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12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK

A new software tool for mapping buried assetsAlessandro Simi, Stefania Bracciali, and Guido Manacorda

IDS Ingegneria Dei Sistemi s.p.a. – Georadar DivisionVia Sterpulino 20, 56121, Pisa, Italy

email [email protected]; [email protected]; [email protected];

In submitting this paper for EuroGPR2008 I hereby assign the copyright in it to the University of Birmingham and confirm that I have had the permission of any third party for the inclusion of their copyright material in the paper. The University of Birmingham will license EuroGPR to use this paper for non-commercial purposes. This will be the sole use of this ma-terial.

Abstract - GPR is one of the most reliable instrument to ob-tain information on underground assets. Since its diffusion many efforts have been made in order to overcome one the main limitations of this technology: the “human factor” in data interpretation. Actually, operators have to look and in-terpret a huge number of radargrams. This requires an enormous effort, about 2 days are usually needed for analyz-ing radar data gathered from a 3000 m2 wide site Some use-ful tools like tomography has been developed, but their qual-ity and reliability is related to the migration algorithm which is heavily depending on knowledge of the propagation velo-city of EM wave in the soil.

Hyperbolas Automatic Detection Algorithm reduces human factor in radar data interpretation and aims to strongly lower the amount of time needed for data analysis. The al-gorithm works on each radargram, recognizes the presence of target by looking for the hyperbolic patterns and marks them. Then the operator can correlate those results from dif-ferent radar maps and extract the position of the under-ground assets. The algorithm also gives an accurate evalu-ation of the propagation velocity of EM waves in the soil, so this estimate can be used by migration algorithm in order to obtain an improved tomography. Using both Hyperbolas Automatic Detection and tomography, time needed to ana-lyze a 3000 m2 wide area can be reduced up to less than 4 hours.

Keywords - Utilities, Automatic Detection, Tomography.

I. INTRODUCTION

Utility companies are frequently asking for tools capable of providing them with reliable information on under-ground objects. For the installation of new buried infra-structures, trenchless techniques such as Horizontal Direc-tional Drilling con play a key role. However, using such techniques without reliable information on existing utilit-ies and on the local geology, can be problematic – and even dangerous.Since the early ’90 GPR products have been developed to help operators to obtain precise information on the posi-

tion of underground assets. Since the beginning it ap-peared clear that the main limitations of this technology were low detection rate and low productivity. To overcome these problems several dedicated tools have been developed – hardware and software – that help to re-duce the influence of the “human factor” over the reliabil-ity of results. Hereunder follows a summary review about these tools and the results of the field measurement pro-gram executed for assessing the performance of the pro-cedure.

II. HOUGH TRANSFORM PIPE DETECTION

2.1 Algorithm implementationGPR transmits short EM pulses in the ground and collects sweeps while moving on the ground surface with a pre-defined step. In this way a 2D radar map (radargram) is created by displaying all these traces side by side. Since GPR antenna has a wide beam in the ground, target is illu-minated even if the antenna is not exactly above it; how-ever, when the antenna is above the target, the flight-time to the target is minimum. This phenomenon creates a typ-ical hyperbolic pattern in radar map when a target is present, as shown in Figure 1. The actual depth of the tar-get can be estimated from the position of the apex of the hyperbola, if the propagation velocity of EM waves in the soil is somehow known.

Figure 1: GPR working principle

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The new Hyperbolas Automatic Detection Algorithm (HADA) automatically recognizes and detects hyperbolic patterns generated by pipes in each radargram (radar B-scan) and marks them on their apex. The algorithm can be summarized in 4 steps: pre-pro-cessing of B-scan images, pattern extraction, hyperbola confidence level assignment and target decision with posi-tion estimation. The patterns extraction is based on a gradient filter with threshold. This edge detection is fol-lowed by linking procedure to assemble edge pixels with amplitude continuity into connected sets. Each labelled set is processed by a Hough Transform to search hyperbolic patterns and their apexes in B-scan images. Finally, all apexes have an associated value representing the level of hyperbola confidence. This step of algorithm also per-forms an accurate estimation of hyperbolas eccentricity; the eccentricity of the hyperbolic pattern is related to the depth and to the propagation velocity of the EM waves in that point of the soil. In this way an estimate of propaga-tion velocity is computed for each detected hyperbola.

Figure 2: HADA flow chart

The output of the algorithm is thus a file where apex posi-tion of hyperbolas, the associated score and an estimation of soil propagation velocity are all reported.After the assembly of a map of all detected apexes, a threshold is chosen and used on the score of all the de-termined apexes in order to achieve a desired false alarm rejection rate in the target decision. This is because al-gorithm performance depends on how “good” is the shape of hyperbolic pattern in the map; indeed, the strength of the clutter level as well as soil condition may deform hy-perbolas (e.g. they can have shorter extensions, can be partly hidden by other overlapped hyperbolas, etc.), so that the operator has to “tune” this threshold to obtain the best performance

2.2 Algorithm testHyperbolas Automatic Detection Algorithm was tested in several operative conditions.The efficiency of the algorithm was evaluated by consid-ering two aspects: hyperbolas detection probability and propagation velocity estimation. Tests were performed on 200 MHz and 600 MHz data sets.Following Figure 3 shows a 600 MHz data set collected in a gas station, where the ground is composed by asphalt and typical clayey soil. The map is about 25 m long and this helps to get meaningful results in statistical terms. De-tection Probability is evaluated as the ratio between hyper-bolas detected by HADA and hyperbolas directly identi-fied by the operator.Applying HADA following results were obtained:Threshold=5 False Alarm =16.6/m DP= 100%Threshold=10 False Alarm =4/m DP= 93%Threshold=15 False Alarm =0.2/m DP= 86%Threshold=20 False Alarm =0.08/m DP= 82%

Figure 3: HADA results on 600 MHz radar map

With a 200 MHz radar map collected along the same route, results were as follows:Threshold=5 False Alarm =5/m DP= 90%Threshold=10 False Alarm =3/m DP= 85%Threshold=15 False Alarm =1/m DP= 79%Threshold=20 False Alarm =0.3/m DP= 76%


Figure 4: HADA results on a 200 MHz radar map

In the 200 MHz data set a lower Detection probability than in the 600 MHz set can be noted; this is mainly be-cause of the depth of some targets: relevant hyperbolic patterns are quite sunk in background noise and HADA filters extract hyperbolas with difficulty (as shown in Fig-ure 4). In 600 MHz map these hyperbolas don’t appear due to the shorter range allowed by this antenna; thus, De-tection Probability is not affected by these Missed Detec-tion.After several tests in many sites (over asphalt, grass, con-crete) a threshold value between 15 and 20 was found suit -able for obtaining a good Detection Probability (>85%) and a false alarms reduction (<0.2/m).

Currently other techniques are being developed to im-prove Detection Probability of HADA. For instance, mul-tiple secondary hyperbolas under the main one could be associated to the same pixel to increase the score of the main hyperbola (to date they are evaluated as independ-ent); moreover, best performing edge detector could be in-troduced and 2D post-processing by means of morpholo-gical operators shall be used in order to reduce noisy fea -tures in the connected set map.

2.3 HADA propagation velocity estimateHyperbolas Automatic Detection Algorithm estimates hy-perbolas aperture calculating several parameters and can output the value of propagation velocity of EM waves in the soil for each detection. This is very useful in order to best migrate each hyperbola in the data set. In Figure 5 and Figure 6 is reported a comparison between automatic estimation and manual estimation; it can be noted that res-ults are identical.

Figure 5: HADA propagation velocity estimate

Figure 6: Propagation velocity estimate by hyperbolic fitting

By comparing the values achieved by standard methods (hyperbola manual fitting) with the HADA estimates on correct detections only, has been found an average error lower than 5% and a maximum error of 8% (it must be considered that with hyperbola manual fitting it’s im-possible to discriminate errors below 2%).


As migration algorithm is quite insensible to little vari-ation of propagation velocity (variations in the order of 10-15% do not significantly affect migration results), the quality of tomography using this approach is anyway very good.

III. TOMOGRAPHY

3.1 HADA and tomographyMain purpose of GPR data analysis is to extract informa-tion from the collected GPR signals and to present it in a meaningful form to the operator; as said before, data inter-pretation needs to be easy even for an unskilled operator.On this matter, latest innovations as the Hyperbolas Auto-matic Detection Algorithm (HADA) and the Tomography, that uses migration techniques to produce a more under-standable display where the position and the route of the pipes are immediately recognisable, help to reduce dra-matically the time required for data analysis and for ex-traction of the features of interest and to limit “human factor” in data interpretation.Hyperbolas Automatic Detection Algorithm works on each single scan by searching for targets in the map. It is necessary to acquire data following a uniform orthogonal grid because operator don’t have prior information about directions of pipes in the ground. A dense measurement grid helps also in the visual correlation of the results pro-duced by the algorithm. Note that the step of the grid is re -lated to the working frequency of the used antenna; as higher it is, as finer the step must be. After the on site survey, operator starts analyzing data simply running the Automatic processing. First a typical filtering processing is applied to every scan in order to ob-tain the “best looking” hyperbola pattern. This sequence includes high pass FIR filter of each single trace, “time-zero” correction and background removal (HADA per-forms additional filtering before searching for hyperbolas, so that other filters are not required).Running HADA on the collected data requires only few minutes computer time (just 5 min. for a 500 m2 wide area), and produces a 2D map of the surveyed area show-ing all the relevant hyperbola markers; by plotting all the markers on a X-Y map, the position of pipes can be easily recognized without any radar data interpretation.

Figure 7: X-Y map of HADA markers

In Figure 7 is shown a X-Y view of the surveyed test area. Red lines represent the trajectory of the GPR scans, whereas white markers are the results of the HADA in the depth range 0-2.5 metres.False alarms create great problems in pipes recognition when results are represented into the X-Y visualization. Thus, operator has to choose a threshold value making a compromise between Missed Detection and False Alarms; the value also depends on clutter level of the specific soil; when the clutter level is low, threshold can be reduced and Detection probability increases. The following step is to create a tomographic map of the surveyed area in order to compare and integrate two 2D map of the same area. As can be seen in the following paragraph, Hyperbola Automatic Detection Algorithm be-comes very important also for tomographic visualization of GPR data.

3.2 Extraction on tomographyTomography interpolates migrated data and shows a 2D view of the underground at a determined depth. Main pur-pose of tomographic visualization is to present GPR data to the operator in a very understandable form; in fact, it allows to recognise immediately the position and the route of pipes. The quality and the reliability of the results achievable with this tool are strictly related to the effectiveness of the migration algorithm. Migration focuses energy of the hy-perbola pattern in its apex; if migration algorithm works correctly tomography will be focused and pipes are easily recognizable. In order to obtain the best focalization of the energy, it is crucial to get an accurate estimate of the E.M. wave propagation velocity. EM wave propagation velocity may largely vary in the soil (typical values are within the range 6-17 cm/nsec); thus, the capability of HADA to estimate this value is


crucial for achieving the best tomographic visualization. Thanks to the performance of HADA in evaluating EM propagation velocity, as reported in the previous para-graph, tomography reaches the best results obtainable with such migration technique. Thanks to tomography understandable maps of the under-ground can be produced; in the example shown in Figure8, both main pipeline and junctions are easily recogniz-able as red lines. The image presents the underground situ-ation sliced at a certain depth, making a sort of average on a thin layer with a predefined thickness (typical few centi-meters), in order to reduce clutter effect and to increase target power.

Figure 8 Tomography: main pipeline with two junctions

In Figure 9 is shown the same wide surveyed area (about 80m x 30m) of Figure 7, sliced at 0.5 m depth.

Figure 9: Tomography

However, in some application only tomography and HADA could be insufficient to reach the precision re-quired for pipe localization. For a more correct depth es-timation and a good data interpretation it’s possible to

keep on using radargrams. By marking the pipes on the tomographic/X-Y HADA image operator can open just few radar scans in particular point of the surveyed area and insert plots directly on them (on the apex of the relev-ant hyperbola). Operator recognizes the apex of the relat-ive hyperbola looking at the markers of HADA or in the neighbourhood of the marker previously inserted on tomo-graphy map.

IV. CONCLUSIONS

Main advantages provided by these new tools is basically related to fact that the final results are less depending on the operator skills and that the new procedure optimizes the performance of the whole survey: HADA locates hy-perbolic pattern in the radar map while tomography presents the collected data in a 2D map easy to be under-stood. This method allows a strong reduction of the time needed to analyze GPR data; on the contrary, previous software required the analysis of a huge number of radar-grams that largely impacts on the productivity of the sur-vey. As a result, the data analysis of long road is now quite completely unbound from radargrams. This approach leads to optimum results also in a square area where pipes can have diagonal route, like site shown in Figure 3 and Figure 4: to analyze this 1200 m2 wide area just 2.5 h were required (more than 1 day in the past).In Table 1 some comparison of analysis time of different surveyed zone are reported.

Table 1: Analisys time

Site Size Typical Ana-lysis time

Analysis time

with new SW

Road 500 m 2 days 4 hoursGas station 1200 m2 1 day 3 hours

Square 550 m2 3 hours 1 hours

A reduction of the time needed for analyze the data of about 60% is obtained.After the whole analysis process, depth and position of located pipes in the underground are automatically trans-ferred to a 3D CAD drawing, which is the final output of the survay.

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