellite navigation. Furthermore, airborne gravimetric measurements depend not only on the determination of the position but also on the attitude of the aircraft. Inertial systems can provide attitude as well as information on short-term accelerations, which are more problematic for the gravimeter. A proper integration of these systems may allow a further improvement of the whole technique where the quality of both the accelerometers and the gyros is the key sensing element. In the scope of the MAST III Project AGMASCO, an airborne geoid mapping system was successfully implemented in different aeroplanes. The characteristics of the aeroplane and the flight parameters play a major role in airborne measurements.Within AGMASCO the airborne system was applied both in a close and an open ocean (Skagerrak, Fram Strait and Azores) areas. The system proved to be a powerful tool in a variety of conditions. The results obtained showed that an accuracy better than 2mGal over 5 to 6 kilometres can be achieved.This was proven by comparison of the airborne data with ground truth and satellite data. This accuracy makes the system interesting for use in various applications including geophysical exploitation.Different hardware installations were experienced and the methods validated. Recovery of the gravity values directly from measurements with the Lacoste & Romberg air/sea gravimeter and from measurements with the inertial sensors was analysed. The potential of these sensors to recover gravity and the experience gained within this project are reported here.     

Quadratic covariance estimation and equivalence of predictions

This article illustrates the use of linear and nonlinear regression models to obtain quadratic estimates of covariance parameters. These models lead to new insights into the motivation behind estimation methods, the relationships between different methods, and the relationship of covariance estimation to prediction. In particular, we derive the standard estimating equations for minimum norm quadratic unbiased translation invariant estimates (MINQUEs) from an appropriate linear model. Connections between the linear model, minimum variance quadratic unbiased translation invariant estimates (MIVQUEs), and MINQUEs are examined and we provide a minimum norm justification for the use of one-step normal theory maximum likelihood estimates. A nonlinear regression model is used to define MINQUEs for nonlinear covariance structures and obtain REML estimates. Finally, the equivalence of predictions under various models is examined when covariance parameters are estimated. In particular, we establish that when using MINQUE, iterative MINQUE, or restricted maximum likelihood (REML) estimates, the choice between a stationary covariance function and an intrinsically stationary semivariogram is irrelevant to predictions and estimated prediction variances.     

航空重力起伏飞行中飞机姿态对测量数据影响分析

在航空重力飞行时,针对GT-1A航空重力系统在同一条测线中使用两种爬升率(1 m/s和2 m/s)进行起伏飞行。考察起伏飞行引起的飞机姿态变化对测量数据的影响,以及在航空重力测量中小坡度缓起伏作业的可行性,是笔者的研究目的。试验结果表明,所使用的两种爬升率对航空重力测量数据未有明显影响,GT-1A航空重力系统可以进行缓起伏飞行测量。     

Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR

In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS.