Three-dimensional forward modeling and inversion of borehole-to-surface electrical imaging with different power sources

Borehole-to-surface electrical imaging （BSEI） uses a line source and a point source to generate a stable electric field in the ground. In order to study the surface potential of anomalies, three-dimensional forward modeling of point and line sources was conducted by using the finite-difference method and the incomplete Cholesky conjugate gradient （ICCG） method. Then, the damping least square method was used in the 3D inversion of the formation resistivity data. Several geological models were considered in the forward modeling and inversion. The forward modeling results suggest that the potentials generated by the two sources have different surface signatures. The inversion data suggest that the low- resistivity anomaly is outlined better than the high-resistivity anomaly. Moreover, when the point source is under the anomaly, the resistivity anomaly boundaries are better outlined than when using a line source.

Groundwater Exploration Using 2-D Geoelectrical Resistivity Imaging Technique at Sungai. Udang, Melaka

Electrical resistivity imaging surveys have been conducted in order to locate, delineate subsurface water resource and estimate its reserve. The resistivity imaging surveys carried out basically measure and map the resistivity of subsurface materials. Electrical imaging is an appropriate survey technique for areas with complex geology where the use of resistivity sounding and other techniques are unsuitable to provide detailed subsurface information. The purpose of electrical surveys is to determine the subsurface resistivity distribution by making measurements on the ground surface. The resistivity imaging measurement employing Wenner electrode configuration was carried out using an ABEM SAS 1000 terrameter and electrode selector system ES464. The field survey was conducted along four profiles which provide a continuous coverage of the resistivity imaging below surface. The surface soil material is mainly clayey silt. The results showed that the layers associated with the low resistivities （Ωm） are located at depth ranging from 2 m to 28 m. This low resistivity values are associated with zone of water saturated weathered layer and fractures. The results showed that the thickness of residual soil is about 0.5-2.55 m. Borehole data indicated that the depth of bedrock is about 10 m and the groundwater level is ranging from 8.73 m to 8.54 m.

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 αk, 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.

An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging

To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.

Groundwater Potential Mapping in Lapan Gwari Community Using Integrated Remote Sensing and Electrical Resistivity Soundings

This research aims to address the pressing issue of failed and abandoned wells, causing water scarcity in Lapan Gwari Community, through an improved groundwater exploration approach integrating remote sensing and electrical resistivity soundings. The study area, located within the Zungeru Sheet 163 SE, spans Latitudes 9°30'00"N to 9°32'00"N and Longitudes 6°28'00" to 6°30'00". The surface geologic, structural, and hydrogeological mapping provided essential insights into the hydrogeological framework. Leveraging SRTM DEM data, thematic maps were created for geomorphology, slope, land use, lineament density, and drainage density. These datasets were then integrated using ArcGIS to develop a preliminary groundwater potential zones map. Further investigations were conducted using Vertical Electrical Sounding (VES) and Electrical Resistivity Imaging (2D VES) surveys at targeted locations identified by the preliminary map. Results show that the study area predominantly consists of crystalline rocks of the Nigerian Basement Complex, primarily comprising schist and granite with minor occurrences of quartz vein intrusions. Surface joint directions indicated a dominant NE-SW trend. The VES data revealed three to four geoelectric layers, encompassing the topsoil (1 to 5 m depth, resistivity: 100 Ωm to 300 Ωm), the weathered layer (in the 3-layer system) or fractured layer (in the 4-layer system), and the fresh basement rock characterized by infinite resistivity. The shallow weathered layers (3 to 30 m thickness) are believed to hold aquiferous potential. Hydrogeological interpretation, facilitated by 2D resistivity models, delineated water horizons trapped within clayey sand and weathered/fractured formations. Notably, the aquifer resistivity range was found to be between 3 - 35 m and 100 - 300 Ωm, signifying a promising aquifer positioned at depths of 40 to 88 m. This aligns with corroborative static water level measurements. Given this, we recommend drilling depths of a minimum of 80 m to ensure the acquisition of sufficient and sustainable water supplies. The final groundwater potential zones map derived from this study is expected to serve as an invaluable guide for prospective groundwater developers and relevant authorities in formulating effective water resource management plans. By effectively tackling water scarcity challenges in Lapan Gwari Community, this integrated approach demonstrates its potential for application in similar regions facing comparable hydrogeological concerns.

Relationship between Natural Fracture and Structural Style and its Implication for Tight Gas Enrichment:A Case Study of Deep Ahe Formation in the Dibei–Tuzi Area,Kuqa Depression

The deep Lower Jurassic Ahe Formation(J_(1a))in the Dibei–Tuzi area of the Kuqa Depression has not been extensively explored because of the complex distribution of fractures.A study was conducted to investigate the relationship between the natural fracture distribution and structural style.The J_(1a)fractures in this area were mainly high-angle shear fractures.A backward thrust structure(BTS)is favorable for gas migration and accumulation,probably because natural fractures are more developed in the middle and upper parts of a thick competent layer.The opposing thrust structure(OTS)was strongly compressed,and the natural fractures in the middle and lower parts of the thick competent layer around the fault were more intense.The vertical fracture distribution in the thick competent layers of an imbricate-thrust structure(ITS)differs from that of BTS and OTS.The intensity of the fractures in the ITS anticline is similar to that in the BTS.Fracture density in monoclinic strata in a ITS is controlled by faulting.Overall,the structural style controls the configuration of faults and anticlines,and the stress on the competent layers,which significantly affects deep gas reservoir fractures.The enrichment of deep tight sandstone gas is likely controlled by two closely spaced faults and a fault-related anticline.

Automatic fracture–vug identification and extraction from electric imaging logging data based on path morphology

We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions,including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.

Self-adaptively inpainting gaps between electrical image pads according to the image fabric

Images created from measurements made by wireline microresistivity imaging tools have longitudinal gaps when the well circumference exceeds the total width of the pad-mounted electrode arrays.The gap size depends on the tool design and borehole size,and the null data in these gaps negatively aff ect the quantitative evaluation of reservoirs.Images with linear and texture features obtained from microresistivity image logs have distinct dual fabric features because of logging principles and various geological phenomena.Linear image features usually include phenomena such as fractures,bedding,and unconformities.Contrarily,texture-based image features usually indicate phenomena such as vugs and rock matrices.According to the characteristics of this fabric-based binary image structure and guided by the practice of geological interpretation,an adaptive inpainting method for the blank gaps in microresistivity image logs is proposed.For images with linear features,a sinusoidal tracking inpainting algorithm based on an evaluation of the validity and continuity of pixel sets is used.Contrarily,the most similar target transplantation algorithm is applied to texture-based images.The results obtained for measured electrical imaging data showed that the full borehole image obtained by the proposed method,whether it was a linear structural image refl ecting fracture and bedding or texture-based image refl ecting the matrix and pore of rock,had substantially good inpainting quality with enhanced visual connectivity.The proposed method was eff ective for inpainting electrical image logs with large gaps and high angle fractures with high heterogeneity.Moreover,ladder and block artifacts were rare,and the inpainting marks were not obvious.In addition,detailed full borehole images obtained by the proposed method will provide an essential basis for interpreting geological phenomena and reservoir parameters.

Logging identification method of depositional facies in Sinian Dengying Formation of the Sichuan Basin

The sedimentary facies/microfacies,which can be correlated with well logs,determine reservoir quality and hydrocarbon productivity in carbonate rocks.The identification and evaluation of sedimentary facies/microfacies using well logs are very important in order to effectively guide the exploration and development of oil and gas.Previous carbonate facies/microfacies identification methods based on conventional well log data often exist multiple solutions.This paper presents a new method of facies/microfacies identification based on core-conventional logs-electrical image log-geological model,and the method is applied in the fourth member of the Dengying Formation(Deng 4)in the Gaoshiti-Moxi area of the Sichuan Basin.Firstly,core data are used to calibrate different types of facies/microfacies,with the aim to systematically clarify the conventional and electrical image log responses for each type of facies/microfacies.Secondly,through the pair wise correlation analysis of conventional logs,GR,RT and CNL,are selected as sensitive curves to establish the microfacies discrimination criteria separately.Thirdly,five well logging response models and identification charts of facies/microfacies are established based on electrical image log.The sedimentary microfacies of 60 exploratory wells was analyzed individually through this method,and the microfacies maps of 4 layers of the Deng 4 Member were compiled,and the plane distribution of microfacies in the Gaoshiti-Moxi area of the Sichuan Basin was depicted.The comparative analysis of oil testing or production results of wells reveals three most favorable types of microfacies and they include algal psammitic shoal,algal agglutinate mound,and algal stromatolite mound,which provide a reliable technical support to the exploration,development and well deployment in the study area.

Forward and inverse problem for cardiac magnetic field and electric potential using two boundary element methods

This paper discusses the forward and inverse problem for cardiac magnetic fields and electric potentials. A torso-heart model established by boundary element method （BEM） is used for studying the distributions of cardiac magnetic fields and electric potentials. Because node-to-node and triangle-to-triangle BEM can lead to discrepant field distributions, their properties and influences are compared. Then based on constructed torso-heart model and supposed current source functional model-current dipole array, the magnetic and electric imaging by optimal constrained linear inverse method are applied at the same time. Through figure and reconstructing parameter comparison, though the magnetic current dipole array imaging possesses better reconstructing effect, however node-to-node BEM and triangleto-triangle BEM make little difference to magnetic and electric imaging.

An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].

High-speed 3D imaging based on structured illumination and electrically tunable lens

In this Letter, we present a high-speed volumetric imaging system based on structured illumination and an electrically tunable lens（ETL）, where the ETL performs fast axial scanning at hundreds of Hz. In the system,a digital micro-mirror device（DMD） is utilized to rapidly generate structured images at the focal plane in synchronization with the axial scanning unit. The scanning characteristics of the ETL are investigated theoretically and experimentally. Imaging experiments on pollen samples are performed to verify the optical cross-sectioning and fast axial scanning capabilities. The results show that our system can perform fast axial scanning and threedimensional（3D） imaging when paired with a high-speed camera, presenting an economic solution for advanced biological imaging applications.

Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

Fusion of ERT Images Based on Dempster-Shafer's Evidence Theory

In this paper, an electrical resistance tomography(ERT) imaging method is used as a classifier, and then the Dempster-Shafer's evidence theory with fuzzy clustering is integrated to improve the ERT image quality. The fuzzy clustering is applied to determining the key mass function, and dealing with the uncertain, incomplete and inconsistent measured imaging data in ERT. The proposed method was applied to images with the same investigated object under eight typical current drive patterns. Experiments were performed on a group of simulations using COMSOL Multiphysics tool and measurements with a piece of porcine lung and a pair of porcine kidneys as test materials. Compared with any single drive pattern, the proposed method can provide images with a spatial resolution of about 10% higher, while the time resolution was almost the same.