Methodology for Automatic A Boundary Layyer Detection using Ground G Penetrating Radaar Tobias Mathiak, Martin Kesting, Ludgger Overmeyer
Veronnika Gau
University of Hannover Institute of Transport and Automation Technology Garbsen, Germany {tobias.mathiak; martin.kesting; ludgerr.overmeyer} @ita.uni-hannover.de
AG RWE Power P Mines Engiineering Centre Frechenn, Germany
[email protected] Veronika.G
I.
detection;
time-
INTRODUCTION
Economically efficient mining processes can be significantly optimized by developing and employing sensor technologies for the detection of material tyypes and boundary layers. These sensors are aimed to assist thhe operator with a continuous operating interpretation system m, thus providing detailed information regarding the workking face during excavation. Therefore, project "SIMT - Senssory Identification of Material Type and Detection of the Interrfaces" targets the integration of a Ground Penetrating Radarr (GPR) and geo electric sensor technology by attaching it onto o a bucket of a bucket wheel excavator [5, 6, 7, 9]. The T feasibility of integrating and using this kind of sensor tecchnology has been proven effective for the conditions of open cast c mining. In this paper the GPR data processing and analyysis for automatic boundary layer detection is demonstrated. II.
P BACKGROUND ON GPR
GPR is used in many areas, such as archhaeology, mineral exploration, pavement and resource evalluation. It is an impulse reflection procedure in which electtromagnetic pulses are radiated into the ground by a transmittinng antenna. When hitting layers, material inhomogeneities or o objects whose electromagnetic properties (e.g. diellectric constant, conductivity) are different from the surrrounding stratum, waves are partially reflected. The reflected waves w are collected by a receiving antenna. Based upon measureed time differences between transmitted and received signals thee position of layers or objects can be calculated if the propagaation speed of the electromagnetic waves in the subsurface iss known [2]. The GPR consisting of antennas, a control unit, power supply and additional sensors like GPS or encoder cann be used ground coupled, but also with varying distance to the ground. Thus, this measuring system can be moved conttinuously over the
The relation between object size and dominant wavelength affects the reflected signal reesponse. The examined surface can be assumed as homogennous if the particle size in the medium is much smaller thhan the wavelength, therefore leading to very weak, neegligible scattering response. Otherwise, the scattering respoonse caused by particles has to be considered. The wavelength λ (m) in thhe examined medium can be calculated using equation (1).. It depends on the velocity c (m/s) of the electromagnetic (EM) wave in free space, the dominant frequency F (Hz) and the material’s dielectric constant εr [8]. (1)
λ = c /( / F ⋅ εr )
GPR measurement data can be displayed as 2D time vs. distance scans also known as raadar image. An exemplary radar image (B-scan) of a measuuring section (working face) acquired in fixed offset configguration of the transmitting and receiving GPR antenna integraated in a bucket wheel excavator in open cast mining is shown in i Figure 1. If several 2D scans are acquired in a close setup foor a defined area with additional positioning information a 3D sccan can be obtained. Measuring Section [m or Coordinated Universal Time (UTC) + GPS data]
Boundary Layers
Keywords—Automatic boundary layer frequency analysis; GPR; open cast mining
is restricted according to s area of interest. The motion speed selected acquisition parameterrs of the employed system and measuring configuration. Thhe frequency range of the respective antennas varies bettween 10 MHz and 5 GHz and depends on the intended purpose [2]. High frequency antennas enable better resolution of suurface structures, but relatively less depth of penetration. Onn the contrary lower frequency antennas provide deeper penettration, but with rather reduced resolution.
Propagation-Time EM Wave [ns]
Abstract— Ground penetrating radars are used u in many fields of application. A challenging task is the t analysis and interpretation of measurement data whereforee nowadays mainly experts’ help is necessary. In this paper an au utomatic boundary layer detection based on selective time-freq quency analysis is proposed. Developed algorithms are tested on measurement data o a bucket wheel acquired with an integrated GPR system on excavator in open cast mining.
Working Face
Air – Material A Material A - Material B Material B - Material C
0° angle Radar Image
Figure 1. GPR raadar image (B-scan)
978-1-4577-0333-7/11/,(((
The GPR raw measurement data contains noise, clutter and interferences and needs therefore to be proceessed. Many filters are developed to improve signal to noise ratio, as well as man operators. 1D visual quality for its interpretation by hum filters act on each single trace separately (e.gg. mean, low-pass, high-pass or band-pass filters), whereas 2D filters f operate on a number of traces or the whole radar imaage (e.g. running average, subtracting average). Moreover, the t attenuation of electromagnetic waves during their propaagation has to be compensated by gain functions. Besidees filtering other techniques are used to improve quality, suchh as deconvolution and migration. Furthermore, pattern recoggnition, numerical modeling and frequency analysis techniqques are used to enhance the interpretation. III.
Sloping Boundary Layer Horizontal Inteerferences
FIELD DATA COLLECTIION
The used GPR consisting of a 300 MHzz transmitting and receiving antenna in fixed offset configuratiion, a control unit and power supply is integrated in a bucket of a bucket wheel excavator at the Inden Mine of the RWE Poower AG [5, 6, 7, 9]. In Fig. 2 the sensor bucket and its functiion are illustrated. The GPR antenna is attached onto the botttom of the sensor housing at the back of the bucket and protected with a synthetic plate against mechanical damagee. This integration permits the radiation of electromagnetic wavves away from the bucket wheel. Data acquisition is triggeredd at a predefined angle and carried out within an adjustable rootation range, thus assuring acquisition of several GPR traces of the working face. Additionally, the acquired data is automatically transferred to a measurement PC with the aid of a wireless local area network. Each data set, also referrred to as trace, is marked and associated with a UTC time stamp. Additional data concerning the rotation angle of the bucket wheel is obtained by an encoder. The GPR posiition in space is determined by a differential GPS sysstem. The timesynchronized measurement PC receives the GPR G measurement as well as the positioning data, mainly conssisting of GPS and encoder information and automatically meerges them to the GPR data using the UTC time stamp. Theese enhanced data sets are stored for further processing andd diverted to the related processing PC for analysis and interrpretation through the local network. Rotattion
Figure 3. Exemplary GP PR survey used for analysis
IV.
Frequency analysis techniqques are used to improve GPR data analysis and have been b widely used in radar and seismic signal processing [1, 3, 3 8, 11]. Many approaches use the Fourier transformation (e.g. FFT), the short-time T) or the continuous wavelet Fourier transformation (STFT transformation (CWT) to classsify material layers and objects by analyzing the frequency spectra of the received GPR signal. The STFT as well as thhe CWT are suitable, because of the non-stationary and time vaarying behavior of GPR signals. These techniques provide information regarding time and R data. Hereafter a classification frequency contents of the GPR system based on neuronal networks or support vector machines is often applied [11]. For the proposed interpretaation system, the GPR raw measurement data is processeed and analyzed automatically. The time-frequency informatioon is received using a Goertzel algorithm [10] applied on winndowed parts along each trace. The following analysis and innterpretation is realized in the time domain using amplitude and a time information of the GPR signal combined with statisttical evaluation techniques. A prediction of existing positioons of boundary layers in the analyzing section is realizeed by an implemented and developed "local specific valuee". V.
Integrated GPR Antenna
Working Face
Sensor Bucket 0° angle
Figure 2. Sensor bucket with integrateed GPR
Material Layering
90° angle
FEATUR RE EXTRACTION
AUTOMATIC GPR P DATA ANALYSIS AND INTERPR RETATION
In continuation, an exemplaryy GPR survey consisting of 74 traces is used to illustrate the analysis and interpretation. The radar image (Figure 3) shows raw data with a sloping boundary layer section betweenn a gravel-sand and a clay-sand composite. Horizontal interferrences distort the analysis and interpretation. Therefore, the raaw data have to be processed.
Surface
Sloping Boundary Layer
Figure 4. Processed GPR survey
A. Data Processing The initial step is eliminating the constant time shift of lowfrequency components (usually noise) in the raw data caused by inductive effects and system dynamic range limitations. In the next step, time varying gain is applied to the GPR traces to compensate for the attenuation due to material absorption and signal dispersion. Hereafter a designed band-pass filter is applied, allowing frequencies in the bandwidth range of 100 MHz up to 2200 MHz. Lower and higher frequencies are attenuated. The band-pass filter is primarily used to suppress noise in the GPR signal response. Afterwards, GPR data is processed with 2D filters in horizontal and vertical direction. These filters smooth the horizontal interferences as shown in Figure 4. In the frequency range this technique causes a slight shift of the main power frequencies to higher frequencies (Figure 5). B. Feature Selection The approach of detecting boundary layers by using only information regarding amplitude content and allocation for each trace often fails to deliver interpretable results. Filtering techniques besides removing noise and clutter affect and attenuate the GPR signal.
Power Spectral Density
GPR Data (raw data) Shift of Main Power Frequencies To Higher Frequencies
GPR Data (processed) Limited Frequency Span
Figure 5. Extract of frequencies of processed GPR trace
Figure 6. Time-frequency analysis of specific frequencies
A range of distinct features has to be considered to realize effective boundary layer detection. A significant possibility to enhance the number of features is using time-frequency information. Investigations on diverse sets of data for different materials (loess, gravel, sand, mixed materials) showed that only a limited frequency span, concerning the pulses main frequencies needs to be reviewed as depicted in Figure 5. Essential time can be spared by employing the Goertzel algorithm looking at specific, predetermined frequencies, instead of the general Fast Fourier Transformation (FFT) algorithm that computes evenly across the bandwidth of the incoming signal. To facilitate a time-frequency dependency a window function is applied on overlapping segments along each trace. At the implementation a Hamming window is applied. For every preselected window the selected frequencies are calculated, a procedure similar to the STFT method. The frequency resolution is defined by the preselected window length. To achieve adequate spectral resolution as well as good time resolution the spectrum is enlarged using zero-padding. Representing the color-coded amplitude in Figure 6, the power spectral density of each spectral component is calculated and then reduced in reference to the observed maximum. This computation is chosen, because the power spectral density indicates the distribution of the signal power over the single frequency components. In Fig. 6 an excerpt of the computation results is displayed similar to a radar image, but rather implicating spectral content of chosen frequencies like a spectrogram. In the range of the sloping boundary layer the energy content of the received signal within most traces is high. Investigations on a variety of data show a good correlation between areas of high signal energy in the spectral analysis and boundary layers in the investigated material layering. Hence, this correlation is further on used as a characteristic feature for boundary layer detection.
Position of Surface
Position of Sloping Boundary Layer
Figure 7. Computed positions of boundary layers in observed GPR section at 640 MHz
C. Calculation and Results To obtain the relevant areas in the spectral radar image (Figure 6) segmented absolute maxima and minima in each trace are automatically calculated and separated. The selection is realized using another implemented specific value (further feature) based on the signal distortion due to dispersion during propagation in the ground. This prevents deterministic preselection of the value, thus enabling it to automatically adapt itself to different processed GPR signals. This specific value is calculated for each trace-signal and every predetermined frequency. The resulting estimated positions of the detected boundary layers include some error that needs to be further corrected. Reason being the time offset inserted throughout the window function by the preselected length of the overlapping segments. Therefore, correcting algorithms are implemented to relocate the calculated positions in relation to the time resolution of the processed GPR data. The result of these computations is shown in Figure 7. The colors specify the strength of the calculated amplitudes for the position of the detected boundary layers. The strongest amplitude is colored blue followed by red and green respectively, relating to the three strongest amplitudes in this section. In Fig. 8 the calculated boundary layer positions at 640 MHz of the
Figure 9. Calculated local specific value for observed GPR section
observed GPR section are displayed in context of the radar image of the working face section. In continuation, the exact position of the boundary layer separating the in-situ material from the following substrate has to be automatically determined. The red line illustrates the rotation angle 0° (zero trace), at which position the surface is lying at around 5.5 ns and the first relevant boundary layer at close to 11 ns. The proposed method to realize this objective includes using a local specific value (further feature), which is the product of a distribution of calculated boundary layer positions of several traces between a -1° to +1° rotation range and their weighted amplitudes, for each respective reviewed frequency. The exemplary results of the local specific value for the observed GPR section are depicted in Figure 9. The maximum peak at 12.9 ns ± time offset ∆tL localizes the area of the relevant boundary layer. This computed area has to be corrected and associated with the positions of the amplitudes for the zero trace. The correction is done to compensate the possibility of picking maxima or minima amplitudes during time offset correction. The identical procedure is used to compute the position of the surface. The distance ∆d (m) of the GPR to the surface as well as the distance to the relevant boundary layer is determined by the equation (2). In the equation v (m/ns) is the velocity of the EM wave in material and ∆t (ns) the time difference (e.g. between surface and first boundary layer). The velocity v of the EM wave inside the in-situ material can be approximated using the ratio of the velocity c (m/ns) of EM wave in free space divided by the root of the relative permittivity εr of the in-situ material [2]. Δd = 0.5 ⋅ν ⋅ Δt = 0.5 ⋅ (c / ε r ) ⋅ Δt
(2)
The relative permittivity of materials in open cast mining is determined by several measurements with a TDR (TimeDomain-Reflectometry) probe. An average value of relative permittivity εr of 4.9 is used for the computation herein. Figure 8. Boundary layer positions calculated at 640 MHz in context of the radar image
The results of detected boundary layers are displayed within a graphical user interface. The calculated values and results are stored on the processing PC for documentation.
VI.
REFERENCES
CONCLUSION
In this paper a developed method is proposed to automatically detect and compute the positions of the first relevant boundary layers in stratum (Figure 10). Signal Processing
Time-frequency analysis of preselected frequencies
Computation of segmented absolute maxima and minima
Time correction
Computation of local specific value to separate the position of boundary layers
Computation of boundary layer positions (Surface and next relevant boundary layer)
Figure 10. Procedure of proposed method
The developed algorithms were tested on a variety of GPR surveys acquired in open cast mining with a bucket wheel excavator. Determining the accuracy of the computed boundary layer positions relative to the real material layering is one objective of present work. An accuracy of ± 0.1 m is aimed for the automatic detection of boundary layers.
[1]
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