Noise filtering of full-gravity gradient tensor data

In oil and mineral exploration, gravity gradient tensor data include higher- frequency signals than gravity data, which can be used to delineate small-scale anomalies. However, full-tensor gradiometry （FTG） data are contaminated by high-frequency random noise. The separation of noise from high-frequency signals is one of the most challenging tasks in processing of gravity gradient tensor data. We first derive the Cartesian equations of gravity gradient tensors under the constraint of the Laplace equation and the expression for the gravitational potential, and then we use the Cartesian equations to fit the measured gradient tensor data by using optimal linear inversion and remove the noise from the measured data. Based on model tests, we confirm that not only this method removes the high- frequency random noise but also enhances the weak anomaly signals masked by the noise. Compared with traditional low-pass filtering methods, this method avoids removing noise by sacrificing resolution. Finally, we apply our method to real gravity gradient tensor data acquired by Bell Geospace for the Vinton Dome at the Texas-Louisiana border.

Full gravity gradient tensors from vertical gravity by cosine transform

We present a method to calculate the full gravity gradient tensors from pre-existing vertical gravity data using the cosine transform technique and discuss the calculated tensor accuracy when the gravity anomalies are contaminated by noise. Gravity gradient tensors computation on 2D infinite horizontal cylinder and 3D ＂Y＂ type dyke models show that the results computed with the DCT technique are more accurate than the FFT technique regardless if the gravity anomalies are contaminated by noise or not. The DCT precision has increased 2 to 3 times from the standard deviation. In application, the gravity gradient tensors of the Hulin basin calculated by DCT and FFT show that the two results are consistent with each other. However, the DCT results are smoother than results computed with FFT. This shows that the proposed method is less affected by noise and can better reflect the fault distribution.

Full magnetic gradient tensor from triaxial aeromagnetic gradient measurements:Calculation and application

The full magnetic gradient tensor （MGT） refers to the spatial change rate of the three field components of the geomagnetic field vector along three mutually orthogonal axes. The tensor is of use to geological mapping, resources exploration, magnetic navigation, and others. However, it is very difficult to measure the full magnetic tensor gradient using existing engineering technology. We present a method to use triaxial aeromagnetic gradient measurements for deriving the full MGT. The method uses the triaxial gradient data and makes full use of the variation of the magnetic anomaly modulus in three dimensions to obtain a self-consistent magnetic tensor gradient. Numerical simulations show that the full MGT data obtained with the proposed method are of high precision and satisfy the requirements of data processing. We selected triaxial aeromagnetic gradient data from the Hebei Province for calculating the full MGT. Data processing shows that using triaxial tensor gradient data allows to take advantage of the spatial rate of change of the total field in three dimensions and suppresses part of the independent noise in the aeromagnetic gradient. The calculated tensor components have improved resolution, and the transformed full tensor gradient satisfies the requirement of geological mapping and interpretation.

Edge enhancement of gravity anomalies and gravity gradient tensors using an improved small sub-domain filtering method

In order to enhance geological body boundary visual effects in images and improve interpretation accuracy using gravity and magnetic field data, we propose an improved small sub-domain filtering method to enhance gravity anomalies and gravity gradient tensors. We discuss the effect of Gaussian white noise on the improved small sub-domain filtering method, as well as analyze the effect of window size on geological body edge recognition at different extension directions. Model experiments show that the improved small sub-domain filtering method is less affected by noise, filter window size, and geological body edge direction so it can more accurately depict geological body edges than the conventional small sub-domain filtering method. It also shows that deeply buried body edges can be well delineated through increasing the filter window size. In application, the enhanced gravity anomalies and calculated gravity gradient tensors of the Hulin basin show that the improved small sub-domain filtering can recognize more horizontal fault locations than the conventional method.

Calculation and correction of magnetic object positioning error caused by magnetic field gradient tensor measurement

Magnetic field gradient tensor measurement is an important technique to obtain position information of magnetic objects. When using magnetic field sensors to measure magnetic field gradient as the coefficients of tensor, field differentiation is generally approximated by field difference. As a result, magnetic objects positioning by magnetic field gradient tensor measurement always involves an inherent error caused by sensor sizes, leading to a reduction in detectable distance and detectable angle. In this paper, the inherent positioning error caused by magnetic field gradient tensor measurement is calculated and corrected by iterations based on the systematic position error distribution patterns. The results show that, the detectable distance range and the angle range of an ac magnetic object（2.44 Am^2@1 kHz） can be increased from（0.45 m, 0.75 m）,（0？, 25？） to（0.30 m, 0.80 m）,（0？,80？）, respectively.

Three direction analytical signal analysis method of magnetic gradient tensor and application in the interpretation

Compared to conventional magnetic data,magnetic gradient tensor data contain more high-frequency signal components,which can better describe the features of geological bodies.The directional analytic signal of the magnetic gradient tensor is not easily interfered from the tilting magnetization,but it can infer the range of the fi eld source more accurately.However,the analytic signal strength decays faster with depth,making it diffi cult to identify deep fi eld sources.Balanced-boundary recognition can eff ectively overcome this disadvantage.We present here a balanced-boundary identifi cation technique based on the normalization of three-directional analytic signals from aeromagnetic gradient tensor data.This method can eff ectively prevent the fast attenuation of analytic signals.We also derive an Euler inversion algorithm of three-directional analytic signal derivative.By combining magnetic-anomaly model testing with the traditional magnetic anomaly interpretation method,we show that the boundary-recognition technology based on a magnetic gradient tensor analytic signal has a greater advantage in identifying the boundaries of the geological body and can better refl ect shallow anomalies.The characteristics of the Euler equation based on the magnetic anomaly direction to resolve the signal derivative have better convergence,and the obtained solution is more concentrated,which can obtain the depth and horizontal range information of the geological body more accurately.Applying the above method to the measured magneticanomaly gradient data from Baoding area,more accurate fi eld source information is obtained,which shows the feasibility of applying this method to geological interpretations.

Direct numerical simulation study of the interaction between the polymer effect and velocity gradient tensor in decaying homogeneous isotropic turbulence

Direct numerical simulation of decaying homogeneous isotropic turbulence （DHIT） of a polymer solution is performed. In order to understand the polymer effect on turbulence or additive-turbulence interaction, we directly investigate the influence of polymers on velocity gradient tensor including vorticity and strain. By visualizing vortex tubes and sheets, we observe a remarkable inhibition of vortex structures in an intermediate-scale field and a small-scale field but not for a large scale field in DHIT with polymers. The geometric study indicates a strong relevance among the vorticity vector, rate-of-strain tensor, and polymer conformation tensor. Joint probability density functions show that the polymer effect can increase ＂strain generation resistance＂ and ＂vorticity generation resistance＂, i.e., inhibit the generation of vortex sheets and tubes, ultimately leading to turbulence inhibition effects.

Fast 3D forward modeling of the magnetic field and gradient tensor on an undulated surface

Magnetic field gradient tensor technique provides abundant data for delicate inversion of subsurface magnetic susceptibility distribution. Large scale magnetic data inversion imaging requires high speed and accuracy for forward modeling. For arbitrarily distributed susceptibility data on an undulated surface, we propose a fast 3D forward modeling method in the wavenumber domain based on(1) the wavenumber-domain expression of the prism combination model and the Gauss–FFT algorithm and(2) cubic spline interpolation. We apply the proposed 3D forward modeling method to synthetic data and use weighting coefficients in the wavenumber domain to improve the modeling for multiple observation surfaces, and also demonstrate the accuracy and efficiency of the proposed method.

Dynamics and geometry of developing planar jets based on the invariants of the velocity gradient tensor

Based on the direct numerical simulation （DNS）, the developing planar jets under different initial conditions, e.g., the con- ditions of the exit Reynolds number and the exit mean velocity profile, are investigated. We mainly focus on the characteristics of the invariants of the velocity gradient tensor, which provides insights into the evolution of the dynamics and the geometry of the planar jets along with the flow transition. The results show that the initial flow near the jet exit is strongly predominated by the dissipation over the enstrophy, the flow transition is accompanied by a severe rotation and straining of the flow elements, where the vortex structure evolves faster than the fluid element deformation, in the fully-developed state, the irrotational dissipation is dominant and the most probable geometry of the fluid elements should remain between the biaxial stretching and the axisymmetric stretching. In addition, with a small exit Re and a parabolic profile for the exit mean streamwise velocity, the decay of the mean flow field and the magnitude of the turbulent variables will be strengthened in the process of the flow transition, however, a large exit Re will promote the flow transition to the fully-developed state. The cross-impact between the exit Re and the exit mean velocity profile is also observed in the present study.

Studying turbulence structure near the wall in hydrodynamic flows:An approach based on the Schur decomposition of the velocity gradient tensor

The Schur decomposition of the velocity gradient tensor(VGT)is an alternative to the classical Cauchy-Stokes decomposition into rotation rate and strain rate components.Recently,there have been several strands of work that have employed this decomposition to examine the physics of turbulence dynamics,including approaches that combine the Schur and Cauchy-Stokes formalisms.These are briefly reviewed before the latter approach is set out.This partitions the rotation rate and strain rate tensors into normal/local and non-normal/non-local contributions.We then study the relation between the VGT dynamics and ejection-sweep events in a channel flow boundary-layer.We show that the sweeps in particular exhibit novel behaviour compared with either the other quadrants,or the flow in general,with a much-reduced contribution to the dynamics from the non-normal terms above the viscous sub-layer.In particular,the reduction in the production term that is the interaction between the non-normality and the normal straining reduces in the log-layer as a consequence of an absence of alignment between the non-normal vorticity and the strain rate eigenvectors.There have been early forays into using the Schur transform approach for subgrid-scale modelling in large-eddy simulation(LES)and this would appear to be an exciting way forward.

Magnetic susceptibility inversion method with full tensor gradient data using low-temperature SQUIDs

Full tensor magnetic gradient measurements are available nowadays. These are essential for determining magnetization parameters in deep layers. Using full or partial tensor magnetic gradient measurements to determine the subsurface properties, e.g., magnetic susceptibility, is an inverse problem. Inversion using total magnetic intensity data is a traditional way.Because of di culty in obtaining the practical full tensor magnetic gradient data, the corresponding inversion results are not so widely reported. With the development of superconducting quantum interference devices(SQUIDs), we can acquire the full tensor magnetic gradient data through field measurements. In this paper, we study the inverse problem of retrieving magnetic susceptibility with the field data using our designed low-temperature SQUIDs. The solving methodology based on sparse regularization and an alternating directions method of multipliers is established. Numerical and field data experiments are performed to show the feasibility of our algorithm.

The effect of different number of diffusion gradients on SNR of diffusion tensor-derived measurement maps

Diffusion tensor imaging (DTI) is mainly applied to white matter fiber tracking in human brain, but there is still a debate on how many diffusion gradient directions should be used to get the best results. In this paper, the performance of 7 protocols corresponding to 6, 9, 12, 15, 20, 25, and 30 noncollinear number of diffusion gradi-ent directions (NDGD) were discussed by com-paring signal-noise ratio (SNR) of tensor- de-rived measurement maps and fractional ani-sotropy (FA) values. All DTI data (eight healthy volunteers) were downloaded from the website of Johns Hopkins Medical Institute Laboratory of Brain Anatomi-cal MRI with permission. FA, apparent diffusion constant mean (ADC-mean), the largest eigen-value (LEV), and eigenvector orientation (EVO) maps associated with LEV of all subjects were calculated derived from tensor in the 7 proto-cols via DTI Studio. A method to estimate the variance was presented to calculate SNR of these tensor-derived maps. Mean ±standard deviation of the SNR and FA values within re-gion of interest (ROI) selected in the white mat-ter were compared among the 7 protocols. The SNR were improved significantly with NDGD increasing from 6 to 20 (P<0.05). From 20 to 30, SNR were improved significantly for LEV and EVO maps (P<0.05), but no significant dif-ferences for FA and ADC-mean maps (P>0.05). There were no significant variances in FA val-ues within ROI between any two protocols (P>0.05). The SNR could be improved with NDGD in-creasing, but an optimum protocol is needed because of clinical limitations.

ON SHRINKING GRADIENT RICCI SOLITONS WITH POSITIVE RICCI CURVATURE AND SMALL WEYL TENSOR

We show that closed shrinking gradient Ricci solitons with positive Ricci curvature and sufficiently pinched Weyl tensor are Einstein. When Weyl tensor vanishes, this has been proved before but our proof here is much simpler.

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.

基于伪磁梯度张量混合方向tilt梯度的磁源定位方法及其应用:以内蒙古塔木素铀矿床为例

斜磁化磁源的准确定位是磁法勘探有效圈定场源分布及划分断裂构造的重要依据.但受磁化方向影响,总磁异常与地质体分布并无明显对应关系.为此,本文从总磁异常垂向积分出发,推导出了伪磁梯度张量,定义了伪磁梯度张量的混合方向tilt梯度及混合方向解析信号振幅,提出了基于伪磁梯度张量混合方向tilt梯度水平导数组合的场源定位方法.模型试验表明,相对于解析信号振幅、tilt梯度的总水平导数及方向tilt梯度的总水平导数模这3种受磁化方向影响较小的方法,本文提出的磁源定位方法能够更有效地反映出不同磁化方向的磁源位置,具有更强的识别能力和定位精度.在内蒙古塔木素铀矿床的航磁资料应用中,新方法较好地展示出了已知断裂带的展布及出露岩浆岩的分布,同时有效地提取了塔木素铀矿床上方的弱磁异常信息,处理结果为研究区圈定隐伏岩体、划分隐伏断裂及扩大铀矿找矿靶区提供了依据.

碳酸盐岩强非均质储层相控反演方法研究

岩溶缝洞体是碳酸盐岩主要储层类型,具有强非均质性特征,量化预测难度大。常规地震反演方法虽然可实现储层的量化预测,但满足不了岩溶缝洞体中多类型储层的量化及高精度研究需求。针对这一问题,在相控约束反演技术思路的基础上,提出了基于梯度结构张量属性约束的确定性相控反演方法。该方法可概述为3步:首先,基于梯度结构张量属性,划分出反映碳酸盐岩缝洞体轮廓的储层相与非储层相;其次,以地震相为约束条件,建立低频模型;最后,将低频模型应用于地震反演过程,得到储层敏感属性,进而实现碳酸盐岩强非均质储层的量化预测。模型试算结果和塔里木盆地M工区实际应用结果均表明,该方法能有效识别岩溶缝洞体中多类型储层的分布范围,与实钻结果及开发动态特征吻合,为碳酸盐岩缝洞型油藏的整体量化研究奠定了基础。

基于U⁃Rnet的重力全张量梯度数据反演

重力反演是通过地表信息获取地下地质体空间结构与物理性质的重要手段之一。每个重力梯度分量反映不同的地质体信息,联合重力梯度分量进行重力反演能够更好地研究地下密度异常体的形态和分布。为此,提出基于神经网络的重力全张量梯度数据反演算法,将U⁃Rnet网络应用于重力全张量数据的三维反演问题。为了检验该算法的有效性,采用六种典型模型进行模拟实验,获得了具有清晰边界和稀疏的反演结果。首先,对比L2和Tversky两种损失函数的反演结果,后者的反演结果能更清晰地反映模型的边界位置;然后,对不同梯度张量组合进行反演,四组实验结果在三个方向(x、y、z)上具有不同的反演精度,组合四的误差最低;最后,将该方法应用于美国德克萨斯州文顿盐丘的FTG数据,反演结果与实际地质信息基本吻合。

基于磁梯度张量的滑坡监测方法

滑坡监测是滑坡防治的有效技术手段,针对钻孔测斜仪在滑坡深部发生大位移时失效的问题,提出一种基于磁梯度张量的滑坡监测方法。该方法以磁梯度张量理论为基础,使用了十字形阵列进行监测,通过测得的磁感应强度三分量得到轴心的梯度值,再根据反演计算出目标磁源的空间坐标,通过实验验证了该方法的可行性。在测量前将磁传感器阵列固定并计算偏离角,之后使永磁体按照矩形轨迹缓慢运动,并选取一定的测量点验证了该方法的精度,三轴平均绝对误差在0.1m以内,三轴最大误差在0.2m以内。最后分析了该方法存在误差的原因。

基于归一化磁源强度的磁目标实时定位方法

标量三角剖分与测距(STAR)方法利用磁梯度收缩特性实现运动状态下对单个磁偶极子目标的实时定位,且对运动平台的姿态不敏感。然而,磁梯度收缩量(C_(T))引入的非球面误差会限制其定位精度。针对该问题,提出了一种新的磁偶极子目标线性定位方法,利用归一化磁源强度(NSS)代替C_(T),消除了非球面误差的影响。为了分析该方法对不同方位磁目标的定位性能,建立了全向仿真分析模型。仿真结果表明,该方法的实时性与STAR方法相当,在标准差为10 nT的高斯白噪声影响下与STAR方法的最大定位误差分别为0.18 m与1.5 m。现场实验结果表明,当探测距离为1.284 m时,该方法在纬线轨迹上的平均定位误差仅为0.094 m,比传统STAR方法低56.7%;在经线轨迹上的平均定位误差为0.097 m,比传统STAR方法低30.7%。

利用重力梯度张量等位面曲率的地质体定位

针对重力梯度张量曲率的研究,前人的工作主要集中在重力张量曲率的解释及边缘检测中,几乎没有涉及到地下密度异常体的定位.本文结合重力矢量和重力梯度张量提出了一套基于重力梯度张量等位面曲率的地下密度异常体位置估计策略.首先,从重力梯度张量等位面曲率的基本定义出发,计算重力梯度张量等位面曲率.然后,通过寻找球面或圆形等位面的重力梯度张量曲率,提出了利用最大主曲率定位地下密度异常体位置的源参数估计方法,并详细推导了估计3D球体(质点)和2D水平线源位置信息的解析表达式.再者,针对噪声和多源存在的情况,提出了一套利用重力梯度张量等位面曲率获得密度异常体位置信息的稳健估计流程,并利用模糊C均值聚类算法进一步确定地下密度异常体的中心位置.最后,通过理论模型测试和文顿盐丘实测航空重力梯度数据测试,验证了本文算法的可行性和可靠性.结果表明:在满足曲率半径定义条件的情况下,本文所提出的源参数估计方法可以定位单个或多个地下3D和2D密度异常体的空间位置,具有较好的稳健性和抗噪能力.该方法拓展了重力梯度张量曲率的应用范围,可为重力梯度张量的三维反演工作提供先验的空间位置信息.