AQUIFER CHARACTERIZATION USING ELECTRICAL RESISTIVITY TOMOGRAPHY AND SURFACE NUCLEAR MAGNETIC RESONANCE

Abstract

The groundwater resources serve as a major source of drinking water, and in some places in India, it is also a source of irrigation. Thus, monitoring groundwater is crucial for sustainable exploitation and development. Traditional methods for hydrogeological monitoring consisted of limited measurement points, and on the other hand, installation cost was quite expensive. Our research problem is the seasonal monitoring of hydrogeological changes in the study region. The introduction of field geophysical methods such as electrical resistivity tomography (ERT) and surface nuclear magnetic resonance (surface NMR) allows subsurface monitoring and mapping. The measured data can be interpolated or extrapolated according to the site-specific requirement. It was observed that the electrical resistivity was highly sensitive for soil properties such as water content, clay content, temperature, and solute concentration. Daily (1992) found ERT was useful for resistivity mapping inside the rock core under temperature and high pressure. The electrical resistivity is highly influenced by soil moisture, temperature, and the ionic concentration of subsurface solutes. Thus, time-lapse investigations were crucial in near-surface research because they included dynamic features related to the Earth-air interface. Time-lapse ERT monitoring has significant advantages over the single and static study, such as it provides dynamic changes in the subsurface properties apart from the static properties. So, time-lapse ERT is helpful to understand ongoing subsurface processes. Miller et al. (2008) investigated the seasonal fluctuation in the subsurface water content using the combination of the time-lapse ERT images, ERT inversions, and available hydrologic data in the Idaho watershed in the United States. Earth resistivity meter is modern equipment in which several electrodes can be installed a maximum of up to 100 electrodes with 5m electrode spacing. Several array configurations were used to collect the survey data. Electrical resistivity tomography (ERT) survey depends on time availability and other site situations, so data collection by each electrode array configuration can't be practically possible. Therefore, an effective and efficient ERT survey requires a quantitative method in which ERT arrays form useful an ERT survey. It is hypothetical to say that quantitative approach for determination of optimal exploration through ERT survey so that ERT survey can be set up based on spatial sensitivity of ERT arrays. The general concept for the ERT survey is the combination of electrode array configurations with different electrode spacing and depth of subsurface exploration ii (penetration depth) which depends on electrode spacing. Penetration depth increases with an increase in electrode spacing. Four electrodes are arranged in such a way that Wenner array that spacing between the electrodes is the same; two electrodes are used for current injection into the subsurface, and the remaining two measure the potential difference (Wenner 1916). A similar procedure is applied for other arrays; however, the spacing between the electrodes may be different. A set of several electrodes are used in a multi-electrode ERT survey; half of the electrodes are inserting the current into the subsurface, and the remaining half of them are measuring the potential difference or resistance between the electrodes. Schlumberger et al. (1932) were introduced ERT surveys; after that, many researchers suggested different array configurations for the earth subsurface. Fourier transform infrared (FTIR) technique generates the soil spectrum and the organic portion of the infrared spectrum. X-ray diffraction (XRD) analysis was carried out for quantification of soil minerals and chemical elements in the soil sample by X-ray fluorescence (XRF), and texture of soil particles was analyzed by scanning electron microscope (SEM). Quantitative analysis of soil samples shows quartz, potassium oxide, magnesium oxide, aluminum oxide, iron oxide as major minerals in the study area. In the last two decades, there has been increased interest in the combined use of geophysical approaches to estimate the aquifer's hydraulic parameters. The time-lapse magnetic resonance sounding (MRS) analysis discovered that the MRS signals were closely connected to the temporal variations in subsurface water content. On the other hand, this study was combined with time-lapse relative gravimetry and compared the time-lapse inversion results to pumping well data. Vouillamoz et al. (2014) suggested that the complexity of the soil can affect the measurement of the MRS signals and the calibration of the MRS data. They established this statement, such as the MRS water content can be calibrated under the saturated aquifer pumping condition. Electrical resistivity tomography and surface NMR are geophysical methods for classifying subsurface soil media up to 100m below the soil's surface. The main focus of the present study is to estimate the seasonal changes in the subsurface aquifer properties like water content and hydraulic iii conductivity using the combination of 2D/3D ERT and surface NMR methods, and these results were compared with the pulse moment time inversion (QTI) scheme. It was observed that small-scale variability in aquifer properties was found in the ERT and surface inversions. However, these models were developed for the field scale. Thus, small-scale variability can be neglected. During the post-monsoon and monsoon seasons, the size of low resistivity (4Ohm-m to14Ohm-m) and very low resistivity 23Ohm-m to 33Ohm-m) zones in the study area. Similarly, surface NMR measurements revealed a significant increase in the water content during these seasons. The chi-square value for the QTI scheme was 1.0672, which was quite near to 1. As a result, the QTI model is considered to be accurate for this study. The subsurface soil samples particle size distribution curves were studied, and soil components were classified using the transverse relaxation time. Thus, the combination of ERT and surface NMR approach promises to be a powerful tool for estimating subsurface characterization. Eight different empirical equations were used in the study, such as Kozeny-Carman, Terzhagi, Hazen, etc., and estimated hydraulic conductivities compared with the observed saturated surface NMR hydraulic conductivity. These empirical equations were used to estimate saturated hydraulic conductivity from soil particle size distribution and surface NMR porosity. It was found that most of the empirical equations showed poor correlation with the surface NMR hydraulic conductivity except Kozeny-Carman, Hazen, and Zamarin equations. These estimated hydraulic conductivities were compared with the surface NMR hydraulic conductivity. To improve the performance accuracy of each empirical equation beta coefficient was modified to obtain the linear equation with unit slope. The evaluation of uncertainty in hydrologic parameters was deemed important when producing data that could be used in broader hydrologic or hydrogeologic contexts. These surface NMR data sets are used to compare the direct traditional hydrologic or hydrogeologic measurements. Müller-Petke et al. (2011) studied surface NMR, which exploits the nuclear magnetic resonance (NMR) phenomenon. Surface coils are used to estimate aquifer properties such as hydraulic conductivity, water content, and so on. The signals should be measured in a well-defined environment, with minimal electromagnetic and environmental noise. The uncertainty analysis of the signal data sets can be used to assess the accuracy of the measured NMR signals and forward modeling. The bootstrap statistics assess the statistical accuracy of complex data sets such as NMR signals, time series, regression models, and so on. Sacchi (1998) used the bootstrap approach to measure average iv coherence and estimate the Kernel density that maximizes coherence. Xia et al. (2008) used the bootstrap method to estimate the uncertainty in the true and mean value of the dynamic measurement. The uncertainty was calculated without any prior information about the distribution of random variables. The uncertainty analysis of the synthetic surface NMR data sets at different noise levels, and seasonal field surface NMR data sets at ambient noise levels. The surface NMR data sets were collected at realistic noise levels during the pre-monsoon and post-monsoon seasons. The seasonal data sets' uncertainty in subsurface water content and relaxation time profiles were assessed. We used an open-source algorithm and inversion routines (Müller-Petke et al. 2016) to estimate uncertainty using only surface NMR signals. The main objective of this study is to demonstrate bootstrap resampling at various known noise levels (i.e., 5nV, 15nV, 30nV, and 50nV) in synthetic data sets for surface NMR and illustrate the bootstrap uncertainty for seasonal field surface NMR data sets. The top 4m aquifer depth 2D/3D ERT inversion model revealed a high resistivity material. The aquifer revealed extremely low and low resistivity between 4-14m depth, and this aquifer zone has a mixture of sand and clay with a high-water content. Beyond 4m deep, the aquifer demonstrated moderate resistivity, and this zone contains the same aquifer material but with a lower water content. The time-lapse ERT investigation revealed a zone of very low and low resistivity in the aquifer that rose from pre-monsoon to monsoon. The ERT survey showed the lowest resistivity among the other ERT measurement during the post-monsoon season. The water content below the water table remained roughly same throughout the year, and with increase in the aquifer depth, the resolution of the surface NMR data decreases. The QT inversion is regarded accurate if the chi-square is near to 1, while the optimal range of the chi-square is between 0.9 to 1.1. The present study showed the chi-square 1.0672, which is near to 1. The bootstrapping without replacement approach is the simplest and most effective strategy to quantify the uncertainty in the water content of the surface NMR data.