Characterisation of subsurface spatial variability using a cone resistivity penetrometer

The subsurface spatial variation in clay soils, such as thin-layered sand seams, affects the mechanical strength and electrical resistivity. The objective of this study is the development and application of cone resistivity penetrometer (CRP), which measures the cone tip resistance, sleeve friction, and electrical resistivity to evaluate the subsurface spatial variability. The electrical resistivity is measured at the cone tip to increase its resolution. Two outer diameters of the cone resistivity penetrometers (CRPs) are developed: D=10 mm CRP with a projected area of 0.78 cm² and D=15 mm CRP with a projected area of 1.76 cm². The cone tip resistance is effectively separated using a friction sleeve. Strain gauges are used to measure the mechanical strength, and coaxial type electrodes monitor the electrical resistivity. The application tests in the laboratory are conducted using layered soils and saturated sands. In addition, the penetration tests in the field are carried out and compared with the standard piezocone test. The penetration tests show that the soil layers and the density changes are clearly detected by the electrical resistivity and mechanical strength. Field tests show that CRP clearly evaluates the subsurface profile. This study suggests that CRP may be a useful technique for the evaluation of subsurface spatial variability during penetration testing.     

Fast imaging of subsurface conductors using very low‐frequency electromagnetic data

The study presents a fast imaging technique for the very low‐frequency data interpretation. First, an analytical expression was derived to compute the vertical component of the magnetic field at any point on the Earth's surface for a given current density distribution in a rectangular block on the subsurface. Current density is considered as exponentially decreasing with depth, according to the skin depth rule in a particular block. Subsequently, the vertical component of the magnetic field due to the entire subsurface was computed as the sum of the vertical component of the magnetic field due to an individual block. Since the vertical component of the magnetic field is proportional to the real part of very low‐frequency anomaly, an inversion program was developed for imaging of the subsurface conductors using the real very low‐frequency anomaly in terms of apparent current density distribution in the subsurface. Imaging results from the presented formulation were compared with other imaging techniques in terms of apparent current density and resistivity distribution using a standard numerical forward modelling and inversion technique. Efficacy of the developed approach was demonstrated for the interpretation of synthetic and field very low‐frequency data. The presented imaging technique shows improvement with respect to the filtering approaches in depicting subsurface conductors. Further, results obtained using the presented approach are closer to the results of rigorous resistivity inversion. Since the presented approach uses only the real anomaly, which is not sensitive to very small isolated near‐surface conducting features, it depicts prominent conducting features in the subsurface.     

2D data modelling by electrical resistivity tomography for complex subsurface geology

A new tool for two‐dimensional apparent‐resistivity data modelling and inversion is presented. The study is developed according to the idea that the best way to deal with ill‐posedness of geoelectrical inverse problems lies in constructing algorithms which allow a flexible control of the physical and mathematical elements involved in the resolution. The forward problem is solved through a finite‐difference algorithm, whose main features are a versatile user‐defined discretization of the domain and a new approach to the solution of the inverse Fourier transform. The inversion procedure is based on an iterative smoothness‐constrained least‐squares algorithm. As mentioned, the code is constructed to ensure flexibility in resolution. This is first achieved by starting the inversion from an arbitrarily defined model. In our approach, a Jacobian matrix is calculated at each iteration, using a generalization of Cohn's network sensitivity theorem. Another versatile feature is the issue of introducing a priori information about the solution. Regions of the domain can be constrained to vary between two limits (the lower and upper bounds) by using inequality constraints. A second possibility is to include the starting model in the objective function used to determine an improved estimate of the unknown parameters and to constrain the solution to the above model. Furthermore, the possibility either of defining a discretization of the domain that exactly fits the underground structures or of refining the mesh of the grid certainly leads to more accurate solutions. Control on the mathematical elements in the inversion algorithm is also allowed. The smoothness matrix can be modified in order to penalize roughness in any one direction. An empirical way of assigning the regularization parameter (damping) is defined, but the user can also decide to assign it manually at each iteration. An appropriate tool was constructed with the purpose of handling the inversion results, for example to correct reconstructed models and to check the effects of such changes on the calculated apparent resistivity. Tests on synthetic and real data, in particular in handling indeterminate cases, show that the flexible approach is a good way to build a detailed picture of the prospected area.     

Characterization of the subsurface architecture and identification of potential groundwater paths in a clay-rich floodplain using multi-electrode resistivity imaging

The interaction between surface water and groundwater in clay-rich fluvial environments can be complex and is generally poorly understood. Airborne electromagnetic surveys are often used for characterizing regional groundwater systems, but they are constrained by the resolution of the method. A resistivity imaging survey has been carried out in the Macquarie Marshes (New South Wales, Australia) in combination with water chemical sampling. The results have enabled the identification of buried palaeochannels and the location of potential recharge points. The data have been compared with previously published airborne electromagnetic data in the same area. Deeper less conductive features suggest that there is a potential connection between the Great Artesian Basin and groundwater contained within the shallow sand aquifer. Even though the chemistry of the groundwater samples does not indicate interaction with the Great Artesian Basin, the observed discontinuity in the saprolite implies potential for this to happen in other locations.     

Focused imaging of electrical resistivity data in archaeological prospecting

Electrical resistivity mapping and electrical resistivity profiling are powerful instruments for investigating archaeological structures. Interpretation of geoelectrical data is complicated by near-surface anomalies and the characteristics of the applied electrode arrays. Averaging Wenner α and Wenner β data as an alternative method of focused imaging is presented to overcome these problems. The mechanism of focused imaging is explained using the sensitivity distribution of the combined arrays. Various methods of imaging geoelectrical data are examined with synthetic and field data. In electrical resistivity mapping, inversion of the data is unnecessary when using focused imaging. In electrical resistivity profiling, focused imaging gives a first idea about the subsurface resistivity distribution without achieving the quality obtainable by inversion.     

Inversion of DC resistivity data using neural networks

The inversion of geoelectrical resistivity data is a difficult task due to its non-linear nature. In this work, the neural network (NN) approach is studied to solve both 1D and 2D resistivity inverse problems. The efficiency of a widespread, supervised training network, the back-propagation technique and its applicability to the resistivity problem, is investigated. Several NN paradigms have been tried on a basis of trial-and-error for two types of data set. In the 1D problem, the batch back-propagation paradigm was efficient while another paradigm, called resilient propagation, was used in the 2D problem. The network was trained with synthetic examples and tested on another set of synthetic data as well as on the field data. The neural network gave a result highly correlated with that of conventional serial algorithms. It proved to be a fast, accurate and objective method for depth and resistivity estimation of both 1D and 2D DC resistivity data. The main advantage of using NN for resistivity inversion is that once the network has been trained it can perform the inversion of any vertical electrical sounding data set very rapidly.     

Image processing of 2D resistivity data for imaging faults

A methodology to locate automatically limits or boundaries between different geological bodies in 2D electrical tomography is proposed, using a crest line extraction process in gradient images. This method is applied on several synthetic models and on field data set acquired on three experimental sites during the European project PALEOSIS where trenches were dug. The results presented in this work are valid for electrical tomographies data collected with a Wenner-alpha array and computed with an l₁ norm (blocky inversion) as optimization method. For the synthetic cases, three geometric contexts are modelled: a vertical and a dipping fault juxtaposing two different geological formations and a step-like structure. A superficial layer can cover each geological structure. In these three situations, the method locates the synthetic faults and layer boundaries, and determines fault displacement but with several limitations. The estimated fault positions correlate exactly with the synthetic ones if a conductive (or no superficial) layer overlies the studied structure. When a resistive layer with a thickness of 6 m covers the model, faults are positioned with a maximum error of 1 m. Moreover, when a resistive and/or a thick top layer is present, the resolution significantly decreases for the fault displacement estimation (error up to 150%). The tests with the synthetic models for surveys using the Wenner-alpha array indicate that the proposed methodology is best suited to vertical and horizontal contacts. Application of the methodology to real data sets shows that a lateral resistivity contrast of 1:5–1:10 leads to exact faults location. A fault contact with a resistivity contrast of 1:0.75 and overlaid by a resistive layer with a thickness of 1 m gives an error location ranging from 1 to 3 m. Moreover, no result is obtained for a contact with very low contrasts (1:0.85) overlaid by a resistive soil. The method shows poor results when vertical gradients are greater than horizontal ones. This kind of image processing technique should be systematically used for improving the objectiveness of tomography interpretation when looking for limits between geological objects.     

Image processing of 2D resistivity data for imaging faults

A methodology to locate automatically limits or boundaries between different geological bodies in 2D electrical tomography is proposed, using a crest line extraction process in gradient images. This method is applied on several synthetic models and on field data set acquired on three experimental sites during the European project PALEOSIS where trenches were dug. The results presented in this work are valid for electrical tomographies data collected with a Wenner-alpha array and computed with an l₁ norm (blocky inversion) as optimization method. For the synthetic cases, three geometric contexts are modelled: a vertical and a dipping fault juxtaposing two different geological formations and a step-like structure. A superficial layer can cover each geological structure. In these three situations, the method locates the synthetic faults and layer boundaries, and determines fault displacement but with several limitations. The estimated fault positions correlate exactly with the synthetic ones if a conductive (or no superficial) layer overlies the studied structure. When a resistive layer with a thickness of 6 m covers the model, faults are positioned with a maximum error of 1 m. Moreover, when a resistive and/or a thick top layer is present, the resolution significantly decreases for the fault displacement estimation (error up to 150%). The tests with the synthetic models for surveys using the Wenner-alpha array indicate that the proposed methodology is best suited to vertical and horizontal contacts. Application of the methodology to real data sets shows that a lateral resistivity contrast of 1:5–1:10 leads to exact faults location. A fault contact with a resistivity contrast of 1:0.75 and overlaid by a resistive layer with a thickness of 1 m gives an error location ranging from 1 to 3 m. Moreover, no result is obtained for a contact with very low contrasts (∼1:0.85) overlaid by a resistive soil. The method shows poor results when vertical gradients are greater than horizontal ones. This kind of image processing technique should be systematically used for improving the objectiveness of tomography interpretation when looking for limits between geological objects.     

Detection of sinkholes using 2D electrical resistivity imaging

Sinkholes in dolomitic areas are notoriously difficult geophysical targets, and selecting an appropriate geophysical solution is not straightforward. Electrical resistivity imaging, or tomography (RESTOM) is well suited to mapping sinkholes because of the ability of the technique for detecting resistive features and discriminating subtle resistivity variations. RESTOM surveys were conducted at two sinkhole sites near Pretoria, South Africa. The survey areas are located in the dolomites of the Lyttelton Formation, which forms part of the Malamani Subgroup and Chuniespoort Group of the Transvaal Supergroup. The survey results suggest that RESTOM is an ideal geophysical tool to aid in the detection and monitoring of sinkholes and other subsurface cavities.     

Three-dimensional forward modeling of DC resistivity using the aggregation-based algebraic multigrid method

To speed up three-dimensional (3D) DC resistivity modeling, we present a new multigrid method, the aggregation-based algebraic multigrid method (AGMG). We first discretize the differential equation of the secondary potential field with mixed boundary conditions by using a seven-point finite-difference method to obtain a large sparse system of linear equations. Then, we introduce the theory behind the pairwise aggregation algorithms for AGMG and use the conjugate-gradient method with the V-cycle AGMG preconditioner (AGMG-CG) to solve the linear equations. We use typical geoelectrical models to test the proposed AGMG-CG method and compare the results with analytical solutions and the 3DDCXH algorithm for 3D DC modeling (3DDCXH). In addition, we apply the AGMG-CG method to different grid sizes and geoelectrical models and compare it to different iterative methods, such as ILU-BICGSTAB, ILU-GCR, and SSOR-CG. The AGMG-CG method yields nearly linearly decreasing errors, whereas the number of iterations increases slowly with increasing grid size. The AGMG-CG method is precise and converges fast, and thus can improve the computational efficiency in forward modeling of three-dimensional DC resistivity.     

Delineating alluvial aquifer heterogeneity using resistivity and GPR data

Conceptual geological models based on geophysical data can elucidate aquifer architecture and heterogeneity at meter and smaller scales, which can lead to better predictions of preferential flow pathways. The macrodispersion experiment (MADE) site, with >2000 measurements of hydraulic conductivity obtained and three tracer tests conducted, serves as an ideal natural laboratory for examining relationships between subsurface flow characteristics and geophysical attributes in fluvial aquifers. The spatial variation of hydraulic conductivity measurements indicates a large degree of site heterogeneity. To evaluate the usefulness of geophysical methods for better delineating fluvial aquifer heterogeneities and distribution of preferential flow paths, a surface grid of two-dimensional ground penetrating radar (GPR) and direct current (DC) resistivity data were collected. A geological model was developed from these data that delineate four stratigraphic units with distinct electrical and radar properties including (from top to bottom) (1) a meandering fluvial system (MFS); (2) a braided fluvial system (BFS); (3) fine-grained sands; and (4) a clay-rich interval. A paleochannel, inferred by other authors to affect flow, was mapped in the MFS with both DC resistivity and GPR data. The channel is 2 to 4 m deep and, based on resistivity values, is predominantly filled with clay and silt. Comparing previously collected hydraulic conductivity measurements and tracer-plume migration patterns to the geological model indicates that flow primarily occurs in the BFS and that the channel mapped in the MFS has no influence on plume migration patterns.     

用相位感应测井数据反演地层电阻率和介电常数

基于相位感应测井的两个接收线圈获取的总场相位差和幅度比曲线,在二维轴对称非均匀介质中同时反演地层电阻率和介电常数. 通过相位差和幅度比测井曲线提取地层纵向边界位置的初始值,利用测井响应方程和变分原理建立反演方程,然后采用共轭梯度(CG)法进行求解. 对多层地层的电阻率、介电常数和纵向边界位置进行整体反演,模拟计算结果表明了方法的可行性.     

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α _k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.     

坑（井）-地电阻率成像技术数据处理方法研究

开发坑（井）一地电阻率成像技术数据处理软件包,对分布式地面一坑道电磁信号接收仪记录的时间序列进行分析处理,最终得到相应的视电阻率和相位参数。实际资料处理结果表明,在远区情况下,需考虑信噪比水平,选择合适的计算方法。当信噪比很低或较高时,可选择根据卡尼亚电阻率定义式直接比值的方法,该方法计算简便、效率高;当信噪比一般时,采用最小二乘或Robust等估算方法,能在一定程度上改善数据处理质量。     

Full waveform inversion and the truncated Newton method: quantitative imaging of complex subsurface structures

Full waveform inversion is a powerful tool for quantitative seismic imaging from wide‐azimuth seismic data. The method is based on the minimization of the misfit between observed and simulated data. This amounts to the solution of a large‐scale nonlinear minimization problem. The inverse Hessian operator plays a crucial role in this reconstruction process. Accounting accurately for the effect of this operator within the minimization scheme should correct for illumination deficits, restore the amplitude of the subsurface parameters, and help to remove artefacts generated by energetic multiple reflections. Conventional minimization methods (nonlinear conjugate gradient, quasi‐Newton methods) only roughly approximate the effect of this operator. In this study, we are interested in the truncated Newton minimization method. These methods are based on the computation of the model update through a matrix‐free conjugate gradient solution of the Newton linear system. We present a feasible implementation of this method for the full waveform inversion problem, based on a second‐order adjoint state formulation for the computation of Hessian‐vector products. We compare this method with conventional methods within the context of 2D acoustic frequency full waveform inversion for the reconstruction of P‐wave velocity models. Two test cases are investigated. The first is the synthetic BP 2004 model, representative of the Gulf of Mexico geology with high velocity contrasts associated with the presence of salt structures. The second is a 2D real data‐set from the Valhall oil field in North sea. Although, from a computational cost point of view, the truncated Newton method appears to be more expensive than conventional optimization algorithms, the results emphasize its increased robustness. A better reconstruction of the P‐wave velocity model is provided when energetic multiple reflections make it difficult to interpret the seismic data. A better trade‐off between regularization and resolution is obtained when noise contamination of the data requires one to regularize the solution of the inverse problem.     

基于IGA算法的电阻率神经网络反演成像研究

为满足地球物理资料反演解释的高精度、快速、稳定的要求,本文结合免疫遗传算法寻优速度快和BP神经网络反演不依赖初始模型等优点,设计了一种将BP神经网络和免疫遗传算法进行有机结合的全局优化反演策略,并将该策略成功地应用于二维高密度电法数据反演.利用免疫遗传算法（Immune Genetic Algorithm,简称IGA）对神经网络的反演参数进行同步优化,提高了电阻率反演的精度.仿真和实验结果验证设计的全局优化反演策略取得了较好的效果,通过与线性反演方法和BP法以及遗传神经网络法等反演方法进行比较,得出该方法具有反演精度更高,反演时间更短等显著优势的结论.     

The effect of subsurface pipes on apparent‐resistivity measurements

Subsurface conducting pipes can be either a target or a noise source in geophysical surveying. Their effect as a noise source in resistivity imaging can be so severe as to render the geophysical data uninterpretable. A method is developed here for identifying, locating and removing the effects of subsurface conducting pipes from image data, thus revealing the background resistivity structure.
A previously known analytic solution for the potential distribution produced by current injection in a uniform half‐space containing an infinitely long conducting cylinder is used to calculate apparent resistivities corresponding to electrode arrays on the surface of the half‐space. Most results concern the Wenner array and an examination is made of the effects produced by varying the electrode spacing and the depth, size and orientation of the pipe with respect to the array. A method is developed for locating pipes in resistivity image data by cross‐correlation of the analytic solution with the measured field data. Pipe effects are then removed by multiplying each datum point in the measurements by the reciprocal of the corresponding value in the analytic solution. The success of the method is demonstrated by applications to synthetic data sets involving one or two pipes embedded in non‐uniform half‐spaces.
In further examples, the method is applied to some measured resistivity images from an ex‐industrial site (a former oil distribution terminal), where an electromagnetic survey had previously revealed a labyrinth of underground pipes. The method is shown to be successful in removing the effects of the pipes to reveal the underlying geology.     

1D inversion of DC resistivity data using a quality-based truncated SVD

Many DC resistivity inversion schemes use a combination of standard iterative least-squares and truncated singular value decomposition (SVD) to optimize the solution to the inverse problem. However, until quite recently, the truncation was done arbitrarily or by a trial-and-error procedure, due to the lack of workable guidance criteria for discarding small singular values. In this paper we present an inversion scheme which adopts a truncation criterion based on the optimization of the total model variance. This consists of two terms: (i) the term associated with the variance of statistically significant principal components, i.e. the standard model estimate variance, and (ii) the term associated with statistically insignificant principal components of the solution, i.e. the variance of the bias term. As an initial model for the start of iterations, we use a multilayered homogeneous half-space whose layer thicknesses increase logarithmically with depth to take into account the decrease of the resolution of the DC resistivity technique with depth. The present inversion scheme has been tested on synthetic and field data. The results of the tests show that the procedure works well and the convergence process is stable even in the most complicated cases. The fact that the truncation level in the SVD is determined intrinsically in the course of inversion proves to be a major advantage over other inversion schemes where it is set by the user.     

Computation of apparent resistivity profiles from VLF-EM data using linear filtering1

A simple filter is developed which transforms VLF-EM real magnetic field transfer functions into apparent resistivities. It is based on the relationship between the horizontal derivative of the surface electric field and the vertical magnetic field at the surface of a two-dimensional earth model. The performance of this simple autoregressive filter is tested for modelled and real survey data. The technique yields profiles of apparent resistivity very similar, both in magnitude and in wavelength, to those which would have been obtained using VLF-EM resistivity measurements or d.c. resistivity profiling. This low-pass filter has the advantage of reducing high-wavenumber noise in the data; therefore only the major features of the VLF-EM profile are displayed.