GPR METHODS

Outline1. Theory

2. GPR Data collection

3. GPR Data processing

4. Data Interpretation

Sukanya

Tun

1. Theory

- Ground-penetrating radar(GPR) is an non-destructive geophysical method

- Popular in shallow surveying

- Composed of varying magnetic fields and electric fields

GPR has three main

Components

-Transmitter-Receiver

-And Control unit

Antenna

Propagation of an EM wave with orthogonal electrical and magnetic waves

εr= The relative electric permittivity µr = The relative magnetic susceptibility

2.GPR Data collection

It provides reliable GPR data. Can be operated by using 120 volt AC or 12 volt DC and has dimensions of 466 x 395 x 174 mm with weight of 12 kg. Its operating temperature is -10 to 40 C and has a range of 0 – 8000 nanoseconds. The sample rate varies from 256 to 8192 points per scan and displays in real time.

Data storage GSSI,SR20.

100 MHz Shielded Antenna.

200 MHz Shielded Antenna.

GPR lines collected at area

3.GPR DATA PROCESSING

GPR data processing step by step

Geometry

- Before processing the GPR data, first input the acquisition geometry into an Excel file (*.xls) - Then convert the data from the RADAN file format (*.DZT) to Matlab, and used the program AreaGeeometry.m to edit the geometry and - Convert the result to the seismic standard *.segy file. The steps involved in this operation are:

Cut Line Beginning / End Regularization Line Reversal

Example of the program used to control mark spacing

Filtering - To eliminate unwanted coherent and random noise from the GPR records. After trying a number of different filters, apply the following filtering sequence to all data lines: - - Trace Balance/AGC - Applied a trace-by--trace filter to balance the amplitudes by normalizing the root-mean- square (r.m.s.) amplitude within a 128-sample window.- Eigenimage Processing - Useful signals usually are overprinted by the ‘wow’. To enhance useful signals, applied a eigenimage filter (eigenimage.m) that selectively removes the flat horizontal reflections from the GPR profiles and leaves the shorter wavelengths.

4.DATA INTERPRETATION

Data Interpretation

After applying the appropriate processing steps. Three groups of anomalies can be distinguished based on reflection characteristics in the 2D model.

Strong reflection anomalies - caused by archaeological structures such as walls or floors.

Weak reflection anomalies - Hyperbolic anomalies in the 2D model likely caused by small objects in the ground. Such small anomalies in the present study were interpreted as subsurface features, such as boulders, bricks or tree roots.

Horizontally linear anomalies -Normally observed in most profiles at the two-way time between 5-8 ns, which was interpreted as the wow signal

2D Time Slices from 3D Cube

Strong continuous anomaly imaged in the west side of Area-7

Strong Reflection

Weak Reflection