In-Ky Cho

Application of a streamer resistivity survey in a shallow brackish-water reservoir

To delineate the resistivity structure of sub-bottom sediments in a shallow brackish-water reservoir in the western coastal area of Korea, we carried out a streamer resistivity survey using a dipole–dipole array. First, through numerical testing, we confirmed that the resistivity method with a dipole–dipole array could be applied in a shallow marine environment, when the resistivity contrast between water and the underlying sediments ranges from a factor of 3 to 5. Also, inversion with a water layer explicitly included is more effective than the conventional inversion method in resolving power, which we confirmed by observing that the inversion results for synthetic datasets matched better when a water layer was included in the inversion procedure. After constructing a data acquisition system composed of a resistivity meter, GPS, and echo sounder, and developing data processing software, we conducted a streamer resistivity survey and inverted the data obtained to identify the hydrogeological sequences and sediment characteristics at the bottom of the shallow brackish-water reservoir. Drill logs identified three sediment layers, including silty sand, fine sand, and mixed sand. The resistivity distributions from inversion matched the resistivity ranges measured on materials obtained by sampling near the drilling points. We constructed a contour map of the top of the mixed-sand layer, using semivariogram analysis. Comparing these results with the drilling results, the depth to each layer, and the measured and estimated resistivity range of the materials, also corresponded to resistivity profile. From this study, we are assured that the streamer resistivity method would be a useful tool for surveying shallow brackish-water reservoirs.

SP Monitoring at a Sea Dike

The self-potential (SP) method is widely used in seepage evaluation hydrological studies to monitor the integrity of infrastructure such as dams, sea dikes, and other types of flood control devices because the electric signals that are measured are directly related to seepage rate. At leaking areas along sea dikes, large SP anomalies can be generated by the rising and falling of tides. Unfortunately, SP data are often contaminated with several types of noise, such as that from drifting electrodes, telluric dis- turbances, and external electrical noise. Furthermore, SP signals can have high levels of spatial vari- ability due to heterogeneity in lateral resistivity at the locations where the electrodes are installed. Because of these issues, it is very difficult to correlate the measured SP voltages with the streaming potentials associated with groundwater flows at particular points in time. To alleviate these problems, we developed a simple but effective interpretation method for SP monitoring data that involves sub- tracting consecutive SP voltages collected at different time points from a particular monitoring station. This subtracting procedure is able to effectively reduce spurious SP anomalies caused by electrode drift, change in resistivity, and other types of interference. Therefore, any changes observed in SP measurements over certain time frames were interpreted as resulting primarily from temporal changes in seepage flow. To demonstrate the performance of this method, we analysed SP monitoring data measured at a sea dike located on the southern coast of Korea. Our results confirmed that the SP interpretation method is able to explain changes in streaming potentials depending on the tide change over time and to detect the horizontal location of anomalous seepage zones along the sea dike.

3D effects on 2D resistivity monitoring in earth-fill dams

Measuring resistivity is a potentially powerful method of monitoring leakage zones that have developed in a dam, and their expansion over time. Generally, for embankment dams, two-dimensional (2D) resistivity data have been measured along the dam crest for the detection of leakage zones. However, the three-dimensional (3D) effects created by specific dam geometry and fluctuations in reservoir water levels significantly distort the 2D resistivity data measured at the dam crest. This study evaluates the 3D effects through 3D resistivity modelling software, which was developed to calculate apparent resistivity data for geometries and material distributions for embankment dams. These modelling results demonstrated that the 3D effect from the dam geometry and variations in water level is significant. Especially, in the case of monitoring, changes in 3D effects from water level fluctuations cause a spurious near-surface layer when time-lapse inversion is applied with a cross-model constraint. To overcome this problem, we introduced a combined reference model constructed from the independent inversion of both time-lapse data and original reference data. The combined reference model was able to effectively suppress the spurious near-surface layer and to clearly image the damaged zone when the change in water level was small. However, a time-lapse inversion using the combined reference model also failed to identify the damaged zone when the change in water level was large. Finally, by using the resistivity monitoring system devised for dam surveillance to a test dam site, resistivity monitoring data were acquired. From the time-lapse inversion of two data sets showing a large change in water level between two measurements, it was confirmed that the variation of water levels produces the occurrence of a spurious near-surface layer due to a strong 3D effect.

Time-lapse Inversion of 3D Resistivity Monitoring Data

We developed a time-lapse inversion using new cross-model constraints based on change ratio and resolution of model parameters. The cross-model constraint based on change ratio imposes the same penalty on the model parameters with equal change ratio. This constraint can emphasize the model parameters with significant change regardless of their increase or decrease. The resolution cross-model constraint imposes a small penalty on the model parameters with poor resolution, but a large penalty on the model parameters with good resolution. Thus, the model parameter with poor resolution can be effectively identified in the inversion result if they are significantly changed with time. Through the numerical tests for 3D resistivity monitoring data sets, the performance of these two cross-model constraints was confirmed. Finally, for the safety estimation of a sea dyke, we applied the developed time-lapse inversion to the 3D resistivity monitoring data that were acquired at a sea dike located in western coastal area of Korea. The result of timelapse inversion suggested that there were no significant changes at the sea dike during the monitoring period.

Suggestion for the Maintenance Program of the Sea Dike Using Geophysical Methods

The sea dike is the most important facility of reclamation projects, and plays an important role in securing freshwater in the reservoir. Systematic research on practical approaches and data analysis techniques are lacking even though some geophysical methods such as electrical resistivity and self-potential surveys are included within the inspection processes. Hence, geophysical methods were considered for improvement of precision safety diagnosis methods after problems in the maintenance system have been identified, such as safety checks and precision safety diagnoses. In addition, geophysical methods customized according to variations in ambient environmental limiting factors such as pore pressure changes by tidal fluctuation, compaction characteristics of the fill materials, and the surface condition of the embankment were suggested.

Resistivity Survey Using Long Electrodes

Generally, a point source has been routinely used in the electrical resistivity measurements because of easy installation. If steel-cased wells are used as long electrodes, we can expect the better depth of investigation. However, the resistivity data with long electrodes can not be processed with a conventional inversion algorithm because a long electrode produces the different primary potential distribution compared with the point source. In this study, we proposed a new technique to process the electrical resistivity data with long electrodes by replacing the long electrode with a sequence of point electrodes. Comparing the potentials obtained from the technique with the analytic/numerical solution, we ensure that the proposed technique can be used for the numerical resistivity modeling based on the finite difference or finite element method.

Inversion of Resistivity Data using Data-weighting

요약: 전기비저항 탐사 자료는 다양한 잡음을 포함하고 있다. 즉 전기비저항 자료는 높은 접촉저항, 장비의 측정 오차및 주변의 불규칙한 전기적 잡음에 의해 영향을 받는다. 전기비저항 탐사 자료의 올바른 해석을 위해서는 이들 잡음의정확한 추정이 요구된다. 이 연구에서는 상반성 시험을 통하여 추정된 잡음을 역산시 자료 가중에 반영하는 방법론을 제안하였다. 또한 역산시 현장 자료와 이론 자료 사이의 적합 오차와 상반성 오차를 분석하고, 상반성 오차와 적합 오차를모두 이용하는 자료 가중법을 제안하였다. 현장 자료에 제안된 자료 가중법을 적용한 결과 통상적인 역산 결과에 비하여국지적 이상대의 출현 빈도가 감소하는 것을 확인할 수 있었다. 주요어: 전기비저항, 상반성, 잡음, 자료 가중Abstract: All the resistivity data contain various kinds of noise. The major sources of noise in DC resistivity measurementare high contact resistance, measurement errors, and sporadic background noise. Thus, it is required to measure data noiseto accurately interpret resistivity data. Reciprocal measurements can provide a measure of data precision and noise. Inthis study, we proposed a data-weighting method from reciprocity measurement. Furthermore, a data-weighting methodusing both the reciprocity error and data-misfit in the inversion process was studied. Applying the data-weighting methodto the inversion of 3D resistivity data, it was confirmed that local anomalies are slightly suppressed in the final inversionresults. Keywords: resistivity, reciprocity, noise, data weighting

One-dimensional Modeling of Airborne Transient Electromagnetic using a Long Grounded-wire Source

Airborne transient electromagnetic (ATEM) surveying was introduced several decades ago in the mining industry to detect shallow conductive targets. However, conventional ATEM systems have limited depth of investigation because of weak signal strength. Recently, the grounded electrical source airborne transient electromagnetic (GREATEM) system was proposed to increase the depth of investigation. The GREATEM is a semi-airborne transient electromagnetic system because a long grounded wire is used as the transmitter. Traditionally, ATEM sounding data have been interpreted with 1D earth models to save the computing time because modern ATEM systems generally collect large data sets. However, the GREATEM 1D modeling requires numerical integration along the wire, so it takes much more time than the 1D modeling of conventional ATEM. In this study, the adaptive Born forward mapping (ABFM) was applied to the ATEM 1D modeling because the ABFM is incommensurably faster than the ordinary GREATEM 1D modeling. Comparing the results from ordinary and ABFM 1D modeling, it was confirmed that the ABFM can be applied to the 1D modeling of GEATEM.

Identification of leachate from livestock mortality burial using electrical resistivity and small-loop EM survey: case history

Leachate from livestock mortality burial is harmful to the soil and groundwater environment and adequate assessment approaches are necessary to manage burial sites. Among the methods used to detect leachate, geophysical surveys, including electrical resistivity and electromagnetic (EM) techniques, are used in many engineering approaches to environmental problems, such as identifying contaminant plumes and evaluating hydrogeological conditions. Electrical resistivity, with a small-loop EM survey, was used in this study as a reconnaissance technique to identify the burial shape and distribution of leachate from livestock mortality burial in five small separate zones. We conducted a multi-frequency small-loop EM survey using lattice nets and acquired apparent conductivity values along several parallel and perpendicular lines over a burial site. We also compared geophysical results to the geochemical analysis of samples from both a leachate collection well and a downstream observation well within the study area. Depth slices of apparent conductivities at each frequency (obtained from the small-loop EM survey data) clearly identified the subsurface structure of the burial shape and the extent of leachate transport. Low-resistivity zones, identified from two-dimensional (2D) electrical resistivity imaging results, were matched to the five burial zones (within a depth of 5 m), as well as high electrical conductivity of the leachate obtained from leachate collection wells, and depth slices of the apparent conductivity distribution obtained from the small-loop EM survey. A three-dimensional (3D) inversion of resistivity data provided a detailed 3D structure of the overall burial site and leachate pathways. Moreover, these zones were widely spread over the burial site, indicating that leachate potentially extended through damaged regions of the composite liner to a depth of 10 m along the downstream groundwater flow. Both the small-loop EM method and the electrical resistivity method were considered suitable for identifying the shape of the livestock mortality burial and the extent of leachate. A small-loop EM survey and electrical resistivity survey were applied to a carcass disposal site to delineate the burial shape and extent of leachate arising from the burial. The small-loop EM survey was adequate for a reconnaissance survey of the burial zone and the resistivity survey was useful for quantitatively interpreting the pathway of leachate flows in the site.

Frequency-to-time Transformation by a Diffusion Expansion Method

Electromagnetic (EM) methods are generally divided into frequency-domain EM (FDEM) and time-domain EM (TDEM) methods, depending on the source waveform. The FDEM and TDEM fields are mathematically related by the Fourier transformation, and the TDEM field can thus be obtained as the Fourier transformation of FDEM data. For modeling in time-domain, we can use fast frequency-domain modeling codes and then convert the results to the time domain with a suitable numerical method. Thus, frequency-to-time transformations are of interest to EM methods, which is generally attained through fast Fourier transform. However, faster frequency-to-time transformation is required for the 3D inversion of TDEM data or for the processing of vast air-borne TDEM data. The diffusion expansion method (DEM) is one of smart frequency-to-time transformation methods. In DEM, the EM field is expanded into a sequence of diffusion functions with a known frequency dependence, but with unknown diffusion-times that must be chosen based on the data to be transformed. Especially, accuracy of DEM is sensitive to the diffusion-time. In this study, we developed a method to determine the optimum range of diffusion-time values, minimizing the RMS error of the frequency-domain data approximated by the diffusion expansion. We confirmed that this method produces accurate results over a wider time range for a homogeneous half-space and two-layered model.