M.H. Loke

Bio: M.H. Loke is an academic researcher. The author has contributed to research in topics: Bedrock & Terrace (geology). The author has an hindex of 1, co-authored 2 publications receiving 60 citations.

Minimisation of electrode polarisation errors when using optimised or generic resistivity imaging arrays

Abstract: Polarisation potentials are caused when metallic electrodes are used to transmit current in resistivity imaging surveys. If these electrodes are subsequently used to measure potential differences, the decaying polarisation potentials can be a source of significant error. In this paper we describe a general method to minimise the impact of polarisation errors by rearranging the resistivity measurements to maximise the time between any electrode injecting current and later measuring potential. This method does not rely on the existence of a natural ordering of the measurements and can therefore be used with arbitrary resistivity imaging arrays, specifically including those generated by automated optimisation schemes. The method uses a global minimisation algorithm ("simulated annealing") to attempt to avoid local minima without performing an exhaustive search of the configuration space. We determine the control parameters and permutation types for the method from the results of a series of numerical experiments on a randomly generated measurement sequence. We then demonstrate the efficacy of the method using real data measured with a permanently installed resistivity monitoring system on an active landslide. The results show that polarisation errors can be effectively eliminated when using optimised resistivity imaging arrays.

Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.

Abstract: Under the alternating electrical excitation, biological tissues produce a complex electrical impedance which depends on tissue composition, structures, health status, and applied signal frequency, and hence the bioelectrical impedance methods can be utilized for noninvasive tissue characterization. As the impedance responses of these tissue parameters vary with frequencies of the applied signal, the impedance analysis conducted over a wide frequency band provides more information about the tissue interiors which help us to better understand the biological tissues anatomy, physiology, and pathology. Over past few decades, a number of impedance based noninvasive tissue characterization techniques such as bioelectrical impedance analysis (BIA), electrical impedance spectroscopy (EIS), electrical impedance plethysmography (IPG), impedance cardiography (ICG), and electrical impedance tomography (EIT) have been proposed and a lot of research works have been conducted on these methods for noninvasive tissue characterization and disease diagnosis. In this paper BIA, EIS, IPG, ICG, and EIT techniques and their applications in different fields have been reviewed and technical perspective of these impedance methods has been presented. The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.

Deep Learning Inversion of Electrical Resistivity Data

Abstract: The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.

Computation of optimized arrays for 3-D electrical imaging surveys

Abstract: 3-D electrical resistivity surveys and inversion models are required to accurately resolve structures in areas with very complex geology where 2-D models might suffer from artefacts. Many 3-D surveys use a grid where the number of electrodes along one direction (x) is much greater than in the perpendicular direction (y). Frequently, due to limitations in the number of independent electrodes in the multi-electrode system, the surveys use a roll-along system with a small number of parallel survey lines aligned along the x-direction. The ‘Compare R’ array optimization method previously used for 2-D surveys is adapted for such 3-D surveys. Offset versions of the inline arrays used in 2-D surveys are included in the number of possible arrays (the comprehensive data set) to improve the sensitivity to structures in between the lines. The array geometric factor and its relative error are used to filter out potentially unstable arrays in the construction of the comprehensive data set. Comparisons of the conventional (consisting of dipole-dipole and Wenner–Schlumberger arrays) and optimized arrays are made using a synthetic model and experimental measurements in a tank. The tests show that structures located between the lines are better resolved with the optimized arrays. The optimized arrays also have significantly better depth resolution compared to the conventional arrays.