1

ESTIMATION OF GROUNDWATER LEVEL BY USING GROUND PENETRATING RADAR

ABSTRACT: This work presents the results from a GPR survey done for estimation ground water level. Reflection data were collected with the common-offset configuration of the GPR. The velocity of EM waves was estimated by velocity analysis of CMP data. The average velocities obtained were used for computing the dielectric value of the medium. These dielectric values were then substituted in the header file of the processed common offset data. The processed common offset profiles clearly shown high amplitude reflections from the water table. The velocity of EM waves increases from January to May and again minimizes from May to June. The work presented in this paper also explains the changes in two way travel time with the changes in soil saturation and groundwater level. Results show that GPR technique can be applied to monitor groundwater levels in hard rock terrains.

KEYWORDS: Ground-penetrating radar; Common midpoint method; Velocity analysis; Two way travel time.

1. Introduction:

Ground water level data must be collected accurately and over periods of sufficient time to enable the proper development, management, and protection of the ground water resources. Water level data are collected over various lengths of time, dependent on their intended uses. Short-term water level data are collected over periods of days, weeks, or months during many types of ground water investigations. For example, tests carried out to determine the hydraulic properties of wells or aquifers typically involve the collection of short-term data. Water level measurements needed to map the altitude of the water table or potentiometer surface of an aquifer are generally collected within the shortest possible period of time so that hydraulic heads in the aquifer are measured under the same hydrologic conditions. Usually, water level data intended for this use are collected over a period of days or weeks, depending on the logistics of making measurements at different observation well locations. The usual way to model the hydraulic conductivity in field, in detail consists in constraining the field with additional information such as hydraulic head measurements during a pumping test (Chapuis et al., 1998). The abundance of indirect measurements of conductivities, even if inaccurate, should allow a better modeling of the conductivity field and consequently, of the flow (Young, 1996). The relationship between water content and dielectric permittivity has been studied and dielectric models have been suggested to describe the mathematical relations of soil particles, water and air (Ulaby et al., 1986). GPR data has been used for resolution of many of the most complex problems dealing with ground water availability, sustainability and also for salinity and layer thickness studies ( David et al., 2006).

Ground Penetration Radar (GPR) is a high resolution technique of imaging shallow soil and ground structures using Electromagnetic (EM) waves in the frequency band of 10-1500MHz. GPR can provide very detailed and continuous

2

images of the subsurface. Since GPR is highly sensitive to presence of water in the soil, the method has been used successfully in hydrological investigation to locate the water table and to delineate shallow, unconsolidated aquifers. The dielectric value of a composite material such as a mineral–air–water mixture is composed ofthe dielectric constants of the individual components, the volume fractions of the components, the geometric properties of the components andthe electrochemical interactions between the components (Knoll and Knight, 1994). GPR reflection techniques are able to detect liquid contaminants and, if combined with rock physics, can be used to map the water content and salinity of sandy soil (Zaki Harari, 1996). Recent literature indicates that GPR is an efficient method to estimate the water content of the subsurface by using the velocity of radar waves derived from Common Mid Point (CMP) profiles. Recent studies have shown that the CMP data have some advantages over common-offset data (Fisher et al., 1992; Grasmueck, 1994). The CMP method records the reflections from the same reflection point several times as the distance between a transmitting and a receiving antenna increases (Nakashima et al., 2001). Velocity travel-time analysis used in conjunction with the CMP data acquisition method is the traditional technique for determining the composition of a reflector (Annan and Cosway, 1992).

The GPR functions by sending high frequency electromagnetic waves into the ground from a transmitter antenna. Some of these waves are reflected back to the surface as they encounter changes in the dielectric permittivity of the matrix through which they are traveling and are detected by a receiver antenna. The amplitude and two way travel time of these reflections is recorded on a portable computer. This information is then used to construct a two dimensional plot of horizontal distance versus travel time. Data collected in the field are stored on a portable computer for later analysis. This study aimed to deepen the understanding of the usability of GPR in the groundwater studies, to determine the behavior of groundwater table using GPR data.

2. Experimentation: The study site is located at ‘Y’ point garden of the VNIT, Nagpur campus and

the start position of survey line is 5.5m away from a dug well. A bore well is also situated near the study area. The length of survey line was 140m. TerraSIRCH SIR-3000 GPR system from Geophysical Survey System was used for GPR survey. The frequency of antenna used was 100 MHz. The data was collected on 23rd January 2007, 13th March 2007, 23rd March 2007, 13th May 2007 and 23rd June. Just before 13th March and 23rd June there was a heavy rain and the soil was saturated. The data was collected on these days to study the performance of GPR in saturated soil.

Initially on 23rd January, only common offset survey was conducted with time based mode. The offset between transmitter and receiver was kept as 0.6m. For next surveys, two types of survey methods were adopted: (1) Common Offset Method and (2) Common Midpoint Method (CMP). In common offset method the distance between two survey points was 0.5m. The offset between transmitter and receiver was kept as 1m. In CMP method the minimum offset was 0m and the maximum offset was 25m. The offset increment was 0.5m. CMP gathers were collected at 12.5m and 128.5m. CMP data collected was used to estimate the velocity of the radar waves. RADAN 6.0 software was used for GPR data processing and analysis. Noise reduction, velocity analysis, using estimated velocities obtained the subsurface images. 3. Data Processing:

3

Some survey questions (e.g., anomaly detection) can be answered in the field by looking at the raw GPR data. However, most often data undergoes a series of simple processing steps (filtering operations). Although GPR data is collected with source and receiver antennae of specified dominant frequency, the recorded signals include a band of frequencies around the dominant frequency component. Frequency filtering is a way of removing unwanted high and/or low frequencies in order to produce a more interpretable GPR image. High-pass filtering maintains the high frequencies in the signal but removes the low frequency components. Low-pass filtering does just the opposite, removing high frequencies and retaining the low frequency components. A combination of these two effects can be achieved with a band pass filter, where the filter retains all frequencies in the pass band, but removes the high and low frequencies outside of the pass band.

Some basic and advanced processing was done on raw data. First of all the file header of each raw data was edited. For time based data collection, the scan/second value was kept as 25. For point base data collection the scan/m value was kept as 2 and the m/mark value was kept as 4.64 stacked at the time-based data. Initially, the dielectric value was assumed as 9.In time based data, the radar scans at the start and end of the file were removed. These were the scans when the antenna was stationary. In both time and point based data collection the markers were edited which helps for distance normalization. In time based data collection the number of scans were measured. From that, number of scans per meter was decided.

The ground surface was plane so the surface normalization was kept in auto mode. FIR filter was used for removing the noise. For deciding low and high pass filter value in vertical filtering the frequency spectrum of each profile was studied. The groundwater reflections were seen in 60-250 MHz frequency range. So the vertical low pass filter value was kept at 250 MHz and vertical high pass filter value was kept at 60 MHz. In CMP gathers there was reverberation from the water level. So instead of getting hyperbolic nature of ground water reflector we got a straight-line pattern. To remove the reverberation effect deconvolution parameter was used. The operator length was decided as 61 by measuring the wavelength of the scans at the water table reflector. Same deconvolution parameters were used for common offset profiles. Thereafter using FIR filter until the data becomes interpretable filtered each profile. 4. Interpretation and Discussion:

The radar propagation velocity is proportional to the square root of the dielectric constant. With a good estimate of the propagation velocity, images with respect to travel time (two-way travel time down and back to the surface) were transformed directly to images with respect to depth. Propagation velocities were estimated with biostatic CMP GPR data alone. However, for monotonic data, and to get the best values, some sort of ground truth must be used to correlate the GPR time data with depth. Once this is done, the electromagnetic propagation velocity can be calculated. In Figure 1 we can see the rock stratigraphy. The stratigraphic layers were clearly seen at depths 14.00m, 16.40m, 17.90m, 19.80m, 21m and 22.10m.

Insert Figure 1 On 23rd January, the data was collected in both time and point base. For this

data a dielectric value 9.00 was substituted by assuming a basaltic type rock. The common offset between transmitter and receiver was kept 0.60m. For point base data

4

collection the survey points were marked at 2.00m. For this setting of point base data collection we did not get any interpretable profiles. So for the next surveys the offset between transmitter and receiver was kept as 1.00m and the survey points were marked at 0.50m. The CMP gathers were collected at 12.50m and 128.50m to compute the wave velocities. The velocities corresponding to maximum amplitude of reflection were used. On 13th March, 23rd March, 13th May and 23rd June we got velocities as shown in Table 1.

Insert Table 1 The average of velocities obtained at 12.50m and 128.50m were used to

compute the dielectric constant of the medium. The soil in the study area was clayey type with a black cotton soil layer of about 0.60m underlying 0.15m thick layer of clayey soil. The rock was of basaltic nature. The standard velocity for clayey soil varies from 0.086-0.11 m/ns and the dielectric constant varies from 8-15. For wet basaltic rock the standard velocity is 0.106m/ns and the dielectric constant is 8.00. Basically the velocity and dielectric constant depends upon the amount of saturation. The average dielectric values obtained at 12.50m and 128.50m were substituted in the file header of common offset profiles. Figures 2 and 3 show the processed common offset data which was collected on 13th of March.

Insert Figure 2 and 3 In figure 2and 3, the variation of velocity at 12.50m and 128.50m for the data

collected on 13th of March has been shown. From Table no 2 it is clear that form January to May, the saturation of soil minimizes, as a result, the propagation velocity increases from January to May. Because of heavy rains in early days of June the velocity of propagation again starts showing a rising trend. As the saturation minimizes the propagation velocity increases and the corresponding dielectric constant minimizes. The water level was lowered from 23rd January to 13th March by 0.02m. From 13th March to 23rd March there was no depletion of water level. From 23rd March to 13th May there was slight rise in water level of about 0.60m. From 13th May to 23rd June the water level was raised by 0.46m.

Insert Table 2 4.1. Estimation of Soil Water Content with GPR Reflected Waves:

The most commonly used relationship between apparent permittivity,ε, and volumetric soil water content, θ (m3m-3), was proposed by Topp et al. (1980):

(1)

which was determined empirically for mineral soils having various textures. The term apparent is used because the permittivity used in this equation is determined from the measured electromagnetic propagation velocity in the soil.

A more theoretical approach to relating soil water content and ε is based on dielectric mixing models, which use the volume fractions and the dielectric permittivity of each soil constituent to derive a relationship. In dielectric mixing models, the bulk permittivity of a soil– water–air system, εb, may be expressed with the Complex Refractive Index Model (CRIM):

(2)

5

where n (m3m-3) is the soil porosity; εw, εs, and εa are the permittivities of water, soil particles and air, respectively; and is a factor accounting for the orientation of the electrical field with respect to the geometry of the medium (α=1 for an electrical field parallel to soil layers, α=-1 for an electrical field perpendicular to soil layers, and α=0.5 for an isotropic medium). After rearranging equation 2, the following expression can be obtained for soil water content:

(3)

After substitution of εa =1 and assuming α=0.5, equation 3 reduces to

(4)

which gives a physical interpretation of a simple soil water content -ε relationship:

(5)

where a and b are calibration parameters and is also referred to as refractive index (na).

The bulk dielectric constant values obtained by velocity analysis were used to compute soil water content. The soil in the study area was clayey type with a black cotton soil layer of about 0.60m underlying 0.15m thick layer of clayey soil. The dielectric constant value for dry clayey soil is 3. The dielectric constant for water and air is 81 and 1 respectively. The porosity of soil in the study area was 0.2719m3m-3. Using equation 5, the soil water content was estimated and is as shown in Table 2.Figure 4 and 5 show variation of two way travel time with time at 12.50m and 128.50m. These graphs clearly show that two way travel time principally depends upon soil water content.

Insert Figure 4 and 5 There were several possibilities for the fluctuation of ground water level and

variation in soil water content. Water levels in many aquifers follow a natural cyclic pattern of seasonal fluctuation, typically rising during the winter and spring due to greater precipitation and recharge, then declining during the summer and fall owing to less recharge and greater evapotranspiration. The magnitude of fluctuations in water levels can vary greatly from season to season and from year to year in response to varying climatic conditions. Changes in ground water recharge and storage caused by climatic variability commonly occur over decades, and water levels in aquifers generally have a delayed response to the cumulative effects of drought. The range and timing of seasonal water level fluctuations may vary in different aquifers in the same geographic area, depending on the sources of recharge to the aquifers and the physical and hydraulic properties of each. It was expected that the water level and soil water content will decline with arrival of summer.

The withdrawal of ground water by pumping is the most significant human activity that alters the amount of ground water in storage and the rate of discharge from an aquifer. A pumping well facility was provided 5.5m away from a survey start point. Also a bore-well was also situated 40m away from the start position of survey line. The bore-well yields water from confined aquifer. The well was not yielding

6

significant amount of water so the water pumped from bore-well was stored in well and thereafter was used for gardening. So a significant variation of water level near to the well due to pumping of water was not observed. 5. Conclusion:

This work presents the results from a GPR survey done for ground water level estimation. Reflection data were collected with the common-offset configuration along 140m survey line. The raw common offset GPR data were processed by editing the file header and marker table, stretching, horizontal scaling, FIR filtering, deconvoluting and again by FIR filtering till the profiles became interpretable. The CMP gathers collected at 12.5m and 128.5m were used to estimate the wave velocity. The average velocities obtained were used for computing the dielectric values of the medium. This dielectric values were then substituted in the header file of the processed common offset data. The processed common offset profiles clearly shown high amplitude reflections from saturated soil water zone near the ground surface, capillary fringe and from the water table. The estimated velocity values were nearly equal to the tabulated velocity for clayey soil along with a basaltic type rock. These values principally depend upon water content of the medium. The velocity of EM waves increases from January to May and again minimizes from May to June. This happened due to minimization of soil saturation with arrival of summer. The velocity of EM waves again decreased with arrival of rain. The graphs clearly show that the dielectric constant values minimizes with minimization in soil saturation. This proved that the dielectric constant principally depends upon the soil saturation. The soil water content was maximum in January and minimum in March. On 13th March due to heavy rain in early days the water content was more as compared to the value on 23rd March. The soil water content increased by 0.0142 m3m-3 from 23rd March to 13th May. From the research conducted, it appears that GPR has the potential to become a valuable tool in ground water level and soil water content estimation. The new GPR systems currently being released are pointing the way towards very simplified GPR mapping.

7

References Annan, A.P., Cosway, S.W., (1992). Ground-penetrating radar survey design. Paper

prepared for Annual Meeting of SAGEEP. Beres, M., and Haeni, F. P. (1991). “Application of ground-penetrating-radar

methods in hydrogeologic studies.” Ground Water, 29(3), 375-386. Birken, R., and Versteeg, R. (2000). “Use of four-dimensional ground penetrating

radar and advanced visualization methods to determine subsurface fluid migration.” J. of Applied Geophysics, 43(2-4), 215-226.

Chapuis, R.P., Chenaf, D., Marcotte, D., Chouteau, M., 1998. Pumping test in an unconfined aquifer in Lachenaie. 51st Canadian Geotechnical ConferenceŽin french., Ottawa, Ont., Canada, pp. 515–522.

Conyers, L. B., and Goodman, D. (1997). Ground-Penetrating Radar: An Introduction for Archaeologists. AltaMira Press.

David A. Willett, Kamyar C. Mahboub and Brad Rister, (2006) Accuracy of Ground-Penetrating Radar for Pavement-Layer Thickness Analysis, ASCE, J. Transp. Engrg., Volume 132, Issue 1, pp. 96-103.

Doolittle, J.A., Jenkinson, B.D., Hopkins, D. Ulmer, M., and Tuttle, W. (2005). “Hydropedological investigations with ground penetrating radar (GPR): Estimating water-table depths and local ground-water flow pattern in areas of coarse textured soils.” Geoderma,131, 2006, 317-329.

Fisher, E., McMechan, G.A., Annan, A.P., (1992). Acquisition and processing of wide-aperture ground-penetrating radar data. Geophys. Prospect. 57, 495–504.

Grasmueck, M., 1994. Application of seismic processing technique to discontinuity mapping with GPR in crystalline rock of the Gotthard Massif, Switzerland. Proc. 5th Int. Conf. on GPR. University of Waterloo, pp. 1135–1149.

Huisman, J. A., Hubbard, S. S., Redman, J. D. and. Annan, A. P. (2003). “Measuring Soil Water Content with Ground Penetrating Radar: A Review.” Vadose Zone Journal, 2, 476–491.

Knoll, M.D., Knight, R., (1994). Relationship between dielectric and hydrogeologic properties of sand–clay mixtures. In: Proceedings of The Fifth International Conference on Ground Penetrating Radar, Vol. 1 of 3, June 12–16. Kitchener, Ontario, Canada, pp. 45–61.

Mellett, J. S. (1995). “Ground penetrating radar applications in engineering, environmental management, and geology.” J. of Applied Geophysics, 33(1-3), 157-166.

Nakashima, Y., Zhou, H., and Sato, M. (2001). “Estimation of groundwater level by GPR in an area with multiple ambiguous reflections.” Journal of Applied Geophysics, 47, 241–249.

Olhoeft, G. R. (2000). “Maximizing the information return from ground penetrating radar.” J. of Applied Geophysics, 43(2-4), 175-187.

Peters, L., Daniels, J. J., and Young, J. D. (1994). “Ground penetrating radar as a subsurface environmental sensing tool.” Proceedings of the IEEE, 82(12), 1802-1822.

Topp, G.C., Davis, J.L, Annan, A.P., (1980), Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Resources Research 16(3), 574-582.

Ulaby, F.T., Moore, R.K., Fung, A.K., (1986). Microwave remote sensing, active and passive, Vol. III. From Theory to Applications: Appendix E. Microwave Dielectric Properties of Earth Materials. Artech House, pp. 2017–2119.

8

Yuichi Nakashima, Hui Zhou, Motoyuki Sato, (2001), Estimation of groundwater level by GPR in an area with multiple ambiguous reflections, Journal of Applied Geophysics 47(2001)241–249.

Young, C.T., (1996). Can radar be used to predict hydraulic conductivity? GPR ’96 Proc., pp. 161–165.

Zaki Harari (1996), “Ground-penetrating radar(GPR) for imaging stratigraphic features and groundwater in sand dunes”, Journal of Applied Geophysics 36 (1996) 43-52

9

List of Tables Table 1: Estimated average velocities from velocity analysis

Table 2: Variation in soil water content

Table 1: Estimated average velocities from velocity analysis

Date

Position

(m)

Travel time

(ns)

Velocity

(m/ns)

Dielectric constant

Average dielectric constant

water level

(m)

23rd January

- 240 0.10 9.00 9.00 13.17

12.50 200.36 0.10 9.00 13th March 128.50 187.73 0.11 7.44

8.22 13.15

12.50 223.83 0.12 6.25 23rd March 128.50 214.80 0.12 6.25

6.25 13.15

12.50 184.12 0.11 7.44 13th May 128.50 196.75 0.12 6.25

6.84 12.55

23rd June 128.50 207.58 0.10 9.00 9.00 12.09

Table 2: Variation in soil water content

Date Bulk dielectric constant

Average bulk

dielectric constant

Porosity

(m3m-3)

Soil water constant

(m3m-3)

Average soil water content

(m3m-3)

23rd January

9.00 9.00 0.1834 0.1834

9.00 0.1834 13th March 7.44

8.22 0.1493

0.1664

6.25 0.1209 23rd March 6.25

6.25 0.1209

0.1209

7.44 0.1493 13th May 6.25

6.84 0.1209

0.1351

23rd June 9.00 9.00

0.2719

0.1925 0.1925

10

List of figures

Figure 1: Rock bedding

Figure 2: Processed common midpoint and velocity diagram

(Position: 12.5m; Date: 13th March)

Figure 3: Processed common midpoint and velocity diagram

(Position: 128.5m; Date: 13th March)

(Position: 128.50m; Date: 23rd June)

Figure 4: Variation of velocity with time at 12.50m

Figure 5: Variation of velocity with time at 128.50m

Figure 1: Rock bedding

11

Processed CMP gathers along with velocity diagram

Figure 2: Processed common midpoint and velocity diagram (Position: 12.5m; Date: 13th March)

Figure 3: Processed common midpoint and velocity diagram (Position: 128.5m; Date: 13th March)

12

Figure 4: Variation of velocity with time at 12.50m

Figure 5: Variation of velocity with time at 128.50m