Recent developments in the direct-current geoelectrical imaging method

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.

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Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.

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.

Deep Learning Inversion of Electrical Resistivity Data

Bedrock detection beneath river terrace deposits using three-dimensional electrical resistivity tomography

River terrace sand and gravel deposit reserve estimation using three-dimensional electrical resistivity tomography for bedrock surface detection

Quality assessment of onshoresand and gravel deposits must be low-cost and efficient to be economically attractive. This paper reports initial results of a thorough comparison of assessment methods for such deposits, by comparing results from geophysical surveys and exploration drilling with a variety of drilling methods, with ground truth obtained by direct sampling of the deposits as the working face of the quarry moved through the areas of study. Here, the results of the geophysical assessment of the deposits uses resistivity and EM methods. While immedaite results are practically useful to the industry, further development of 3D inversion, and petrophysical models are needed to fully exploit the data quality from current survey instruments.

Geophysical Quality Assessment of Sand and Gravel Deposits

This paper was published as Mineral Industry Sustainable Technology Programme (MIST), Final Reports, 2004-2007, MA/4/2/002, pp. 39-42, Impact Mitigation and Management. It is available from http://www.sustainableaggregates.com/rprts_revs/rr_search.htm

C.A. Jeffrey

Papers

Bedrock detection beneath river terrace deposits using three-dimensional electrical resistivity tomography

Geophysical Quality Assessment of Sand and Gravel Deposits

WARM-IT Waste minimisation by the application of integrated technology