Detection of soil pipe network by geophysical approach: Electromagnetic induction (EMI) and electrical resistivity tomography (ERT)

Abstract

Studying soil pipes is a methodological challenge that needs improvement in detection methods in order to better recognize the role of piping erosion in land degradation and hillslope hydrology. This study explores electromagnetic induction (EMI) and electrical resistivity tomography (ERT) in order to identify soil pipes. The study was conducted in a mountainous area (the Bieszczady Mountains, SE Poland) under a temperate climate, where pipes develop in silty-clayey soils. In the plot area, eight profiles were measured by the conductivity meter at different depths and then interpolated to present apparent electrical conductivity (EC a ). Also, six ERT profiles were carried out using the Wenner-Schlumberger electrode configuration. The EC a values measured by EMI are not very diversified, suggesting its lower sensitivity to changes in the EC a , whereas the EC a values measured by ERT are characterized by greater fluctuation, that is, better detection possibilities. ERT has revealed soil pipes as zones of higher electrical resistivity (ER >268 Ωm) than their surroundings (characterized below pipes by ER <105 Ωm) underlying the air filling of pipes (ER >427 Ωm), whereas EMI has revealed its higher sensitivity to water content. The EMI results have shown the lowering of the water table in the lower part of the slope, perhaps because of the drainage by a complex pipe network. EMI allows quick measurements of EC a providing information on water content, and thus indirectly soil pipes, but, it cannot delineate individual pipes. Only the integration of geophysical methods supported by field recognition provides an effective method to detect soil pipes.

Revealing subsurface structure at the former municipal solid waste disposal site Leuwi Gajah of Cimahi Town by using geophysical data: a preliminary result

“…According to [15], we obtained magnetic susceptibility ( ) values with equipment in vertical magnetic dipole -VMD -mode using conductivity measurement accomplished in soil surface ( ) and in 1.5 m of height ( ), whose measurement difference is given by: ( ) Moreover, magnetic susceptibility ( ) is expressed by: ( ) Figure 4 A conductivity meter EM38-MK2 coil schematic in vertical (V-mode) and horizontal (H-mode) mode, where Tx -Rx -Transmitter and receiver coil, respectively. [1] The magnetic susceptibility of the topsoil exhibited higher values than that of the subsoil and even exceeded the original parent material's susceptibility. We attributed this enhancement to the soil's prevalence of the clay-size fraction, arising from the transformation of weakly magnetic forms of iron oxides and hydroxides into magnetite.…”

“…In particular, it excels when contrasted with the geoelectric resistivity method due to its non-invasive nature. It is worth noting that the principal soil properties susceptible to detection through EMI encompass water content, solution ion concentrations, and clay content [1]. This repertoire of detected properties underscores the versatility and utility of EMI in soil characterization.…”

“…Electromagnetic induction (EMI) techniques have been pervasive among researchers for various objectives. These applications range from identifying soil pipe networks [1] and characterizing topsoil in reclaimed fields [2] to digitally monitoring and assessing soil conditions [3]. One notable device within the realm of EMI is the EM-38, renowned for its noninvasive nature, which makes it a staple for monitoring salinity, mapping bulk soil properties, and evaluating soil nutrient content [4].…”

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

“…Space-born remote sensing images are most frequently used to obtain features of erosion on large scales with coarse spatial resolution [6][7][8][9]. However, some soil landforms (erosion features) are not as large as others; therefore, they are high-resolution remote Drones 2023, 7, 659 2 of 13 sensing images [10,11].…”

“…Several studies have resulted in fast and accurate outputs when using UAV for erosional feature modeling and mapping in remote and complex regions [32][33][34][35][36][37][38][39]. Although UAV data have recently been used to calculate erosional landforms [8,40], these studies calculated soil losses using three different images. In other words, this study proposed a novel deep-learning approach for calculating soil loss in the Loess Plateau of Iran, where a convolutional neural network was employed for the task of interest.…”