Potential of geoelectrical methods to monitor root zone processes and structure: a review

Understanding the processes that control mass and energy exchanges between soil, plants and the atmosphere plays a critical role for understanding the root zone system, but it is also beneficial for practical applications such as sustainable agriculture and geotechnics. Improved process understanding demands fast, minimally invasive and cost-effective methods of monitoring the shallow subsurface. Geoelectrical monitoring methods fulfil these criteria and have therefore become of increasing interest to soil scientists. Such methods are particularly sensitive to variations in soil moisture and the presence of root material, both of which are essential drivers for processes and mechanisms in soil and root zone systems. This review analyses the recent use of geoelectrical methods in the soil sciences, and highlights their main achievements in focal areas such as estimating hydraulic properties and delineating root architecture. We discuss the specific advantages and limitations of geoelectrical monitoring in this context. Standing out amongst the latter are the non-uniqueness of inverse model solution and the appropriate choice of pedotransfer functions between electrical parameters and soil properties. The relationship between geoelectrical monitoring and alternative characterization methodologies is also examined. Finally, we advocate for future interdisciplinary research combining models of root hydrology and geoelectrical measurements. This includes the development of more appropriate analogue root electrical models, careful separation between different root zone contributors to the electrical response and integrating spatial and temporal geophysical measurements into plant hydrological models to improve the prediction of root zone development and hydraulic parameters

Electrical tomography for characterizing transport properties in cement-based materials: A review

The ability to spatially and temporally quantify the state and distribution of moisture and ions is of central importance to understanding the durability of cement-based materials and structures. Owing to the heterogeneous nature of concrete and challenges associated with using point-based measurements in accomplishing such a task, the use of two- and three-dimensional tomography for quantifying transport properties has become the source of much research interest. Distinct from electromagnetic radiation-based modalities – Electrical Tomography (ET), including Electrical Resistance Tomography, Electrical Impedance Tomography, and Electrical Capacitance Tomography, has emerged as a viable means for characterizing transport in cement-based materials. In this work, we provide a technical overview of ET and the nature of ET inverse problems. We also review historical challenges and successes of ET for imaging transport properties in cement-based materials. Based on realizations from the review, challenges and opportunities afforded by ET for characterizing transport properties are provided and discussed

FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging

Inverse problems are essential to imaging applications. In this paper, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.Comment: 11 pages

Application of acoustic techniques to fluid-particle systems – A review

Acoustic methods applied to opaque systems have attracted the attention of researchers in fluid mechanics. In particular, owing to their ability to characterise in real-time, non-transparent and highly concentrated fluid-particle systems, they have been applied to the study of complex multiphase flows such as fluidised beds. This paper gives an overview of the physical principles and typical challenges of ultrasound and acoustic emission AE methods when applied to fluid-particle systems. The principles of ultrasound imaging are explained first. The measurement techniques and signal processing methodologies for obtaining velocity profiles, size distribution of the dispersed phases, and solid volume fraction are then discussed. The techniques are based on the measurement of attenuation, sound speed, frequency shift, and transit time of the propagated sound wave. A description of the acoustic emission technique and applications to fluid-particle systems are then discussed. Finally, extensions and future opportunities of the acoustic techniques are presented