3D FEM simulation of responses of LWD multi-mode resistivity imaging sonde

A new multi-mode resistivity imaging sonde, with toroidal coils as source, can conduct three resistivity measurements: azimuthal resistivity, lateral resistivity, and bit resistivity measurements. Thus, the logging time and cost are greatly saved. The toroidal coils are simplified as an extended voltage dipole and the response equations are derived for a homogenous formation. Based on 3D FEM, the depth of investigation(DOI), vertical resolution, circumferential azimuthal capacity, borehole diameter, mud resistivity, thickness of target formation, and the resistivity of the surrounding formation and mud invasion are simulated. The results suggest that the three measurement modes of the new sonde are different in vertical resolutions and DOIs. The circumferential detection ability of the azimuth button depends on the contrast between the anomaly and formation resistivity and the open angle of the anomaly. Whether the borehole is truncated at the bit or not has a great influence on the simulation results. The borehole and mud invasion affect the apparent resistivity in all modes, but the effects of resistivity of surrounding formation and thickness of the target formation are only corrected for lateral resistivity measurement.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

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.

Application of Electrical Resistivity Imaging in Investigating Groundwater Pollution in Sapele Area, Nigeria

Sixty-four multi-electrode Lund imaging system coupled with ABEM SAS 4000 Terrameter was used for the electrical imaging of the study area. Wenner and Gradient arrays with 2 m minimum electrode spacing were employed which revealed resistivity changes in the vertical and horizontal directions along the survey lines. Earth imager software was employed for?the processing and the iteration of the 2-D resistivity data. The subsurface is characterized with soil material with resistivity ranging from 42 - 15,000 Ohm-m, reflective of varying degree of conductivity associated with changing lithology and fluid type. Correlation with borehole data shows that the first 10 m is composed of laterite. While sand materials occupy 10 to about 60 m beneath the surface, with anomalously high resistivity ≤15,000 Ohm-m in most parts. These high resistivity formations can be attributed to the presence of hydrocarbon within the subsurface, which is an indication that shallow aquifer in the study area has been polluted. The water level in the study area is close to the surface, between 4 - 5 m. As a result of the high resistivity formations in most parts, deep wells of about 45 m are recommended after geophysical investigations.

Multi-Mode Guided Waves Based Reference-Free Damage Diagnostic Imaging in Plates

Probability-based diagnostic imaging(PDI)is one of the most well-known damage identification methods using guided waves.It is usually applied to diagnose damage in plates.The previous studies were dependent on the certain damage index(DI)which is always calculated from the guided wave signals.In conventional methods,DI is simply defined by comparing the real-time data with the baseline data as reference.However,the baseline signal is easily affected by varying environmental conditions of structures.In this paper,a reference-free diagnostic imaging method is developed to avoid the influence of environmental factors,such as temperature and load conditions.The DI is defined based on the mode conversion of multi-mode guided waves with realtime signals without baseline signals.To improve the accuracy of diagnosis,two terms are included in the reference-free DI.One is called energy DI,which is defined based on the feature of signal energy.The other is called correlation DI and is defined based on the correlation coefficient.Then the PDI algorithm can be carried out instantaneously according to the reference-free DI.The real-time signals which are used to calculate DI are collected by the piezoelectric lead zirconate titanate(PZT)transducers placed on both sides of a plate.The numerical simulations by the finite element(FE)method on aluminum plates with PZT arrays are performed to validate the effectiveness of the reference-free damage diagnostic imaging.The approach is validated by two different arrays:a circle network and a square network.The results of diagnostic imaging are demonstrated and discussed in this paper.Furthermore,the advantage of reference-free DI is investigated by comparing the accuracy of defined reference-free DI and energy DI.

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.

2D Electrical Resistivity Imaging for Delineation of Crystalline Basement Aquifer in Northern Ghana

Lack of access to potable and adequate water is a major problem for sustainable development in northern Ghana. Developing groundwater resource is the best option for safe, reliable, and cost-efficient water supplies to these dispersed communities. In this study, nine 2D ERI profiles were carried out with the Schlumberger array in eight communities underlain by the crystalline basement rocks in the Bole District of the Savannah Region of Ghana. The aim was to delineate the aquifer zones and select points for groundwater extraction. Nine boreholes were drilled from the selected points. The yield was found to vary from 12 to 180 l/min with a depth range of 50 to 70 m. The weathered and fractured zones together with the bedrock topography were clearly marked. It is evident that the 2D electrical resistivity technique is useful tool in determining the availability of groundwater in weathered and fractured crystalline environment.

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.

Numerical simulation research on multi-electrodes resistivity imaging survey array

Multi-electrodes Resistivity Imaging Survey(MRIS)is an array method of electrical survey.In practice how to choose a reasonable array is the key to get reliable survey results.Based on four methods of MRIS such as Wenner,Schlumberger,Pole-pole and Dipole-dipole the authors established the model,by studying the result of the forward numerical simulation modeling and inverse modeling,and analyzed the differences among the different forms of detection devices.

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease.

Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass

Glyphosate is widely used in row crop weed control programs of glyphosate-resistant (GR) crops. With the accumulation of glyphosate use, several weeds have evolved resistance to glyphosate. In order to control GR weeds for profitable crop production, it is critical to first identify them in crop fields. Conventional method for identifying GR weeds is destructive, tedious and labor-intensive. This study developed hyperspectral imaging for rapid sensing of Italian ryegrass (Lolium perenne ssp. multiflorum) plants to determine if each plant is GR or glyphosate-susceptible (GS). In image analysis, a set of sensitive spectral bands was determined using a forward selection algorithm by optimizing the area under the receiver operating characteristic between GR and GS plants. Then, the dimensionality of selected bands was reduced using linear discriminant analysis. At the end the maximum likelihood classification was conducted for plant sample differentiation of GR Italian ryegrass from GS ones. The results indicated that the overall classification accuracy is between 75% and 80%. Although the accuracy is lower than the classification of Palmer amaranth (Amaranthus palmeri S. Wats.) in our previous study, this study provides a rapid, non-destructive approach to differentiate between GR and GS Italian ryegrass for improved site-specific weed management.

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.

Performance Assessment on Corrosion Resistance of Refractory Materials Based on High-temperature Machine Vision Technology

Refractory materials,as the crucial foundational materials in high-temperature industrial processes such as metallurgy and construction,are inevitably subjected to corrosion and penetration from high-temperature media during their service.Traditionally,observing the in-situ degradation process of refractory materials in complex high-temperature environments has presented challenges.Post-corrosion analysis are commonly employed to assess the slag resistance of refractory materials and understand the corrosion mechanisms.However,these methods often lack information on the process under the conditions of thermal-chemical-mechanical coupling,leading to potential biases in the analysis results.In this work,we developed a non-contact high-temperature machine vision technology by the integrating Digital Image Correlation(DIC)with a high-temperature visualization system to explore the corrosion behavior of Al2O3-SiO2 refractories against molten glass and Al2O3-MgO dry ramming refractories against molten slag at different temperatures.This technology enables realtime monitoring of the 2D or 3D overall strain and average strain curves of the refractory materials and provides continuous feedback on the progressive corrosion of the materials under the coupling conditions of thermal,chemical,and mechanical factors.Therefore,it is an innovative approach for evaluating the service behavior and performance of refractory materials,and is expected to promote the digitization and intelligence of the refractory industry,contributing to the optimization and upgrading of product performance.

Functional imaging and endoscopy

The emergence of endoscopy for the diagnosis of gastrointestinal diseases and the treatment of gastrointestinal diseases has brought great changes.The mere observation of anatomy with the imaging mode using modern endoscopy has played a significant role in this regard.However,increasing numbers of endoscopies have exposed additional deficiencies and defects such as anatomically similar diseases.Endoscopy can be used to examine lesions that are difficult to identify and diagnose.Early disease detection requires that substantive changes in biological function should be observed,but in the absence of marked morphological changes,endoscopic detection and diagnosis are difficult.Disease detection requires not only anatomic but also functional imaging to achieve a comprehensive interpretation and understanding.Therefore,we must ask if endoscopic examination can be integrated with both anatomic imaging and functional imaging.In recent years,as molecular biology and medical imaging technology have further developed,more functional imaging methods have emerged.This paper is a review of the literature related to endoscopic optical imaging methods in the hopes of initiating integration of functional imaging and anatomical imaging to yield a new and more effective type of endoscopy.

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.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

The application of resistivity tomography to hydrogeological exploration

Resistivity tomography, an advanced technique reconstructing resistivity image from a series of potential survey of electrode arrays is quite different from the non linear inversion of wave problems. The main problem in the resistivity reconstruction is how to solve the Poisson′s equation of direct current fields in voluminous media and to complete the inversion iteration efficiently. A mathematical idea of cascade algorithm proposed by Shima (1992), as an example, is introduced briefly in this paper. The emergence of water flood in tunnels in Wennan Coal Mine, Shandong Province, gave us chances to carry out hydrogeological exploration twice using resistivity tomography in 1995～1996. Three profiles with a total length of 5832 m and a maximum depth of 120 m in tomograms are completed. The series of resulting tomograms distinctly reveal the distributions of stratigraphic structure, mined out areas, fracture zones, crack belts and piping water loss zones. These results from tomograms are verified by drill records and then successfully adopted in the followed hydrogeological engineering in the coal mine. Finally, the authors discussed the technical method and existing problems in resistivity tomography.

Imaging technologies in oilfield applications

In this paper, we describe some recent imaging technologies developed by Schlumberger for oilfield downhole mul- tiphase flow production logging (PL) and cross-well electromagnetic (EM) survey applications. FloScan Imager (FSITM) has been introduced as a 3-phase oil/gas/water flow PL tool for deviated and horizontal wells. FSI sensors can map fluid velocity and holdup profiles along a vertical diameter of the wellbore at every survey depth, enabling a robust estimate of the individual phase flow rates in complex flow regimes. The cross-well EM survey is based on cross-borehole induction logging technique and provides resistivity distribution at a reservoir scale. It is a useful tool for reservoir management and is most effective in dynamic fields where fluid saturations are variable in time and space. The tool can be used to identify (water or steam) flooded and bypassed regions. By monitoring changes in the resistivity spatial distribution with time, cross-well EM survey is very effective at mapping inter-well temperature and structure. Some field examples are shown for both FloScan Imager PL tool and cross-well resistivity imaging survey.

Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm

A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.

Geophysical Investigation of a Solid Waste Disposal Site Using Integrated Electrical Resistivity Tomography and Multichannel Analyses of Surface Waves Methods

Electrical resistivity tomography survey was deployed at a solid waste landfill in southwest Missouri USA with the intent to map variations in moisture content through the solid waste and underlying subsurface, and to map the top of bedrock. Multichannel analyses of surface waves survey was also deployed to map variations in engineering properties of the solid waste and underlying subsurface, and to constrain the interpretations of top of bedrock. The 2-D resistivity images through the waste suggest rainwater seeps through the cap cover system of the solid waste landfill, and moisture content within the solid waste increases with solid waste burial depth. The resistivity anomalies displayed by the soil and bedrock directly underneath the solid waste suggests a lateral component to moisture infiltrating at the toe of the landfill, which is flowing inward to the base of solid waste structural low. The 1-D shear wave velocity profiles obtained from the multichannel analyses of surface waves survey helped interpret the top of bedrock underneath the solid waste, where top of bedrock is difficult to map using electrical resistivity tomography, as shallow fractured bedrock is moist and displays comparable resistivity values to that of overlying soil. Not surprisingly, the top of bedrock is readily identified on the electrical resistivity tomography profiles in places where subsurface is relatively dry. The deployment of the combined non- invasive, cost and time effective geophysical surveys, along with engineering judgement on available site history data, has reasonably identified potential landfill seepage pathways. The methodology presented could be used in similar site investigation settings.