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Subsurface Evaluation of Railway Track Using 

Ground Penetrating Radar 
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Subsurface Evaluation of Railway Track Using Ground Penetrating 

Radar DTFR53-05-D-00200 6. AUTHOR(S) 

Roger Roberts(1), Imad Al-Qadi(2), Erol Tutumluer(2), Jeff Boyle(3)

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(1) GSSI (2) Department of Civil Engineering (3) Sperry Rail Service 

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13. ABSTRACT

This report details the implementation of 2 GHz horn antennas for measuring working ballast thickness using ground 

penetrating radar and the initial implementation of a 500 MHz horn antenna used for subballast and subgrade 

characterization. The work was performed as part of the first task order and modification task 1 in the fourth phase of a 

multi-phase ground penetrating radar research and development project. During the course of the project, it was found 

that the 2 GHz horn antennas produced data that contain reflections from the void space in clean ballast. The amplitude 

of the void space reflections was found to be indicative of the degree of fouling. A simple data processing scheme was 

implemented that converted the reflection amplitudes to color bands, which were assigned to relative degrees of ballast 

fouling. The 2 GHz antennas were tested on five different tracks covering more than 382 mi and produced color-coded 

data that correlated well with available ballast condition ground truth. Subballast and subgrade layering to depths 

extending beyond 6 ft and anomalous high amplitude reflection events were successfully identified with the 500 MHz 

antenna. 

14. SUBJECT TERMS 15. NUMBER OF 

PAGES

116 

16. PRICE CODE

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF 

OF THIS PAGE OF ABSTRACT ABSTRACT 

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ii

METRIC/ENGLISH CONVERSION FACTORS 

ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE) 

1 inch (in) = 2.5 centimeters (cm) 1 millimeter (mm) = 0.04 inch (in) 

1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in) 

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft) 

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd) 

 1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE) 

1 square inch (sq in, in2) = 6.5 square centimeters 

(cm2

) 

1 square centimeter (cm2

) = 0.16 square inch (sq in, in2) 

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, 

yd2

) 

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2) 

1 square mile (sq mi, mi2) = 2.6 square kilometers 

(km2

) 

10,000 square meters (m2

) = 1 hectare (ha) = 2.5 acres 

1 acre = 0.4 hectare (he) = 4,000 square meters (m2) 

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE) 

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz) 

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb) 

1 short ton = 2,000 

pounds (lb) 

= 0.9 tonne (t) 1 tonne (t) = 

= 

1,000 kilograms (kg) 

1.1 short tons

VOLUME (APPROXIMATE) VOLUME (APPROXIMATE) 

1 teaspoon (tsp) = 5 milliliters (ml) 1 milliliter (ml) = 0.03 fluid ounce (fl oz) 

1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt) 

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt) 

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal) 

1 pint (pt) = 0.47 liter (l) 

 1 quart (qt) = 0.96 liter (l) 

1 gallon (gal) = 3.8 liters (l) 

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3) 

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3)

TEMPERATURE (EXACT) TEMPERATURE (EXACT) 

[(x-32)(5/9)] °F = y °C [(9/5) y + 32] °C = x °F

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 34 5

Inches

Centimeters01 3 26 1 4 5 7 1098 1 12

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSION

 -40° -22° -4° 14° 32° 50° 68° 86° 104° 122° 140° 158° 176° 194° 212°

 

°F

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iii

 

Table of Contents 

Page

1. INTRODUCTION........................................................................................................................................2

1.1. STATEMENT OF PROBLEM AND TASK GOALS ........................................................................................2

1.2 PREVIOUS WORK...................................................................................................................................4

1.2.1 Summary of Literature Review...................................................................................... 4

1.2.1.1 Ballast Fouling....................................................................................................................................... 4

1.2.1.2 Subballast and Subgrade Mapping......................................................................................................... 5

1.2.1.3 Other Developments .............................................................................................................................. 6

1.2.2 Phases I through III....................................................................................................... 8

2. UTILIZATION OF 2 GHZ HORN ANTENNAS FOR BALLAST CONDITION ASSESSMENT ...10

2.1. THEORY...............................................................................................................................................11

2.2. IMPLEMENTATION ...............................................................................................................................12

3. 500 MHZ HORN ANTENNA DEVELOPMENT....................................................................................15

3.1. THEORY...............................................................................................................................................15

3.2. IMPLEMENTATION ...............................................................................................................................16

4. DATA PROCESSING................................................................................................................................19

4.1. 2 GHZ HORN ANTENNA DATA............................................................................................................19

4.2. 500 MHZ HORN ANTENNA DATA........................................................................................................22

5. GPR DATA COLLECTION, ANALYSIS, AND GROUND TRUTH...................................................25

5.1. TRANSPORTATION TECHNOLOGY CENTER INCORPORATED TRACK, AUGUST 2005.............................25

5.2. AMTRAK NORTHEAST CORRIDOR, MARCH 2006.................................................................................32

5.3. BNSF JUNE 2006.................................................................................................................................42

5.3.1 West Bill, WY–Shawnee Junction, WY Data ............................................................... 42

5.3.2 Crawford, NE–Ardmore, SD Data .............................................................................. 55

5.3.3 Orin Subdivision, Wyoming, July 2007 ........................................................................... 62

5.3.3.1 Data Collection .................................................................................................................................... 62

5.3.3.2 Ground Truth ....................................................................................................................................... 63

5.3.3.3 Two GHz Horn Antenna Data ............................................................................................................. 65

5.3.3.4 Five Hundred MHz Horn Antenna Data .............................................................................................. 74

5.3.4 Alaska Railroad, August 2007..................................................................................... 80

5.3.4.1 Data Collection .................................................................................................................................... 80

5.3.4.2 Ground Truth ....................................................................................................................................... 81
iv

5.3.4.3 2 GHz Horn Antenna Data.................................................................................................................. 84

5.3.4.4 500 MHz Horn Antenna Data.............................................................................................................. 90

6. CURRENT HARDWARE AND SOFTWARE STATUS .......................................................................98

6.1. EQUIPMENT .........................................................................................................................................98

6.2. DATA COLLECTION RATE....................................................................................................................98

6.3. SENSITIVITY TO EXTERNAL NOISE ......................................................................................................98

6.4. DATA COLLECTION SOFTWARE ...........................................................................................................99

6.5. DATA POST-PROCESSING SOFTWARE ..................................................................................................99

7. SUMMARY AND FINDINGS ................................................................................................................100

8. RECOMMENDATIONS FOR FUTURE RESEARCH AND DEVELOPMENT..............................102

ACKNOWLEDGMENTS.................................................................................................................................103

REFERENCES ..................................................................................................................................................103

ACRONYMS......................................................................................................................................................106
v

Illustrations 

Page 

Figure 1. Typical active railroad ballast cross-section. ..............................................................2 

Figure 2. Typical ground truth cross-trench in railroad, showing ballast condition. ................3 

Figure 3. Results of ballast with various fouling conditions using short-time Fourier transform 

(Al-Qadi et al., 2007)................................................................................................7 

Figure 4. STFT result for the scan with coherent RF Interference (Al-Qadi et al., 2007). .......7 

Figure 5. Data collected from ballast after water sprayed on surface at various times (Al-Qadi 

et al., 2007)...............................................................................................................8 

Figure 6. Section of GPR data obtained with 1 GHz horn antennas during first three phases of 

FRA project (modified from Hyslip et al., 2005).....................................................9 

Figure 7. Comparison between 2 GHz (top) and 1 GHz (bottom) data obtained along same 

section of HTL loop track. Solid and dashed lines show interpreted ballast bottom. 

The only processing applied to this data was a time-zero correction. The depth 

units are inches in the vertical scales on the left side of the figure. .......................10 

Figure 8. Interpreted 2 GHz horn antenna data from Figure 4 using textural differences in the 

data. The depth units are inches in the vertical scales on the left side of the figure.

................................................................................................................................11 

Figure 9. Interpretation of 2 GHz horn antenna data................................................................12 

Figure 10. Data collection setup for the 2 GHz horn antennas. ...............................................13 

Figure 11. Data collection setup for initial TTC data collection..............................................16 

Figure 12. Comparison of 500 GPR data and ground truth at one of cross trenches dug at 

TTC: (a) 500 MHz data on shoulder; (b) picture of cross-trench; (c) 500 MHz data 

from between rails; and (d) fouling index versus moisture content obtained from 

analysis of samples extracted from cross-trench. ...................................................17 

Figure 13. Construction of scattering amplitude envelope from GPR data. ...........................19 

Figure 14. Data processing and display steps applied to TTC 2 GHz GPR data. ....................22 

Figure 15. Data processing and display steps applied 500 MHz data......................................24 

Figure 16. Comparison between GPR and ground truth data from section of thick, clean 

ballast layer.............................................................................................................27 

Figure 17. Comparison between GPR and ground truth data from section of thin, fouled 

ballast layer.............................................................................................................27 

Figure 18. Comparison between GPR and ground truth from section of moderately fouled 

ballast layer.............................................................................................................28 

Figure 19. Scattering amplitude envelope at each cross-trench location. Red and green lines 

indicate end-member amplitude decay characteristics. ..........................................28 

Figure 20. Scattering amplitude envelope at cross-trench locations with the location of the 

first major layer boundary indicated by red asterisks. Black lines are stepped-type 

decay curves and blues lines represent relatively steady slope amplitude decay. 

(Data from 3 cross-trench locations excluded due to incomplete or inconsistent 

sampling). ...............................................................................................................29 

Figure 21. Average amplitude envelope slope in ballast layer versus weighted fouling index 

from sieve analysis of ballast sample. Colors indicate relative weighted percent 

moisture content. ....................................................................................................30 

Figure 22. Weighted fouling index versus percent moisture of samples obtained within ballast.

................................................................................................................................30 
vi

Figure 23. Average amplitude envelope slope in ballast layer versus weighted fouling index 

from sieve analysis of sample obtained within ballast. Blue and black points are 

from sloped and stepped scattering amplitude envelopes, respectively.................31 

Figure 24. Processed TTC data along a section with 4 cross trenches shown at a density of (a) 

6 Scans/ft and (b) 0.5 scans/ft. The ballast fouling depths based on cross-trench 

data are represented by the white circles................................................................32 

Figure 25. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 189-190 on Amtrak track (bottom). Red ovals denote large 

areas of significant fouling near the bottom of the ties ..........................................33 

Figure 26. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 190-195 on Amtrak track (bottom).Red ovals denote large areas 

of significant fouling near the bottom of the ties. “ST” denotes passenger platform 

location. ..................................................................................................................34 

Figure 27. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 195-200 on Amtrak track (bottom). Red ovals denote large 

areas of significant fouling near the bottom of the ties. .........................................35 

Figure 28. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 200-205 on Amtrak track (bottom). Red ovals denote large 

areas of significant fouling near the bottom of the ties. “ST” denotes passenger 

platform location. ...................................................................................................36 

Figure 29. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 205-210 (bottom) on Amtrak track.Red ovals denote large areas 

of significant fouling near the bottom of the ties. ..................................................37 

Figure 30. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 210-215 on Amtrak track (bottom). The large areas with 

missing data are associated with a replaced rail being too close to the antenna on 

the shoulder. “ST” denotes passenger platform location........................................38 

Figure 31. Available ground truth data (top) and automatically processed GPR data on right 

shoulder from MP 215–218.6 on Amtrak track (bottom). The large areas with 

missing data are associated with a replaced rail being too close to the antenna on 

the shoulder. ...........................................................................................................39 

Figure 32. Mudspot data: (a) Picture of mudspot on Amtrak track at MP 203.71; (b) 

corresponding ground truth; and (c) processed GPR data......................................41 

Figure 33. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 82.9-85 on BNSF track...............................................................43 

Figure 34. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 85-90 on BNSF track..................................................................44 

Figure 35. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 90-95 on BNSF track..................................................................45 

Figure 36. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 95-100 on BNSF track................................................................46 

Figure 37. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 100-105 on BNSF track..............................................................47 

Figure 38. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 105-110 on BNSF track..............................................................48 

Figure 39. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 100-115 on BNSF track..............................................................49 
vii

Figure 40. Available ground truth data (top) and automatically processed GPR data (bottom) 

from Main 2 MP 115-117.4 on BNSF track...........................................................50 

Figure 41. Mudspot data: (a) Picture of mudspot on BNSF track Main at MP 109.61; (b) 

corresponding ground truth; and (c) processed GPR data......................................52 

Figure 42. Ground truth data from MP 97 on Main 2: (a) Pictures of sample location; (b) 

Processed GPR data; and (c) analysis of ground truth sample...............................53 

Figure 43. Ground truth data from Main 2 at MP 91.8 + 255 ft and 280 ft: (a) and (b) Pictures 

of sample locations; (c) Processed GPR data; and (d) analysis of ground truth. ...54 

Figure 44. Available ground truth (top) and automatically processed GPR data (bottom) from 

Main2 MP 433–435 on BNSF track.......................................................................56 

Figure 45. Available ground truth (top) and automatically processed GPR data (bottom) from 

Main 2 MP 435–440 on BNSF track......................................................................57 

Figure 46. Available ground truth (top) and automatically processed GPR data (bottom) from 

Main 2 MP 440–445 on BNSF track......................................................................58 

Figure 47. Available ground truth (top) and automatically processed GPR data (bottom) from 

Main 2 MP 445–450 on BNSF track......................................................................59 

Figure 48. Available ground truth (top) and automatically processed GPR data (bottom) from 

Main 2 MP 450–451 on BNSF track......................................................................60 

Figure 49. Mudspot data: (a)Picture of mudspot on BNSF track Main 2 at MP 446.02; (b) 

corresponding ground truth; and (c) processed GPR data......................................61 

Figure 50. Data collection setup. Two 2 GHz horn antennas were mounted on the shoulders 

and a 500 MHz horn antenna was mounted between the rails. ..............................62 

Figure 51. Typical ground truth hole. Pink material at bottom is top of the subballast. ..........64 

Figure 52. Donkey Creek Junction ground truth location exhibiting no fouling based on 

surface inspection, but 4-inch thick layer of coal dust just beneath the top of the 

ballast......................................................................................................................64 

Figure 53. Processed 2 GHz horn antenna data showing degrees of processing: (a) Processed 

data; (b) color key for bar data at the bottom of (a). ..............................................65 

Figure 54. Processed 2 GHz horn antenna data from section of track with predominately clean 

ballast between M2 MP 35.0–MP 37.0. .................................................................66 

Figure 55. Processed 2 GHz horn antenna data from section of track containing severely 

fouled ballast M2 MP 44.6–MP 44.8 and MP 45.6–MP 45.8................................67 

Figure 56. Comparison of the GPR predicted fouling depth and the fouling index calculated 

from sieve analysis of samples. ..............................................................................67 

Figure 57. Comparison between GPR-calculated fouling depth and percent moisture. ..........70 

Figure 58. Plot of percent moisture versus fouling index from all the ground truth samples. 

The sample points are color-coded based on the depth they were extracted..........71 

Figure 59. Processed 2 GHz data obtained between MP37 and 39 on track Main 2 before a 

rainfall event...........................................................................................................72 

Figure 60. Processed 2 GHz data obtained between MP 37 and 39 on track Main 2 during 

rainfall event...........................................................................................................72 

Figure 61. Processed 2 GHz data obtained between MP 44 and 46 on track Main 2 before a 

rainfall event. Data has been reversed relative to data collection direction. As a 

result the left track side data in this figure corresponds to the right track side data 

in Figure 62.............................................................................................................73 

Figure 62. Two GHz data obtained between MP 44 and 46 on track Main 2 during rainfall 

event. ......................................................................................................................73 
viii

Figure 63. Processed GPR data between MP 51 and MP 49 on track Main 2: (a) 500 MHz 

data and (b) 2 GHz data. A significant reflection from the bottom of the ballast in 

the 500 MHz data the correlates to a thick clean section of ballast calculated from 

the 2 GHz data.......................................................................................................75 

Figure 64. Ground truth of hole dug on track Main 3 at MP 52 +286 ft..................................76 

Figure 65. Processed (a) and interpreted (b) 500 MHz GPR data at ground truth location on 

track Main 3 near MP 52. Mapped layers in (b) are in good agreement with the 

actual locations of the ballast-subballast and subballast-subgrade interfaces. .......77 

Figure 66. Ground truth hole dug on track Main 1 at the crossing at MP37.8 + 1,420 ft. .......78 

Figure 67. Processed (a) and interpreted (b) 500 MHz GPR data at ground truth location on 

track Main 1, crossing at MP 37.8 + 1,420 ft south. Mapped layers in (b) are in 

good agreement with the actual locations of the ballast-subballast and subballastsubgrade interfaces. ................................................................................................79 

Figure 68. Processed 500 MHz data from MP 8.0–10.0 on track Main 2. Reflections from 

layers along this segment extend beyond 6 ft in depth. A relative permittivity of 

6.25 was used to calculate depth from two-way travel time. .................................80 

Figure 69. Typical ground truth hole. The depth of this hole relative to the top of the tie was 3 

ft..............................................................................................................................82 

Figure 70. Layer of distinct blue-gray crushed stone that is typically near 24-inches deep at 

most ground truth locations. The moisture content of the subballast beneath this 

layer was substantially higher. ...............................................................................83 

Figure 71. Fouling index and moisture content profiles at 6-inch deep increments at the 

sample location corresponding to Figure 70. Sample associated with blue-gray 

layer is 18–24-inches deep. ....................................................................................83 

Figure 72. Another ground truth location exhibiting the layer of blue-gray stone...................84 

Figure 73. Fouling index and moisture content profiles at 6–inch depth increments at the 

sample location corresponding to Figure 72. Sample associated with blue-gray 

layer is 12–18-inches deep. ....................................................................................84 

Figure 74. Processed data from the right shoulder from MP 115-117.25. The processed –data 

indicates that the shoulder ballast is generally clean to depths 12 in or greater 

along this segment of track.....................................................................................85 

Figure 75. Processed data from right shoulder from MP 203–201. The processed data 

indicates the shoulder ballast is generally fouled at depths within 2 in relative to 

the bottom of the ties. .............................................................................................85 

Figure 76. Comparison of the GPR predicted fouling depth and the fouling index calculated 

from sieve analysis of samples at 6-inch depth increments. ..................................86 

Figure 77. Comparison between GPR-calculated fouling depth and percent moisture. ..........88 

Figure 78. Plot of percent moisture compared to fouling index from all the Alaska ground 

truth samples. The sample points are color-coded based on the depth they were 

extracted. Bold symbols indicate samples extracted 1–2 days after rainfall between 

0.3–0.5 in................................................................................................................89 

Figure 79. Comparison of processed 2 GHz data before and the day after a rainfall estimated 

to be 0.3 and 0.5 inches ..........................................................................................90 

Figure 80. Comparison of 500 MHz data and ground truth at one location.............................91 

Figure 81. Automatically picked highest amplitude reflections between 30- and 75-inches 

deep. Color key: Blue < 67.5 dB, Green 64.5–70 dB, Yellow 70–72.5 dB, Red > 

72.5 dB. ..................................................................................................................92 
ix

Figure 82. Locations of Surface track geometry defects (orange pushpins) and frost heaves 

(red pushpins) superimposed on top of the highest amplitude GPR reflections 

between 30- and 75-inches in depth. GPR Color key: Blue < 67.5 dB, Green 64.5-

70 dB, Yellow 70-72.5 dB, Red > 72.5 dB. ...........................................................93 

Figure 83. Locations of two surface track geometry defects (orange pushpins) near MP 194 

superimposed on top of the highest amplitude GPR reflections between 30- and 

75- inches deep (top), and corresponding 500 MHz GPR data (bottom middle) and 

processed 2 GHz data (bottom). .............................................................................94 

Figure 84. Locations of two track geometry-surface defects (orange pushpins) between MP 

164 and 165 superimposed on top of the highest amplitude GPR reflections 

between 30- and 75-inches deep (top) and , corresponding 500 MHz GPR data 

(bottom middle), and processed 2 GHz data (bottom). ..........................................95 

Figure 85. Location frost heave at MP 170.7 superimposed on top of the highest amplitude 

GPR reflections between 30- and 75-inches deep (top) and corresponding 500 

MHz GPR data (bottom). .......................................................................................96 

Figure 86. Location frost heave at MP 196.3 superimposed on top of the highest amplitude 

GPR reflections between 30- and 75-inches deep (top) and corresponding 500 

MHz GPR data (bottom). .......................................................................................97 

Figure 87. Current ballast evaluation survey types and maximum data collection speeds. .....98 
x

Tables 

Page

Table 1. Laboratory measured value of Dielectric Constant for Different Ballasts modified 

from Sussmann (2002) .............................................................................................4 

Table 2. Two GHz Data Collection Settings on SIR-20s........................................................13 

Table 3. 500 MHz data collection Settings on SIR-20s ...........................................................18 

Table 4. Summary of data collection areas and ground truth...................................................25 

Table 5. Data Collection Summary on BNSF Railroad ...........................................................63 

Table 6. GPR accuracy related to ground truth fouling data....................................................69 

Table 7. Data Collection Summary on Alaska Railroad .........................................................81 

Table 8. GPR accuracy related to ground truth fouling data....................................................87 

Table 9. Summary of data collection areas and ground truth...............................................101 
1

Executive Summary 

This report details the implementation of 2 GHz horn antennas for measuring working ballast 

thickness using ground penetrating radar and development and implementation of a 500 MHz 

horn antenna for characterizing the subballast and subgrade. The work was performed as part 

of the first task order and modification task one in the fourth phase of a multiphase ground 

penetrating radar research and development project. During the course of the project, it was 

found that the 2 GHz horn antennas produce data that contain reflections from the void space 

in clean ballast. The amplitude of the void space reflections was found to be indicative of the 

degree of fouling. An automatic data processing scheme was implemented that first converted 

the reflection amplitudes into reflection amplitude envelopes, then mapped the amplitude 

envelopes to color bands and calculated the thickness of clean ballast. The 2 GHz antennas 

were tested on six different track conditions. Data were obtained on the High-Tonnage Loop 

(HTL) track at the Transportation Technology Center (TTC) in Colorado, on Amtrak track 

south of Boston, MA, on tracks in Wyoming and Nebraska, and on the Alaska Railroad. 

Repeat data were obtained on the Wyoming and Alaska Railroad track before and after 

significant precipitation events. Sections of track indicating ballast distress via mud stains on 

the rails and ties correlated well with the evident distressed zones in the processed colorcoded data. Other sections of track that were recently undercut are indicated as containing 

mostly clean ballast in the processed color-coded data. Samples were obtained for moisture 

content and sieve analyses from 40 locations in Wyoming and Alaska during Modification 

Task One performance period. Agreement was good between the clean ballast thickness 

calculated from the samples and from the GPR data. This developed methodology presents a 

feasible approach for routine inspection of working railroads and is especially suited to assess 

the ballast fouling condition near the bottom of railroad ties. In 2007, Burlington Northern 

Santa Fe (now known as BNSF) railroad, encouraged by the results, contracted out more than 

1,000 mi of data to be obtained with the 2 GHz antennas. An air-launched 500 MHz horn 

antenna was developed as part of modification task one in 2007 for the purpose of mapping 

the subballast and subgrade. The performance of this antenna was assessed on the HTL loop 

at TTC. Layers extending over 4 ft in depth were identified in the GPR data and confirmed by 

cross trenches. The 500 MHz antenna was subsequently mounted between the rails for data 

collection in Wyoming and Alaska. Subballast and subgrade layer reflections observed in the 

GPR data compared well with ground truth in Wyoming. Automatic picking of the highest 

amplitude reflections between the depths of 30 inches (in) and 75 in was performed and the 

amplitude values were mapped in Google Earth. Some correlation was observed between the 

location of frost heaves and/or local bodies of water and anomalous high amplitude 

reflections. However, much more data and ground truth need to be obtained to better 

understand the relationship between these high amplitude reflection zones in the 500 MHz 

data and track structural problems. 
2

1. Introduction 

This report summarizes the work performed in the first task of Phase IV and Modification 

Task One of a multiphase track subsurface evaluation using ground penetrating radar (GPR) 

partially funded by the Federal Railroad Administration (FRA). The work presented here was 

focused on implementing GPR for automatically mapping the condition of ballast and 

characterizing the subballast and subgrade. With accurate and real-time information collected 

by GPR, severe hazards to railroad traffic can be prevented and remediation can be planned. 

1.1. Statement of Problem and Task Goals 

Railroad ballast is the uniformly graded coarse aggregate placed between and immediately 

underneath railroad ties. Subballast is sand or gravel used to provide drainage and, along with 

ballast, distributes the applied dynamic train loading into the subgrade. The ballast and 

subballast system are commonly referred to as the granular layer supporting track with a 

design thickness of typically 18–30 inches (in). In some instances, the ballast may be placed 

directly on stable subgrade. Figure 1 illustrates a typical active railroad cross-section. Often a 

transition area occurs at the ballast-subballast interface containing fines. 

Figure 1. Typical active railroad ballast cross-section. 

Over time, ballast is progressively contaminated by fine-grained aggregate and metal dust that 

fills the void space between ballast stones. This contamination is commonly referred to as 

fouling. The fouling index is used to describe the level of fouling in track and is calculated as 

the summation of the percentage of material passing the #4 sieve plus the percentage of 

material passing the #200 sieve (Selig and Waters, 1994). When the fouling reaches a specific 

threshold, the structural integrity of the contaminated ballast system can be compromised and 

the drainage ability of the ballast system is jeopardized. This leads to track instability and 

ultimately, without corrective maintenance, train derailments. Hence, early detection of 

ballast fouling is of utmost importance to maintain safe operation and cost-effective 

maintenance. Due to its critical location, evaluation of ballast condition near the bottom of the 

ties is highly desirable. 

Mostly Clean Ballast 

Clean Ballast 

Fouled Ballast or Subballast 

Subgrade 
3

Figure 2 is an example of a typical ballast condition in the railroad. The top 10 in contain 

clean ballast. Beneath 10 in the ballast is fouled by fine-grained material. It is apparent that 

the condition of the ballast below the ties cannot be assessed visually without invasive 

methods such as cross-trenching. One major benefit of GPR is the ability to assess the 

condition of the ballast in a nondestructive manner. 

Figure 2. Typical ground truth cross-trench in railroad, showing ballast condition. 

The range of track structural problems includes the portion of the roadbed below the ballast 

layer. These problems include, but are not limited to, subgrade failure, subgrade attrition, and 

trapped water (Hyslip et al, 2005). Antennas with center frequencies much lower than 2 GHz 

are necessary to penetrate to depths up to 7 ft or greater. The lower-frequency GPR antennas 

are useful for mapping the ballast-subballast and subballast-subgrade interfaces. The depths of 

the layer interfaces calculated from the GPR data provide useful information related to the 

stability of the structure. The relative amplitudes of the reflections from the interfaces mapped 

in the GPR data are typically indicative of the presence of excessive moisture. Consequently, 

the mapping of the roadbed structure below the ballast layer using GPR can potentially 

provide valuable information that will make track maintenance more efficient and hence save 

money. 

The ultimate goal of this project is to advance GPR technology as a viable and efficient tool 

for rapid evaluation of track subsurface condition which is not amenable to visual inspection. 

The work presented in this report is part of the fourth phase of an ongoing FRA project. The 

immediate goals of the initial work done in Phase IV of this project were to: (1) develop 

automatic processing algorithms for evaluation of ballast condition from GPR data, (2) 

evaluate the performance of these algorithms on at least two separate ballast types. 

Subsequent to the initial task, the goals of modification task one were to: (1) further verify the 

performance of the ballast fouling assessment methodology on more track with different 

ballast conditions, and (2) enhance the ability to automatically detect and evaluate subballast 

and subgrade using 500 MHz GPR. 

Clean 

Ballast

Very-Fouled 

Ballast or 

Subballast
4

1.2 Previous Work 

Ground penetrating radar is a nondestructive technique that has been evaluated over the past 2 

decades to detect ballast contamination. The reported success of this evaluation is mixed. As 

part of this phase of the project, a review of previous work was performed. Technological 

advances continually provide new hardware and software that, in turn, permit the testing of 

new ideas and data collection and analysis methodologies. Often the case is that proper 

evaluation of ideas was not possible in the past due to equipment limitations that are no longer 

a factor. Consequently, periodically reviewing work that has already been done is important.

1.2.1 Summary of Literature Review 

 

Much progress has been made using GPR for railroad investigations, especially in the past 5 

years. The review presented below focuses on the recent work that has practical implications. 

1.2.1.1 Ballast Fouling 

Studies by Sussmann et al. (2002) and Clark et al. (2001) have established the relationship 

between the fouling and dielectric constant. The significant increase in dielectric constant 

associated with fouled ballast relative to clean ballast can be attributed to the fouling material 

filling the void space. In addition, the dielectric contrast between clean and fouled ballast is 

often magnified by the greater ability of the fouling material to retain moisture. The 

laboratory results of the dielectric constant for different ballast conditions from Sussmann 

(2002) are listed in Table 1. 

Table 1. Laboratory measured value of Dielectric Constant for Different Ballasts 

modified from Sussmann (2002) 

Material ε r Dry ε r Saturated ε r Drained 

Granite ballast 3.2 26.4 16.0 

3.7 Dolomite Ballast 25.5 16.5

4.3 Trap Rock Ballast (basalt) 31.4 4.8

5.0 Trap Rock Ballast (10% fouled) 22 17.0

3.5 Trap rock ballast (30% fouled) 28 20.6

 

Keogh et al. (2006) used the RAIL RADARTM system to measure the signal travel time and 

material dielectric constant of each detected subsurface layer, as well as the tie condition. The 

RAIL RADARTM is an integrated system for accurate tie and ballast condition assessment. 

Tie condition is determined based on the interpretation of a high-resolution down-looking 

digital camera. Ground penetrating radar is used to measure ballast condition, including 
5

ballast layer thicknesses, ballast fouling and moisture-content variations. The GPR system 

used included a single transmit antenna and multiple 1.1 GHz receive antennae. It acquires 

samples at typically every 1–2 in and can resolve layers as thin as 1.5 in to a depth greater 

than 5 ft in normal railbed structure. The results showed that the RAIL RADARTM system has 

a layer thickness measurement accuracy of ±5 percent and is able to survey through wooden 

or reinforced concrete ties. The data collection speed is limited to 25 mph because groundcoupled antennae are used. Keogh et al. (2006) noted that the radar pulse propagation velocity 

significantly decreased (10-30 percent) from clean to fouled ballast as a result of increased 

fine particles. Accordingly, the RAIL RADARTM approach can be used to define an 

acceptable ballast threshold. 

 

Kind et al. (2006) used an automated common midpoint measurement to obtain the wave 

propagation velocity. In the experimental setup, two 500 MHz antennae were set up on 13 ft 

by 13 ft test specimen. The transmitting antenna was placed in a fixed position with an 

elevation of 8 in above the ballast. The receiving antenna was mounted on the trolley in the 

same elevation. By changing the location of receiver, the wave propagation velocity can be 

obtained. According to related dielectric constant, the ballast fouling index can be estimated. 

1.2.1.2 Subballast and Subgrade Mapping 

Recent work using GPR for subballast and subgrade mapping has been focused on air-coupled 

antennas operated at 400 MHz to 1 GHz center-frequency. 

Considering that the logistics requirement of ground-coupled antenna limit the GPR survey 

speed to about 25 mph, Kathage et al. (2005) used three 400 MHz and one 1 GHz horn 

antennae in the GPR survey on railway. Two Geophysical Survey Systems, Inc. (GSSI) SIR20 control units were used for collecting the GPR data. The results showed that GPR can be a 

successful method for the investigation of railway embankments and structure at a surveying 

speed of 72 mph. 

A GPR system mounted at the front of the train, using up to four air-launched antennae (IDS 

RIS-2K), was used by Al-Nuaimy et al. (2004). A number of digital video cameras collected 

optical and infrared video data from different angles, which was time-synchronized with GPS 

data. The data collection speed was up to 125 mph. Then, the following off-line processing 

stages were used: (1) gain normalization, (2) vertical and horizontal band-pass filtering, (3) 

clutter and air-wave removal, (4) distance normalization, (5) filtering and interpolating for 

GPS abnormalities, (6) topographic correction for water table, (7) extraction of sub-sleeper 

data, (8) data fusion, (9) ballast quality analysis and segmentation, (10) ballast depth 

reporting, and (11) suggestions for core sample positions. The results proved that GPR can 

effectively detect various degrees of ballast conditions including: ballast thickness, uneven 

settlement, perched water table, sinkholes, and subballast irregularities. 

Eriksen et al. (2006) used 400 MHz IDS bow-tie antennae and a 1 GHz GSSI horn antenna 

for data collection. The control systems used were a RIS-2K unit for the IDS antennae and a 

SIR-20 unit for the 1 GHz horn antenna. The spatial resolution, which depends on the systems 

and number of channels used, ranges from 1 in at 62.5 mph to a maximum of 4 in at 125 mph. 

The raw data was post-processed using the following steps; (1) time-zero adjustment, (2) drift 
6

removal, (3) sleeper-crib separation, (4) surface object detection, (5) background removal, (6) 

appropriate gain application, and (7) interactive layer tracking and depth assessment. These 

automatic processing methods were used to extract ballast layer thickness, reduce the effect of 

non-steel sleepers by separately analyzing the data from different positions. To obtain good 

data, Eriksen et al. (2006) listed the most important parameters for designing GPR antenna 

configuration; (1) operating frequency, (2) sampling interval, (3) station spacing, (4) antenna 

separation, (5) antenna orientation, (6) electrical properties of the host environment, (7) 

resolution frequency, (8) clutter frequency, and (9) external interference. 

1.2.1.3 Other Developments 

In the study of Silvast et al. (2006), the Fourier transform is used to obtain the frequency 

spectrum for each scan. This technique is suitable for a stationary signal whose properties, 

such as frequency, does not change with time. However, as the radar wave energy propagates 

through the ballast, the higher frequencies are attenuated more rapidly than the lower 

frequencies. Consequently, some propagation time dependency occurs in the frequency 

content of GPR data obtained on railroads. A full-scan Fourier transform is useful for 

assessing the total frequency content of a scan. Therefore, comparisons of the frequency 

content of GPR scans from clean ballast versus fouled ballast should be indicative of the 

relative fouling at the locations where the scans were obtained. However, all time-dependent 

information is lost when a Fourier transform is performed on an entire scan. This means the 

depth of clean ballast and the extent of fouling cannot be quantified using this methodology. 

An alternative approach using the Fourier transform on a moving portion of each scan 

provides the additional time (and hence depth) dependent frequency content information 

necessary to quantify the ballast fouling extent. This method is named the Short-Time Fourier 

Transform (STFT). 

Al-Qadi et al. (2007) used the (STFT) to analyze the change in the frequency spectrum of the 

signal versus depth. Figure 3 shows the STFT of three scans representing different ballast 

conditions. The vertical axis of the images represents increasing depth and the horizontal axis 

represents the frequency spectrum. Near the ballast surface, the frequency spectra of the scans 

are very similar. However, with increasing depth there is a pronounced decrease in amplitude 

spectra amplitudes in the range from 0-3 GHz for the fouled ballast scan relative to the 

nonfouled ballast scan. 

A second application of the STFT is isolating signal from coherent clutter in the data from 

external radio frequency interference (RFI). Figure 4 illustrates the STFT of a scan with 

significant RFI. The portion of the frequency spectrum associated with the scattering from the 

ballast aggregate is clearly isolated from the RFI band. 
7

 

 (a) Clean ballast. (b) Moderately fouled ballast. (c) Fouled ballast. 

Figure 3. Results of ballast with various fouling conditions using short-time Fourier 

transform (Al-Qadi et al., 2007). 

 

 

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

surface surface surface

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

(ns)

13.8

surface surface surface

GPR data key for reflected signal:

Middle amplitude

High amplitude

Non reflection

Weak amplitude

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

13.8

RF Noise

surface

0.0 5.3 10.6 15.9 (GHz)

4.6

9.2

13.8

RF Noise

surface

Figure 4. STFT result for the scan with coherent RF Interference (Al-Qadi et al., 2007).

Al-Qadi et al. (2007) used 2 GHz antenna to measure the effects of moisture on the clean and 

fouled ballast, as shown in Figure 5. A water truck was used to control the moisture content of 

a railroad section at the Transportation Technology Center (TTC). The data collected over the 

same section at various times showed that the clean ballast allows water to flow through 

easily. In slightly fouled ballast, fine particles partially block the air voids and influence 

drainage capability, resulting in greater saturation. Consequently, this results in a significant 

GPR reflection at this saturation contrast. Over time, the water drained from the slightly 

fouled section, resulting in much lower reflection amplitude. The results revealed that GPR 

may be an effective technique to measure moisture content in the ballast. 

(ns)
8

 

(a) Data collected before spray 

 

(b) Data collected 0.25 hr after spray. 

 

Significant 

Saturation 

Contrast

Diminished 

Saturation 

Contrast 

(c) Data collected 5 hr after spray. 

Figure 5. Data collected from ballast after water sprayed on surface at various times 

(Al-Qadi et al., 2007). 

Xu et al. (2004) used texture analysis methodology to process 400 and 900 MHz antenna data. 

The texture analysis was performed to track the interfaces between different layers. Xu 

derived a ratio edge detector in the wavelet domain. The results proved that the edge detector 

can successfully enhance the boundaries that separate different layers. 

In general, a great deal of work using GPR for railroad structure assessment has been done in 

the past 3 years. Ground penetrating radar is clearly a useful tool that, if properly used, can 

benefit the railroad industry. 

1.2.2 Phases I through III 

The work performed in this task of the project builds on work performed in Phases I-III. 

Many important developments were made during the first three phases. These developments 

include: (1) determining the antenna type for data collection, (2) establishing the antenna 

configuration to use for data collection, (3) developing preliminary data processing 

methodologies, and (4) demonstrating the effectiveness of GPR for ballast condition 

assessment (Hyslip et al., 2005). 

A significant quantity of data was obtained with 1 GHz horn antennas in the first three phases 

of this project. The strategy for processing data developed in the first three phases of the 

project involved mapping reflections associated with layer interfaces and quantifying textural 
9

differences in the data to distinguish clean from fouled ballast. The textural differences in 

GPR data obtained from ballast using 1 GHZ horn antennas tend to be subtle and can be 

masked by clutter in the data. Figure 6 shows a section of data from the 1 GHz horn antennas 

presented in the Final Report from Phases I–III. 

Figure 6. Section of GPR data obtained with 1 GHz horn antennas during first three 

phases of FRA project (modified from Hyslip et al., 2005). 

Detailed inspection of Figure 6 reveals slight textural differences between the ballast and 

subballast layers in the central portion of the figure. A dotted line demarcates the interpreted 

boundary between the two layers. The inherent difficulty in accurately mapping the ballast 

condition and predicting ballast thickness in this region is readily apparent. This limitation in 

the technique has been eliminated using higher-frequency air-launched antennas. However, 

the trade-off of the enhanced resolution provided by the higher-frequency antennas is less 

penetration depth. 
10

2. Utilization of 2 GHz Horn Antennas for Ballast Condition Assessment 

Between phases III and IV of the project a 2 GHz horn antenna was developed at GSSI. This 

antenna was tested during initial data collection efforts at the High-Tonnage Loop (HTL) test 

track at TTC. The intent of the trip was to start collecting a database of 1 GHz data and 

ground truth for algorithm development. Fortunately, the 2 GHz horn antenna was brought 

along on the trip and a short section of data were collected to observe its performance 

compared to the 1 GHz horn antenna. 

In Figure 7, a section of data collected in May 2005 from the 2 GHz horn antenna are 

compared to 1 GHz horn antenna data collected at the same time. As can be seen, much 

2 GHZ Horn Data 

1 GHZ Horn Data 

Figure 7. Comparison between 2 GHz (top) and 1 GHz (bottom) data obtained along 

same section of HTL loop track. Solid and dashed lines show interpreted ballast bottom. 

The only processing applied to this data was a time-zero correction. The depth units are 

inches in the vertical scales on the left side of the figure. 

less horizontal banding is observed in the 2 GHz data. The clutter-free near-surface region 

between 0–10 in within the 2 GHz data is especially significant. The same depth range in the 

1 GHz data contains a large consistent reflection associated with the reflection from the top of 

the rail. The reflection from the interpreted ballast bottom is easily tracked along 

approximately half of the section in data from both antennas in Figure 7. This leaves 

approximately half of the section that cannot be interpreted using layer-tracking algorithms. 
11

Closer inspection of Figure 7 reveals significant textural differences in the 2 GHz data. These 

textural differences are manually tracked as shown by the yellow line and arrows in Figure 8 . 

Figure 8. Interpreted 2 GHz horn antenna data from Figure 4 using textural differences 

in the data. The depth units are inches in the vertical scales on the left side of the figure. 

The clear distinction in unprocessed 2 GHz data between areas that possess no reflections and 

areas that have a mottled appearance from numerous incoherent scattering from void space in 

the ballast aggregate was a very promising development that necessitated a change in data 

collection and processing strategy. This finding potentially meant that clean ballast could be 

distinguished clearly from fouled ballast without the requirement of a layer interface 

reflection. The next section describes the theory underlying this data interpretation approach. 

2.1. Theory 

The intensity of electromagnetic (EM) wave scattering from discrete scattering objects 

increases as the dimensions of the objects approach the wavelength of the radiated energy. In 

the case of clean ballast, the scattering objects are voids between the ballast aggregate. Ballast 

aggregate may vary depending on the railroad and track application. However, for the 

purposes of this report, in dimensions from 1.5 in to 4 in were assumed. The wavelength 

corresponding to the center frequency of the 2 GHz antenna is 5.9 in within air. The 

percentage void volume of clean ballast is on the order of 30 percent. The void size 

distribution in clean ballast necessarily depends on the ballast size and shape. To maintain a 

void volume percentage of 30 percent, a compacted, well-graded, equi-dimensional ballast 

must maintain void sizes ranging from less than 30 percent of the aggregate size to greater 

than 30 percent of the aggregate size. A roughly equi-dimensional void volume that is 30 

percent of a uniformly dimensioned aggregate corresponds to sizes ranging from 0.5 to 1.2 in, 

which translates to a ratio of 0.08 to 0.2 of the dominant wavelength. 

The reflection from a thin layer that is one-tenth of the radar wavelength can be discernable in 

GPR data. Randomly positioned voids totaling 30 percent of the volume of a material and 

sized one-tenth to two-tenths in relation to the dominant radar wavelength would predictably 

create a discernable scattering pattern that would vary laterally and vertically. Infilling of the 

void space with fines would effectively decrease the void space, resulting in diminished 
12

scattering. This is, in fact, observed in GPR data collected with the 2 GHz horn antennas. 

Figure 9 schematically illustrates the concept. 

Implementation of this model for assessing the depth of clean ballast simply entails detection 

of the depth corresponding to significant loss in scattering energy in the GPR data after 

corrections have been made to accommodate signal scattering, spreading, and absorption 

losses that attenuate the energy received from increasing depths. This is the approach that has 

been taken thus far in the project. 

Figure 9. Interpretation of 2 GHz horn antenna data. 

2.2. Implementation 

Data were collected with three 2 GHz air-launched antennas mounted as shown in Figure 10. 

This is nearly the same as the configuration established in the first three phases of the project. 

The notable difference between the two setups is the data obtained from the center antenna. In 

the first three phases, two antennas were positioned between the rails with one antenna 

transmitting to a second antenna positioned parallel to the first antenna in a manner so that 

both antennas were alternately positioned over a tie or between two ties (Olhoeft and Selig, 

2002). The 2 GHz horn antennas radiation pattern is much more focused than the 1 GHz horn 

antennas. Data collection using a single 2 GHz horn antenna mounted between the rails and 

orientated with the same polarization as the outer two antennas provided reasonable data. The 

outer antennas are mounted 6 in outside the end of the ties and at a height of 14–16 in. It 

should be noted that this configuration, while optimum for obtaining air-launched antenna 

data for the 2 GHz antennas, does not meet Plate C specifications, which specify that the 

minimum clearance above the ties at that location is greater than 32 in. The height limitation 

was not a factor in data obtained in Wyoming and Alaska. However, the antennas had to be 

moved at passenger platforms during data collection on Amtrak. 

Clean Ballast 

Mostly Clean Ballast 

Fouled Ballast or Subballast 

Subgrade 

Ballast Cross-section 

Scan of 

GPR Data 

Line Scan image of 

multiple scans of 

GPR Data 
13

Figure 10. Data collection setup for the 2 GHz horn antennas. 

The antennas were mounted at as low a height as practical to clear obstacles during data 

collection. Most data were obtained with the antennas at a height of 14–15 in above the ties. 

Table 22 lists the data collection settings on the GSSI SIR-20 units used for all the data. 

Table 2. Two GHz Data Collection Settings on SIR-20s 

Setting Value 

Time Range 15 ns 

Scans/ft 4-12 

Transmit Rate Center antenna 200 KHz, Outer antennas 

300 KHz 

Gain Single Point, varied depending on antenna 

Vertical Low-Pass Hamming Filter 5000 MHz 

Vertical High-Pass Hamming Filter 250 MHz 

Two of the 2 GHz antennas used to collect data were FCC certified. The other antenna was 

built prior to the introduction of FCC radiation limitations. The output power of this antenna 

is approximately 6 dB higher than the other antennas. Consequently, this antenna is much less 

sensitive to external noise, which in some locations, significantly interfered with the 

reflections from the ballast. 

Initial data obtained at TTC were collected at a density of 12 scans per foot. These data were 

subsequently decimated to different degrees and processed. Some loss in horizontal resolution 

was necessarily observed in processed data at lower scan densities, but this was the only 

notable difference in processed data quality. The upside of collecting data at lower scan 

densities is that it increases the maximum speed at which the data can be collected. Data 

obtained with a density of 4 scans/ft permitted data collection at a speed of 25 mph. 

Left Side Antenna Center Antenna Right Side Antenna
14

A 15 nanosecond time range yields data to a depth of approximately 30 in. It is possible to 

collect data at longer time ranges and thereby increase the depth range. However, the intent of 

this study was mapping the clean ballast thickness, which is of particular concern near the 

bottom of the ties. 

The GPR data were collected using two GSSI SIR-20 control units. A distance-measuring 

instrument (DMI) was attached to the lug nuts of one of the back wheels of the hi-rail vehicle. 

The DMI provided pulses at regular distance intervals that were used to trigger the GPR 

control units. GPS and video data were obtained in addition to the GPR data at each data 

collection location. 
15

3. 500 MHz Horn Antenna Development 

An antenna with a center frequency much lower than 2 GHz is necessary to image the ballast 

and subballast layering to depths of 5–7 ft. This depth range is typically imaged by 400–500 

MHz GPR antennas. However, these commonly used GPR antennas are designed to be in 

contact with the ground. This restriction is unacceptable for data collection on railroads at a 

reasonable data collection speed. Air-launched antennas are designed differently than ground 

coupled antennas. The air-launched design, commonly referred to as a horn antenna, focuses 

the radiated energy into the ground. GSSI developed a 500 MHz horn antenna in the spring of 

2007 for use in modification task one. 

3.1. Theory 

The theoretical underpinning behind the use of GPR for mapping the subballast and subgrade 

is the fact that the ballast, subballast, and subgrade are typically comprised of materials with 

dissimilar EM properties. Hence, a radar wave will typically be reflected at the ballastsubballast and subballast-subgrade layer interfaces. The magnitude of the reflection is related 

to the contrast in EM properties. Equation 1 is a commonly used equation that approximates 

the reflection amplitude at the layer interface to the layer properties on either side of the 

interface assuming that the layers contain no magnetic minerals: 

 (1) 

where: 

ρ = reflection coefficient; 

ε1 = relative permittivity of upper layer; and 

ε2 = relative permittivity of lower layer. 

Given that clean ballast typically possesses a relative permittivity of 3–4 and dry subballast 

and subgrade possess relative permittivities on the order of 5–9, it is reasonable to expect a 

substantial reflection from the ballast–subballast interface. Water has a relative permittivity of 

81. The huge difference between the relative permittivity of water and typical track 

substructure materials means that the presence of water is typically inferred from anomalous 

high amplitude reflections in GPR data from track substructure. 

It is not certain that there will always be a reflection at the interfaces between the different 

substructure layers. The relative permittivity of fouled ballast increases as the fouling material 

invades the void space between the aggregate. Additionally, the fouling material has a higher 

moisture adsorption capacity, which means that all other things being considered, a fouled 

ballast will have more moisture than an adjacent clean ballast. As a result, a weak or nonexistent reflection is not uncommon from the ballast–subballast interface in areas where the 

ballast is substantially fouled. Likewise, there are scenarios where there is no reflection from 
16

the subballast–subgrade interface due to the fact that the relative permittivity of the subballast 

can be nearly the same as common coarse-grained subgrade. 

The absence of layer reflections and the relative amplitudes of the reflections at the layer 

interfaces can be an important indicator of the condition of the track substructure. This is the 

rationale behind the use of GPR for mapping the subballast and subgrade layers. Numerous 

examples of the application of GPR exist for detecting track structural problems associated 

with these deeper layers in the report from Phases 1–3 of this project (Hyslip et al, 2005). 

3.2. Implementation 

The newly developed 500 MHz air-launched antennas were tested on the HTL at TTC in 

June, 2007. The data obtained at TTC were obtained with two antennas, one mounted between 

the rails and the other mounted 6 in off the end of the ties. Several antenna orientations were 

tested. By mounting the antennas at an orientation of 45 degrees relative to the rails, the 

reflections from the rails and reinforcement in the concrete ties were optimally reduced. 

Figure 11 shows the data collection setup. This configuration was subsequently used for data 

collected in Wyoming and Alaska in 2007. 

Figure 11. Data collection setup for initial TTC data collection. 

A series of 12 cross trenches were dug to verify the outer and inner antennas could map the 

subballast and subgrade layer interfaces. Good correlation was observed between the layers 

tracked in the 500 MHz antenna data and the observed layering in the cross trenches. 

Figure 12 shows the 500 MHz data versus ground truth at one of the ground truth locations. 
17

Several findings from examination of Figure 12 included: (a) good correlation occur between 

the locations of reflections in the GPR data and the ground truth; (b) the reflections from the 

layers are observed in data obtained from the center antenna and the antenna on the shoulder, 

though the data from the shoulder is less cluttered because there are no tie reflections; and (c) 

the deepest reflection in the GPR data corresponding to a layer interface in the cross-trench is 

at 42–48 in. Though no ground truth was obtained from layers below 48-inches deep at TTC, 

it was anticipated that the antenna would be able to resolve deeper layers because the 

amplitude of deeper reflections at TTC were well above the noise floor of the data. This 

assumption was validated by analysis of data obtained in Wyoming and Alaska, which is 

presented later in this report. 

(a) 500 MHz data on shoulder (b) picture of cross trench 

0 5 10 15

0"-6"

6"-12"

12"-18"

18"-24"

24"-30"

30"-36"

36"-42"

42"-48"

Depth 

Moisture content

0 20 40 60 80

Fouling index

Fouling index Moisture content

GPR result

0 5 10 15

0"-6"

6"-12"

12"-18"

18"-24"

24"-30"

30"-36"

36"-42"

42"-48"

Depth 

Moisture content

0 20 40 60 80

Fouling index

Fouling index Moisture content

GPR result

(c) 500 MHz data from between rails (d) fouling index and moisture content 

vs. depth 

Cross trench location 

Figure 12. Comparison of 500 GPR data and ground truth at one of cross trenches dug 

at TTC: (a) 500 MHz data on shoulder; (b) picture of cross-trench; (c) 500 MHz data 

from between rails; and (d) fouling index versus moisture content obtained from 

analysis of samples extracted from cross-trench. 
18

The optimal data collection configuration with the 500 MHz antenna was found to be at a 45-

degree angle relative to the ties and rails. A configuration was successfully tested at TTC with 

the 500 MHz antenna mounted between the rails and the 2 GHz horn antennas mounted on the 

shoulders. This configuration was subsequently used for data collected in Wyoming and 

Alaska in 2007. 

Table 3. 500 MHz data collection Settings on SIR-20s 

Setting Value 

Time Range 50 ns 

Scans/ft 4-12 

Transmit Rate 200 KHz 

Gain Single Point, varied depending on antenna 

Vertical Low-Pass Hamming Filter 1645 MHz 

Vertical High-Pass Hamming Filter 50 MHz 
19

4. Data Processing 

The first task of the project was focused exclusively on data collection and analysis of the 2 

GHz horn antenna data. The follow-on task, Modification Task One, involved the 

development and testing of a 500 MHz antenna. The data processing approach associated with 

the 500 MHz antenna was necessarily much different than the 2 GHz data. The different data 

processing methodologies are described below. 

4.1. 2 GHz Horn Antenna Data 

Initial inspection of the 2 GHz horn antenna data revealed the broad amplitude decay 

characteristics of scattering from the void space in clean ballast. An amplitude envelope could 

be constructed by connecting the peaks of the reflections down a scan as shown in Figure 13. 

A very low amplitude envelope could then be associated with fouled ballast or subballast. 

Likewise, a sudden change in the slope of the amplitude envelope would indicate a change in 

ballast condition. This is the principle behind the scattering amplitude envelope approach. To 

achieve the effective amplitude envelope a number of standard data processing steps have 

been implemented. These steps have been developed based on data collected with extensive 

ground truth at one location and applied to data collected on commercial railroads elsewhere 

in the country. A brief description of each data processing step follows. 

2 GHz Horn Data Interpretation:

Concentrate on Scattering from Voids 

Clean Ballast

Mostly Clean Ballast 

Fouled Ballast or Subballast

Subgrade

Ballast Cross-section Color Transform of 

processed data

Scattering

Amplitude

Envelope

Figure 13. Construction of scattering amplitude envelope from GPR data. 

• Time-Zero Correction. The first significant reflection following the direct-coupling 

between the transmit and receive horn antennas is the reflection from the ballast 

surface. This reflection is the “time-zero” to which all subsequent reflections are 

referenced. Figure 14(a) and Figure 14(b) show a section of data before and after 

application of a time-zero correction, respectively. This processing step is typically 

performed on GPR data. 

• Background Removal. Close examination of the time-zero corrected data shown in 

Figure 14(b) reveals a dark horizontal band in the data corresponding to an arrival 
20

time of 2.5 ns. Low amplitude horizontal banding in horn antenna data collected over 

rails can be due to reflections from the rails or antenna clutter. The banding is readily 

removed using a background removal filter as shown in Figure 14(c). The background 

removal filter chosen for the data was a moving average subtraction with a window of 

1,023 scans. Because the scattering from voids associated with clean ballast are not 

correlated in time over distances greater than a fraction of a foot, the background 

removal filter is a very effective clutter reduction tool. This is readily apparent from 

comparison of the pre- and post-background removal filtered data in Figure 14(b) and 

Figure 14(c), respectively. This type of filtering is safe when preservation of layer 

boundary reflections is not a concern. Layer boundary reflections will vary in time to 

some degree and therefore will not be eliminated by the background removal filter. 

Some amplitude and phase distortion will typically occur, however. This distortion 

does not hinder the detection of significant layer reflections in the fully processed 

data. 

• Gain Restoration. All of the GPR data were collected using a single point gain. This 

means that no amplitude adjustment was applied to the signal arriving from greater 

depths. The later arriving signal will naturally possess lower amplitude in clean ballast 

due to spreading, scattering, and absorption losses. This natural loss of signal strength 

was corrected empirically by assessing the log-linear gain curve that would negate the 

amplitude decay in an exceptionally thick, clean ballast section. This correction was 

found to be a 0–18 decibel (dB) correction, where 0 dB was applied at the top of the 

scan, 18 dB applied on the bottom of the scan, and the linearly interpolated gain values 

applied to samples in between. This correction appears to be robust in that it has been 

successfully applied to data from two substantially different ballast compositions and 

conditions. Figure 14(d) shows a section of data after the gain restoration step. 

Equation 2 presents the relationship used to compute the decibel level from reflection 

amplitudes: 

⎛ A ⎞ LdB = 20 log ⎜ 1

10 ⎟ ⎜ ⎟ (2) 

⎝ A0 ⎠

where: 

LdB = change in amplitude in terms of dB between A1 and A0

A1 = Amplitude value; 

A0 = reference amplitude value. 

• Hilbert Transform. The reflections in GPR data contain waveforms with positive and 

negative polarity peaks. Construction of an amplitude envelope requires only one 

polarity. This leaves a number of options, including, but not limited to, selecting only 

the positive peak, selecting the peaks after calculating the absolute amplitude of the 

signal, and selecting the peaks following application of the Hilbert Transform. The 

Hilbert Transform magnitude yields an amplitude envelope of each reflection 

waveform. This envelope encompasses both the positive and negative polarity portions 

of the waveform and is thus the most intuitive peak-amplitude representation of 

waveforms. Figure 14(e) shows the data appearance following the Hilbert Transform. 
21

• Horizontal Smoothing 

Scan-to-scan variations in the locations and amplitudes of the reflections from voids in 

ballast are evident in Figure 14(e). These random fluctuations are effectively 

minimized by performing a horizontal boxcar moving average. The length of the 

moving average window necessarily determines the maximum horizontal resolution of 

ballast thickness variations. All data were processed using an 8-ft horizontal moving 

average. This filter length is subject to change depending on the type of survey. For 

network-level surveys, a larger moving average may be desirable. For detailed projectlevel surveys where ballast thickness variations on the order of several ties in length 

are preferable, a smaller moving average window is necessary. Figure 14(f) shows 

data following application of the horizontal moving average. 

• Vertical Smoothing 

Subsequent to horizontal smoothing, the data are smoothed in the vertical (i.e. time) 

direction by applying a 200 MHz Hamming window filter. Post-filtered data are 

shown in Figure 14(g). Application of the vertical low-pass filter diminishes the 

vertical resolution of the data. 

The final step transforms the data from amplitude values to color ranges, as shown in 

Figure 14(h). Each color in Figure 14(h) represents a 5–6 dB amplitude range. Examination of 

the processed data in color-coded format provides rapid assessment of the ballast condition 

from the top of the ties to a depth of approximately 30 in. Three important features are in the 

color-coded data that will be examined in detail in the data analysis section: (1) the color 

versus depth; (2) the rate of color transition versus depth; and (3) color reversals versus depth. 

Each of the three features is easily extracted from the color-coded data using automated 

routines. This methodology was initially implemented to process the TTC data discussed in 

Section 5.1. 
22

Figure 14. Data processing and display steps applied to TTC 2 GHz GPR data. 

Data presented in Sections 5.2–5.5 were processed using a slightly different methodology 

whereby the horizontal low pass filter was replaced by a 1,000 MHz high pass FIR filter, 

which was implemented as the first post-processing step. The 1,000 MHz high pass filter 

effectively removes the low-frequency dark band in the data without impacting significant 

layer reflections that could be, for example, associated with trapped moisture. 

A second difference in the data processing associated with Sections 5.2–5.5 was the last step 

applied to the data—a transformation to ballast condition color-coding. Three significant 

developments were implemented: (1) a highly-desirable self-calibrating feature, (2) the signalto-noise level monitoring of the data, and (3) both the rate of scattering amplitude decay and 

total scattering amplitude decay were accommodated in the color transformation. These 

processing changes were introduced after data from Amtrak and BNSF track were obtained in 

2006. All data presented in sections 5.2–5.5 were processed using this color transform step. 

4.2. 500 MHz Horn Antenna Data 

The 500 MHz data were processed differently than the 2 GHz data. After initial decimation of 

the data from a 13.2 scans/m (4 scans/ft) data collection density to 0.3 scans/m (1 scan/ft), a 

time-zero correction was applied. Next, a horizontal background removal was performed, 

where the background scan was obtained by averaging all the scans in the file. File lengths 

were typically 8–16 km (5–10 miles). Subsequently, a 6 m (20 ft) horizontal moving average 

(h) Color Transform 

(a) Raw Data 

(e) Hilbert Transform (f) Horiz. Moving Average 

(b) Time-zero correction (c) Background removal (d) Gain Restoration

(g) Vert. Low-pass Filter 
23

was applied to the data to improve layer reflection continuity. Finally, a 0–42 decibels (dB) 2 

point gain was applied to accommodate for natural signal propagation loss. This correction 

was empirically derived from visual inspection of the data from the Orin Subdivision and 

Alaska Railroad. After implementation of the amplitude correction, anomalous amplitude 

reflections can be easily detected irrespective of their depth. Figure 15 shows the data 

processing steps applied to the 500 MHz data after decimating it to 1 scan/ft density. The fact 

that the same amplitude correction has been applied to data obtained from two very diverse 

track substructures is indicative that the correction values may be usefully applied to data 

obtained from other track substructures. 
24

(a) Unprocessed 500 MHz data, 1 scan/ft data density 

(b) Time-zero Correction 

(c) Background removal 

(d) Horizontal smoothing 

(e) 0-42 dB 2-Point Gain 

Figure 15. Data processing and display steps applied 500 MHz data. 
25

5. GPR Data Collection, Analysis, and Ground Truth 

 

GPR data have been obtained at five different tracks. Fifteen miles of data were obtained on 

tracks in Pueblo, CO in August 2005. Ground truth collected at this location consisted of 15 

cross trenches. Many ground truth methods, including dynamic cone penetrometer (DCP) 

data, visual inspection, and ballast sampling, were implemented at 14 of these cross trenches. 

The GPR data and ground truth from 12 trenches were subsequently used to develop the 

automatic data processing methodologies. Twenty-eight miles of GPR data were collected on 

Amtrak’s tracks between Providence, RI and Sharon, MA in March 2006. Ground truth data 

consisted of five locations along the track where mud splashes were apparent on the ballast, 

ties, and rails. GPR data and ground truth are analyzed in detail in the following two 

subsections. GPR data were obtained on track in the Orin Subdivision, Wyoming and near 

Crawford, NE in June, 2006. 

GPR data have been obtained at five different tracks. Table 4 lists the data collection 

locations, dates, amount of data obtained at each location, subgrade type, and available 

ground truth. 

Table 4. Summary of data collection areas and ground truth. 

Location Date Miles Subgrade Samples and # Mudspots Records of 

Data Type Cross Trenches Undercutting 

And Shoulder cleaning

(miles) 

TTC 8/05 15 Sand 15 0 - 

Providence, RI – 3/06 29.5 Sand/Clay 0 5 29.5 

Sharon, MA 

West Bill, WY– 6/06 75 Sand 5 260 21.9 

Shawnee Junction, 

WY 

Crawford, NE- 6/06 34.9 Expansive Clay 0 54 0.55 

Ardmore, SD 

Gillette Area, 7/07 131 Sand 20 - - 

Wyoming 

Alaska Railroad 8/07 97 Sand/Clay 20 - - 

In 2007, 2 GHz and 500 MHz horn antenna data were obtained along 228 miles of track in the 

Orin Subdivision, Wyoming, and on the Alaska railroad. It was fortuitous that at both 

locations repeat data could be obtained along portions of the track before and after a rainfall. 

5.1. Transportation Technology Center Incorporated Track, August 2005 

TTC track provided the ideal setting to collect GPR data and detailed ground truth via cross 

trenches. Two miles of data were obtained on HTL in August, 2005. Subsequent to data 

collection, 15 cross trenches were dug for ground truth. The condition of the ballast at 

fourteen cross trenches was assessed using a combination of visual and statistical methods. 

Ballast samples from each visually identifiable layer were collected for sieve analysis. A 

fouling index was calculated from the sieve data. This fouling index value was then assigned 

to the visually discernable layer from which the sample was extracted. 
26

A separate analysis was performed using the DCP data. Significant ballast fouling was 

identified based on the following two criteria: (1) there is a significant decrease in the slope of 

the DCP values with increasing depth; or (2) the calculated California Bearing Ratio (known 

as CBR), from DCP data, over an extended depth range is 75 percent or less. The DCP data 

may mainly provide insight into the location of layer interfaces. 

The accuracy of the ballast ground truth measurements is limited by the nature of the ballast 

structure. Due to the presence of a transition area at interfaces, approximate measurements of 

ballast thickness may not be identified visually. Often no clear boundary can be observed 

between fouled ballast, clean ballast, and the subballast or subgrade. A second limiting factor 

in collecting the ground truth was the location relative to the position of the antenna during 

data collection. The backhoe used to dig the cross trenches could not effectively dig a vertical 

cross-section directly below the antenna that was positioned 6 in from the edge of the ties. 

The closest location yielding a vertical cross-section was right at the edge of the ties where 

some differences in ballast compaction and fouling relative to the actual data collection 

location would be expected. 

Figures 16–17 show two end-member comparisons of 2 GHz data and the ground truth at the 

respective cross trenches. Ground penetrating radar data are compared to ground truth from a 

moderately fouled ballast layer in Figure 18. Figure 16 shows an exceptionally clean ballast 

layer. The minimally processed and fully processed GPR data in Figure 16(a) clearly reveal 

no loss in scattering amplitude down to a depth of 20–25 in. The pictures of the cross-trench, 

Figure 16 (c), reveal a clean ballast layer to an approximate depth of 17 in from the top of the 

ties. A thin 3-inch layer of crushed ballast containing some moisture is also evident in the 

figures. This layer lies on top of fine-grained subgrade. Three samples were extracted from 

the face of the cross-trench at depths of 11, 18, and 21 in. The sieve analyses from the deeper 

two samples yielded the fouling index and weight percent moisture values shown in Figure 

16(b). The fouling boundary interpreted from DCP data obtained within several ties of the 

cross-trench is shown as an orange line in Figure 16(b). The large drop in the GPR scattering 

amplitude envelope observed in Figure 16(b) corresponds well with the approximate location 

of the clean ballast bottom. 

Close inspection of Figure 17(b), which compares the scattering amplitude envelope to 

ground truth from a ballast section fouled starting at approximately 2-inches deep, reveals 

rapid attenuation with increasing depth. A less steeply dipping scattering amplitude envelope 

is observed in Figure 18(b) from the cross-trench with intermediate fouling. 
27

Figure 16. Comparison between GPR and ground truth data from section of thick, clean 

ballast layer. 

Figure 17. Comparison between GPR and ground truth data from section of thin, fouled 

ballast layer. 
28

Figure 18. Comparison between GPR and ground truth from section of moderately 

fouled ballast layer. 

Figure 19 presents the scattering amplitude envelopes of scans obtained at the locations of 

cross trenches. Focusing on the depth range from 5 to 25 in, two dominant trends in the 

amplitude curves are: (1) steady slope as illustrated by the red line in Figure 19; and (2) a 

stepped curve as illustrated by the green line in Figure 19. One might initially infer increased 

gradational ballast fouling with increasing depth from the red line and a constant ballast 

fouling followed by an abrupt transition from the green line. 

Figure 19. Scattering amplitude envelope at each cross-trench location. Red and green 

lines indicate end-member amplitude decay characteristics. 
29

Figure 20 presents the scans from Figure 19 with the depth corresponding to the subballast or 

subgrade layer indicated by a red asterisk. A constant permittivity of 4 was used to construct 

the depth scale in Figure 20. The major layer boundary plots near the top or along the steep 

slope of curves with abrupt boundaries (black lines) and at different locations along the decay 

curves with consistent slopes (blue lines). 

Figure 20. Scattering amplitude envelope at cross-trench locations with the location of 

the first major layer boundary indicated by red asterisks. Black lines are stepped-type 

decay curves and blues lines represent relatively steady slope amplitude decay. (Data 

from 3 cross-trench locations excluded due to incomplete or inconsistent sampling). 

The relationship between the relative decay of the scattering amplitude envelope with 

increasing depth and the fouling index can be quantified by charting the average scattering 

amplitude envelope decay rate between 5 in and the top of the subballast or subgrade layer 

versus the weighted average fouling index of samples obtained within the ballast layer, as 

shown in Figure 21. This calculation was used for comparison with the fouling index because 

it does not require any amplitude calibration and minimizes the effect of local amplitude 

disturbances in the GPR data. A general trend of higher decay slopes versus increasing 

fouling index is observed. In keeping all other variables fixed, this relationship would be 

expected considering that the proportional void volume decreases with increasing fouling, 

hence, the scattering from the voids would also decrease. The rate of GPR scattering 

amplitude attenuation is also significantly influenced by moisture content. The colors of the 

points plotted in Figure 21 represent the relative moisture content of the samples. A general 

trend of lower-to-higher moisture content versus lower-to-higher fouling index is observed. 
30

Figure 21. Average amplitude envelope slope in ballast layer versus weighted fouling 

index from sieve analysis of ballast sample. Colors indicate relative weighted percent 

moisture content. 

The fouling index versus moisture content is plotted in Figure 22. A nearly linear relationship 

is apparent between moisture content and fouling index. The average scattering amplitude 

envelope slope versus fouling index is plotted in Figure 23, with the sloping amplitude 

envelopes plotted in blue and the stepped amplitude envelopes plotted in black. Six of the 

seven sloping amplitude envelopes, which are the blue dots in Figure 23, correspond to all of 

the samples with average moisture content greater than 2 percent in Figure 22. A general 

relationship occurs between the shape of the scattering amplitude envelope (i.e., steady slope 

or stepped) and moisture content. The sloping amplitude envelopes may arise from a constant 

attenuation rate when associated with higher moisture content; whereas the stepped scattering 

amplitude envelopes possess a low attenuation rate and lower moisture contents. 

Figure 22. Weighted fouling index versus percent moisture of samples obtained within 

ballast. 
31

Figure 23. Average amplitude envelope slope in ballast layer versus weighted fouling 

index from sieve analysis of sample obtained within ballast. Blue and black points are 

from sloped and stepped scattering amplitude envelopes, respectively. 

An expected increase in fines content would correspond to greater moisture retention 

capability. Because both fouling and moisture would increase the dielectric properties, 

although at different degrees, the research at this stage is not yet clear as to how much of the 

scattering amplitude envelope behavior is due strictly to moisture content versus progressive 

void filling with fouling material. 

The general correlation between the scattering amplitude envelope characteristics, ballast 

fouling and moisture content presented in Figures 21–23 is a promising development with 

practical implications. First, the data processing strategy is very simple and easy to automate. 

Second, for this analysis, relative amplitudes are used, so no calibration is required. Third, the 

technique appears to be very sensitive to moisture content. Fourth, the technique can assess 

ballast condition without requiring reflecting boundaries in the data. Finally, the high vertical 

resolution provided by the 2 GHz horn antennas permits ballast evaluation in the depth range 

associated with the bottom of the ties. 

One of the strengths of the scattering amplitude envelope approach is that it is scaleindependent. The same processing sequence applied to data obtained at a scan density of 4 

scans/ft can be applied to data obtained at 0.5 scans/ft. The desirable data collection density is 

thus dictated by the minimum resolvable ballast fouling area. Network level surveys at speeds 

up to 70 mph can be accomplished at lower scan densities to provide a large scale ballast 

condition assessment. Conversely, more detailed surveys can be conducted at speeds less than 

25 mph to yield ballast condition on the scale of the tie-to-tie separation distance. 

Figure 24(a) shows a section of processed TTC data at a data density of 6 scans/ft. The same 

data decimated to 0.5 scans/ft prior to processing are shown in Figure 24(b). Note the small 

scale features, such as the 10 ft wide pocket of thick clean ballast from 655–665 ft in Figure 
32

24(a) are not observed in Figure 24(b). However, the thinning of clean ballast from the left 

side of the section to the right side of the section is evident in both figures. 

 (a) 6 Scans/ft density (b) 0.5 Scans/ft data density. 

Very Fouled 

Ballast 

Clean Ballast Water 

Information 

Unavailable 

Color Key 

Increasing 

Fouling 

Notes 

(1) Zero Depth = Top of Ties 

(2) Depth to water should be measured 

form center of blue area 

Figure 24. Processed TTC data along a section with 4 cross trenches shown at a density 

of (a) 6 Scans/ft and (b) 0.5 scans/ft. The ballast fouling depths based on cross-trench 

data are represented by the white circles. 

5.2. Amtrak Northeast Corridor, March 2006 

A total of 29.6 miles of GPR data were obtained on Amtrak rails between Providence, RI and 

Sharon, MA on March 31, 2006. Areas of ballast distress were detected visually by staining 

on the ties and rails where the pumping action of saturated fouled ballast forced muddy water 

to the surface. These stained sections, often termed “mudspots” were clearly evident in the 

video data obtained at the same time as the GPR data. Five stained sections of track have been 

identified. No undercutting or shoulder cleaning has been performed along this section of 

track for at least 10 years. The GPR data were decimated from the data collection density of 4 

scans/ft to 0.5 scans per foot before processing. Subsequent to processing, the data were 

further decimated to a density of 0.1 scan/ft to permit display of 5 mi of data per page. Figures 

25–31 show the processed GPR data from the right shoulder from Milepost (MP) 189 to MP 

218.6. 
33

Pre-1996

Figure 25. Available ground truth data (top) and automatically processed GPR data on 

right shoulder from MP 189-190 on Amtrak track (bottom). Red ovals denote large 

areas of significant fouling near the bottom of the ties
34

ST 

Pre-1996

Figure 26. Available ground truth data (top) and automatically processed GPR data on right shoulder fr

Amtrak track (bottom).Red ovals denote large areas of significant fouling near the bottom of the ties. “ST

platform location. 
35

Pre-1996

Figure 27. Available ground truth data (top) and automatically processed GPR data on right shoulder fr

Amtrak track (bottom). Red ovals denote large areas of significant fouling near the bottom of 
36

ST 

Pre-1996

Figure 28. Available ground truth data (top) and automatically processed GPR data on right shoulder fr

mtrak track (bottom). Red ovals denote large areas of significant fouling near the bottom of the ties. “ST

platform location. 

A
37

Pre-1996

Figure 29. Available ground truth data (top) and automatically processed GPR data on right shoulder from 

on Amtrak track.Red ovals denote large areas of significant fouling near the bottom of the 
38

ST 

Pre-1996

Figure 30. Available ground truth data (top) and automatically processed GPR data on right shoulder fr

Amtrak track (bottom). The large areas with missing data are associated with a replaced rail being too clo

the shoulder. “ST” denotes passenger platform location. 
39

Pre-1996

Figure 31. Available ground truth data (top) and automatically processed GPR data on right shoulder fro

Amtrak track (bottom). The large areas with missing data are associated with a replaced rail being too clo

the shoulder. 
40

The ground truth section of each figure contains a number of categories. The top category refers 

to undercutting information. If the segment is left blank, then no undercutting information was 

obtained. Otherwise, the segment above the relevant section of GPR data is colored red or green 

and the year in which the undercutting was performed is indicated. The second category “No 

Video” is yellow over the sections of GPR data where no video data were obtained. The “Ball. 

Cover” category is colored black over sections of track that are covered with ballast. Along these 

sections the ballast cover typically prevents mudspot detection. The last three categories refer to 

mudspots. The last letter of each category refers to the location where the mudspot was detected 

based on inspection of the video data, where “L”, “C”, and “R” refer to the left side, center, and 

right side of the track facing in the direction of increasing MP. A red line is drawn at each 

mudspot location. The length of the line is a relative indicator of the severity of the mudspot. 

The shortest lines indicate staining and the longest lines are indicative of fouling so severe that 

the fouling material covers the ties. 

GPR data should be interpreted using the color key and notes shown in Figure 24. Note, the data 

from the other two antennas are not shown. The GPR data from the antenna located between the 

rails does not yield acceptable results when the data are obtained over concrete ties. The data 

from the left side antenna was compromised by external radio-frequency noise. Fortunately, the 

right-side antenna was manufactured prior to FCC transmission regulations, so the signal 

strength ratio of this antenna is 6 dB greater than the signal strength of the other two antennas. 

This difference in signal strength was sufficient to yield acceptable data from the pre-FCC 

regulation antenna in most of the data collection area. 

Examination of Figures 25–31 reveals that the processed GPR data indicates much of the data 

collection section is fouled at depths less than 10 in. More severe fouling is indicated in certain 

areas. These areas of concentrated fouling are circled. Four out of 5 of the mudspot locations are 

concentrated between MP 203.5–205 in a zone indicated by the GPR data to be heavily fouled. 

GPR data containing strong reflecting horizons can infer the presence of an interface between 

media containing different water saturation levels. These strong reflecting horizons are 

represented by blue in Figures 25–31. Concentrated blue areas at MP 200.55 and MP 202.35 

infer high saturation levels within several in of the bottom of the ties. 

Figure 32 shows processed data at a 4 scans/ft density from a mudspot at MP 203.71. Although 

the processed data in the vicinity of MP 203.71 infers significant fouling, the extent of fouling at 

203.71 is even greater as shown by the black color in the figure. 

Large sections of the GPR data from MP 210–218.6 are not shown due to the presence of a 

replaced rail very near the antenna mounted on the shoulder. The near proximity of the rail is 

observed in the GPR data as a strong reflector, thereby impacting the scattering amplitude 

envelope. 

The GPR data on the Amtrak track were obtained and processed without knowledge of the 

undercutting and shoulder cleaning history in the data collection area. The processed GPR data 

may indicate the correct ballast condition based on the fact that neither undercutting nor shoulder 

cleaning has been done in the past decade. Unfortunately, the data collection area didn’t include 

a section of track that had recently been undercut or subject to shoulder cleaning so as to clearly 

differentiate between known clean and fouled ballast. 
41

(a) Mudspot location at MP 203.71. Antenna data shown below from left side of track 

(b) Ground truth information 

 

 (c ) Processed GPR data 

Pre-1996 

Figure 32. Mudspot data: (a) Picture of mudspot on Amtrak track at MP 203.71; (b) 

corresponding ground truth; and (c) processed GPR data. 
42

5.3. BNSF June 2006 

More than 104 mi of GPR data were obtained on BNSF tracks June 6–8, 2006. Data were 

obtained on Main Tracks 2 and 3 between MP 82 and 118, starting at West Bill, WY and ending 

at Shawnee Junction, WY. The second data set was collected on Main Tracks 1 and 2 between 

MP 433 and 451, starting north of Crawford, NE and ending in Ardmore, SD. 

5.3.1 West Bill, WY–Shawnee Junction, WY Data 

The GPR data were obtained at scan densities ranging from 4 to 18 scans/ft. Video data were 

obtained over most of the data collection area. The video data are missing along approximately 7 

miles because of video camera overheating problems. 

A significant amount of ground truth verification in the form of surface mudspots and 

undercutting records was used to assess the accuracy of this processed GPR dataset for ballast 

condition evaluation. Additionally, the ballast was sampled at five locations. The ballast samples 

were extracted between the rails. Only processed data from the two outer antennas is provided in 

this report. The ties in most of the data collection area were concrete. The processing algorithm 

does not perform adequately on center antenna data obtained on tracks with concrete ties. 

Nonetheless, visual observation of mudspots and close examination of GPR data indicate that in 

most cases a general relationship occurs between the ballast condition immediately outside the 

ties and between the rails. 

Figures 33–40 show processed GPR data and currently available ground truth along BNSF track 

Main 2 from MP 82.9–117.4. Some sections of the track were completely covered with ballast to 

the top of the rails. As a result the bottoms of the antennas were too close to the ballast surface 

and most of the GPR data could not be processed. This is the reason why there are large gray 

sections in Figure 33. 

Available undercutting records from 2004–2006 are provided as part of the ground truth data in 

Figures 34-40. In general, the extent of ballast fouling predicted by the GPR data is much less in 

these recently undercut areas than areas where undercutting has not been performed in the past 2 

years. For example, undercutting performed in 2005 from MP 92.3–92.6 (Figure 35) is clearly 

indicated by a clean section of ballast which is adjacent to a much fouled ballast section from 

MP 91.3–92.3. The section from MP 96.3–98.5 (Figure 36) was undercut 2 months prior to data 

collection. This section appears as very clean ballast in the processed GPR data. The section 

from MP 103.6–106.5 (Figures 36 and 37) was undercut in 2004 and appears as very clean 

ballast in the processed GPR data. The subsequent section from MP 106.5–110.6 was also 

undercut in 2004 but the GPR data indicates a higher amount of fouling. Numerous mudspots, 

observed in video data along this section, support this interpretation. Clean ballast is inferred 

from the GPR data from MP 110.6-117.4 (Figures 39 and 40). This section of track was undercut 

in 2005. Comparison of the undercut sections reveals that there are different levels of fouling 

between the different sections. The most significant fouling among the recently undercut track 
43

Figure 33. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
44

2005 

Figure 34. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
45

2005 

Figure 35. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
46

2006 

Figure 36. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
47

2004 

Figure 37. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
48

2004 

Figure 38. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
49

2004 2005 

Figure 39. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 2 

track. 
50

2005 

Figure 40. Available ground truth data (top) and automatically processed GPR data (bottom) from Main 

BNSF track.
51

sections is observed in the data along the section from MP 86.2–87.6 (Figure 34). In this section 

there is considerable fouling approaching the bottom of the dies ties on the left side of the track. 

All other sections of recently undercut track appear as relatively clean ballast in Figures 34-40. 

A total of 148 separate mudspots were identified based on inspection of video from Track Main 

2 MP 82.9–111.5. There is exceptional agreement between mudspot locations and severe 

shallow fouling indicated by the processed GPR data. The most striking comparison is illustrated 

in the section from MP 90-95 (Figure 35). Concentrated shallow fouling from MP 90–90.75 and 

91.3–92.3 inferred from GPR data is clearly confirmed by many mudspots. Significantly, there 

are no mudspots in the section from MP 90.75–91.3 where the GPR data indicate clean ballast. 

Figure 41 shows high resolution mudspot data from MP 109.61. This portion of the track was 

undercut in 2004. The mudspot captured by the video camera clearly reveals fouling extending 

to the ballast surface. This was inferred from examination of the GPR data presented in Figure 

41(c). The most severe fouling indicated by the GPR data corresponds to the physical location of 

the mudspot. The entire section of GPR data in addition to the mudspot appears to be severely 

fouled near the surface. Additional GPR data were obtained along a parallel track between MP 

82.9–117.4. The data and ground truth are in good agreement. 

The undercutting and mudspot ground truth data are supplemented by five ballast samples that 

were dug between the rails. Comparisons of processed data to ground truth at three of these 

sample locations are presented in Figures 42 and 43. The sample shown in Figure 42 was 

obtained at MP 97 on Main Track 2, which was undercut two months before GPR data 

collection. Very clean ballast was measured to a depth of 15 in between the rails. A sample 

extracted between 15- and 18-inches in depth was very moist, as shown by the mud on the spade 

in Figure 42(a). This moisture corresponds well to the blue band in the GPR data centered at 

approximately 16-inches in depth. 

A local pocket of clean ballast was inferred from the GPR data presented in Figure 43 from MP 

91.8 to MP 91.8 plus 400 ft. Two samples were extracted in close proximity. The sample from 

the fouled section is clearly fouled to the surface as shown in Figure 43(a). The local pocket of 

clean ballast inferred from GPR data was confirmed by the sample extracted between the rails, 

which was clean to a depth of 10 in (Figure 43(c)). The data from outside the ties matched the 

condition of the ballast sampled between the rails. GPR and ground truth comparisons of the 

remaining two samples were in good agreement. 
52

(a) Mudspot location at MP 109.61. Left side of track corresponds to right side of video. 

 (b) Ground truth information 

 

(c) Processed GPR data from left (top) and right (bottom) sides of track. 

2004

Figure 41. Mudspot data: (a) Picture of mudspot on BNSF track Main at MP 109.61; 

(b) corresponding ground truth; and (c) processed GPR data. 
53

 (a) Pictures of sample location. Note the moisture on the spade in the left picture. 

(b) Processed GPR data from antennas outside ties. 

(c) Ground truth analysis from sample extracted between rails. 

Sample Location 

Very Fouled 

Ballast 

Clean Ballast 

Water 

Information 

Unavailable 

Color Key 

Increasing 

Fouling 

Figure 42. Ground truth data from MP 97 on Main 2: (a) Pictures of sample location; 

(b) Processed GPR data; and (c) analysis of ground truth sample. 
54

 (a) Picture of sample location #1. (b) Picture of sample location #2. 

(b) Processed GPR data from antennas outside ties. Use color key in Figure 42 to assess ballast condition. 

 Sample #1 Analysis Sample #2 Analysis 

(c ) Ground truth analysis from sample extracted between rails. 

Sample 

Loc. #2 

Sample 

Loc. # 1 

Figure 43. Ground truth data from Main 2 at MP 91.8 + 255 ft and 280 ft: (a) and (b) 

Pictures of sample locations; (c) Processed GPR data; and (d) analysis of ground truth. 
55

Very good agreement between the GPR data and available ground truth is observed along the 69 

miles of GPR data obtained on tracks south of West Bill, WY. The GPR data clearly revealed the 

extent of ballast fouling associated with mudspots, demarcated the extents of undercutting, and 

inferred the condition of the ballast where undercutting has not been recently done and no 

mudspots are present. 

5.3.2 Crawford, NE–Ardmore, SD Data

The data along tracks north of Crawford, NE to Ardmore, SD were obtained at scan densities 

ranging from 6 to 12 scans/ft. Video data were obtained over the entire data collection area. 

Most of the track contains concrete ties. Consequently, only data from antennas mounted outside 

the ties are presented. Ground truth obtained at the data collection area consists of two sections 

of track which were undercut in 2006 prior to GPR data collection and 54 mudspot locations 

identified from the video data. 

Figures 44–48 show the processed GPR data and available ground truth from the section of track 

identified as Main 2 between MP 433.0 and MP 451. Only 23 separate mudspots were identified 

along this section. Most of the GPR data in this section infers little ballast fouling. Much of the 

fouling that is present is apparently localized. For example, the fouled ballast section from MP 

437.5–-437.7 is surrounded on both sides by clean ballast (Figure 45). Localized fouled areas are 

also apparent in Figure 46 at MP 440.3, MP 440.85, MP 441.4, MP 441.7, MP 443.0, and MP 

444.7. Mudspots were identified at two of these locations and the center of the track was covered 

with 2–4 in of ballast at four other locations. The four locations with covered ballast can 

reasonably be assumed to be areas requiring maintenance in the form of tamping. 

The one section of track that is known to be recently undercut, MP 436.95- MP 437.15 in Figure 

45 shows very clean ballast in the GPR data from the right side antenna and interestingly, some 

significant spot fouling within the undercut section in the left side antenna. This discrepancy is 

currently unexplained. Detailed records of the undercutting along this section have not been 

obtained. There is, however, a turnout located along the left side antenna on this section of track. 

The most fouled sections of ballast on the left side antenna correspond to the beginning and 

ending locations of the turnout. It is likely that the undercutting operation was impacted by 

proximity of the turnout at these locations. 

The very localized mudspot location at MP 446.02, shown in Figure 47, is explored in more 

detail in Figure 49. The picture of the mudspot in Figure 49(a) clearly shows significant fouling 

to the surface in the middle of the track. The corresponding GPR data from outside the rails in 

Figure 49(c) are in good agreement. Significant fouling, extending to the near surface, is clearly 

observed in the GPR data between MP 446.01 and 446.03. The processed GPR data and ground 

truth comparison—along track Main 1 between MP 433.6 and MP 450.5—is similar to the data 

from track Main 2. 

A reasonable relationship between the GPR data and available ground truth is observed from the 

data obtained north of Crawford, NE and Ardmore, SD. Some discrepancy
56

Figure 44. Available ground truth (top) and automatically processed GPR data (bottom) from Main2 MP 

track. 
57

2006 

Figure 45. Available ground truth (top) and automatically processed GPR data (bottom) from Main 2 MP 

track. 
58

Figure 46. Available ground truth (top) and automatically processed GPR data (bottom) from Main 2 MP 

track. 
59

Figure 47. Available ground truth (top) and automatically processed GPR data (bottom) from Main 2 MP 

track. 
60

Figure 48. Available ground truth (top) and automatically processed GPR data (bottom) from Main 2 MP 

track.
61

(a) Mudspot location at MP 446.02. Left side of track corresponds to right side of video. 

 (b) Ground truth information 

 

(c) Processed GPR data from left (top) and right (bottom) sides of track. 

Figure 49. Mudspot data: (a)Picture of mudspot on BNSF track Main 2 at MP 446.02; 

(b) corresponding ground truth; and (c) processed GPR data. 
62

exists between recent undercutting data and inferred ballast fouling based on GPR data along 

one side of the track that is currently unexplained. The mudspots are limited, but correlate well 

to fouling inferred from processed GPR data. 

5.3.3 Orin Subdivision, Wyoming, July 2007 

Ground penetrating radar (GPR) data were obtained on tracks in the Orin Subdivision 

between July 23 and July 26, 2007. The data were obtained using a 500 MHz air-launched 

antenna centered between the rails and two 2 GHz horn antennas mounted on the shoulders. 

5.3.3.1 Data Collection 

BNSF provided a hi-rail vehicle to mount the GPR horn antennas. The data collection setup is 

shown in Figure 50. 

Figure 50. Data collection setup. Two 2 GHz horn antennas were mounted on the 

shoulders and a 500 MHz horn antenna was mounted between the rails. 

Table 5, below, summarizes the GPR data collection. There was significant rainfall in Gillette 

the evening of July 25 and during the day of July 26. The precipitation recorded at Gillette 

Airport on July 25 and 26 was 0.29 and 0.43 in, respectively. On July 26, most of the data was 

obtained while it was raining. The rainfall was fortuitous in that it permitted data collection 

under moist conditions. It was also possible to recollect data on 5-mile (Main 2 MP34.8 – 40), 

18-mile (Main 2 MP 43.3-62), and 0.2-mile (Main 4 MP -0.2 – 0) segments of track that data 

were also obtained on July 23 and July 25, before the rainfall. Historical precipitation records 

at Gillette airport indicate relatively dry conditions with no rainfall events greater than 0.1 in 

during the 2 weeks prior to data collection. Gillette Airport is approximately 12 mi and 35–62 

mi from the shortest and two longer sections of track, respectively, where the repeat data were 

obtained. Local variations in weather can occur, and it is likely, but not certain, that the 

moisture condition in the ballast over the data collection area July 23–25 and was much drier 

than after the rainfall on the evening of July 25 and during data collection on July 26. 
63

Table 5. Data Collection Summary on BNSF Railroad 

Date Track MP Start MP End Total Miles 

7/23/07 Main 2 62 43.3 18.7 

7/23/07 Main 1 43.5 34.9 8.6 

7/23/07 Main 2 34.8 40 5.2 

7/23/07 Main 3 40 54 14.0 

7/25/07 Main 4 -0.2 0 0.2 

7/25/07 Main 2 0 14.2 14.2 

7/26/07 Main 4 -0.2 0 0.2 

7/26/07 Main 3 0 14.7 14.7 

7/26/07 Main 2 14.7 62 47.3 

7/26/07 Main 4 -0.2 0 0.2 

7/26/07 Main 3 0 8 8 

5.3.3.2 Ground Truth 

Ground truth in the form of hand-dug holes was obtained at 20 locations on the shoulder of 

the track. Ground truth holes were dug concurrent with the data collection. All ground truth 

holes were located near road crossings or other well-registered locations along the track that 

could be accurately correlated with the GPR data. Near these registered locations, specific 

ground truth locations were chosen for the purpose of sampling the widest range of ballast and 

subballast conditions based on visual inspection of the GPR data. Figure 51 shows a typical 

hand-dug ground truth hole. Two groups obtained ground truth. One group sampled the 

aggregate at 6-inch depth increments. The other group sampled the aggregate at within each 

visually identifiable layer, the depth of which varied from one ground truth location to 

another. At each sampling depth interval, two samples of aggregate of about 2–3 lbs weight 

were collected and triple-bagged in zip-lock bags. All samples were subsequently analyzed 

for moisture content and aggregate size distribution. 

The two largest factors affecting the accuracy of the samples were their locations 

perpendicular to the edge of the ties, and the fact that discrete samples were obtained. The 

fouling condition of the ballast can change rapidly along a cross-section of the road bed. This 

is important because the radiation pattern of the horn antenna is relatively narrow. The center 

of the 2 GHz antenna was mounted 6 in off the end of the ties. Very careful sample gathering 

would ensure that the sample was always obtained 6 in off the end of the tie. However, some 

of the samples likely were obtained closer to the end of the tie because that corresponds to the 

wall of the hole that was generally the most stable. The samples that were obtained were 

representative of the 6-inch depth increment. Any change in ballast fouling over this 6-inch 

range would be registered in the sample bag as a mixture of the different fouling materials. 

How accurate, then, is the ground truth from the samples? In general, it is believed that most 

of the samples provide an accurate measure of the fouling index at the location of the GPR 

data. The implication is that several samples could be marginally inaccurate due to their 

sample location or a mixture of two different fouling conditions (i.e. very clean and much 

fouled ballast) over the 6 in depth interval. For the purposes of this report, the sample fouling 

is assumed to be constant over 6 in depth increments. 
64

Figure 51. Typical ground truth hole. Pink material at bottom is top of the subballast. 

The top of the subballast layer shown in Figure 51 was typically observed at 18–25 in below 

the top of the ties. In areas where the ballast was clean, the moisture contrast between the 

clean ballast and the subballast was substantial. Correspondingly, a significant layer typically 

is reflected in the GPR data associated with the interface between the ballast and subballast. 

The subgrade layer was located in only two of the 20 ground truth holes. At both locations the 

subballast layer was approximately 10-12-inches thick. The ballast fouling material was often 

visually identifiable as coal dust. The coal on the cars is piled above the top of the car and 

blows off while the train traverses along the tracks. Figure 52 shows one location near 

Donkey Creek Junction where a 4-inch thick layer of coal dust was just beneath the top of the 

ballast that was not identifiable from the surface. 

 (a) Surface at Donkey Creek Junction (b) Coal dust just beneath top of ballast 

Figure 52. Donkey Creek Junction ground truth location exhibiting no fouling based on 

surface inspection, but 4-inch thick layer of coal dust just beneath the top of the ballast. 
65

5.3.3.3 Two GHz Horn Antenna Data 

The 2 GHz horn antenna data obtained on the shoulders of the track were processed using the 

same methodology as the data obtained in 2006. The graphic display of the processed data has 

been finalized and is shown in Figure 53. The color-coded data are shown on the top in Figure 

53. The calculated shoulder ballast height—relative to the top of the ties, clean ballast 

thickness, and depth to significant moisture—are shown in graph the middle section of Figure 

53(a). The bottom section Figure 53(a) contains colors representing clean ballast thickness 

along a thin bar. The color key to the bar is shown in Figure 53(b). The color bar display is 

very intuitive and allows the user to quickly locate areas of significant fouling. All of the 

following figures of processed 2 GHz data in this report are displayed with the format shown 

in Figure 53. 

(a) Processed data: top–color coded data indicated degrees of fouling (green–clean, blue–

moisture, red-black fouled; middle–red dots = depth to fouling, blue dots = depth to 

moisture; bottom–color coded ballast fouling depths fro shoulders; 

(b) Fouling depth color key for color bar data. 

Color-coded processed data 

from left (top) and right 

(bottom) shoulders. 

Color-coded processed data 

Plotted ballast height relative to 

ties (gray), depth to fouling 

(red), depth to significant 

moisture (blue) 

Clean shoulder ballast thickness 

color bars 

Figure 53. Processed 2 GHz horn antenna data showing degrees of processing: (a) 

Processed data; (b) color key for bar data at the bottom of (a). 
66

Inspection of the processed 2 GHz GPR data reveals areas of clean ballast, moderately fouled 

ballast, and severely fouled ballast. The severely fouled ballast sections are typically less than 

0.4 miles in extent. Examples of typical processed data from clean and fouled ballast in the 

Orin Subdivision are shown in Figures 54 and 55, respectively. Some sections of the track 

were recently built, thus contained clean ballast, while other sections were significantly 

fouled. Sudden changes in fouling conditions, as shown at MP 45.6 in Figure 55, were 

common. 

Before comparing the GPR calculated fouling depth to the ground truth data, considerations 

must be taken. It is unclear what fouling index constitutes significant fouling. On the basis of 

data obtained during this project, a fouling index between 20 and 30 is suggestive of 

substantial fouling. The relationship between fouling index and track structural integrity 

depends on several factors. Clearly, increased fouling also increases the likelihood of track 

instability. The potential for track instability is further compounded by certain types of 

fouling material and/or increased moisture content (Tutumluer, 2008). What minimum fouling 

depth presents potential track stability issues? Research indicates that as the fouling depth 

approaches the bottom of the ties, the potential for track instability increases substantially. 

Given this, a fouling depth within several inches of the tie thickness would warrant attention 

(Wenty, 2007). 

Figure 54. Processed 2 GHz horn antenna data from section of track with predominately 

clean ballast between M2 MP 35.0–MP 37.0. 
67

Figure 55. Processed 2 GHz horn antenna data from section of track containing severely 

fouled ballast M2 MP 44.6–MP 44.8 and MP 45.6–MP 45.8. 

In Figure 56, the fouling depth, obtained from GPR data analysis, is compared with the 

fouling index for each sample depth at each ground truth location. The close correlation 

between the location of the GPR-calculated fouling depth and transition from green to yellow 

or red at most ground truth locations in Figure 56 leads to the conclusion that a reasonable 

agreement between the GPR-predicted calculated fouling depth and the depth of the sample 

corresponding to the transition from a fouling index less than 20 to greater than 20. 

Figure 56. Comparison of the GPR predicted fouling depth and the fouling index 

calculated from sieve analysis of samples. 
68

An observed agreement exists between the depth intervals corresponding to changes in ballast 

fouling and the GPR-calculated fouling depth. In hindsight, a better ground truth sampling 

interval would have incorporated gathering a sample at a 9-inch depth, to better assess the 

accuracy of the GPR data at this important depth. Assessing the accuracy of the GPRcalculated fouling depths requires a major assumption that may not be valid for all of the 

ground truth samples. This assumption is that the fouling index is constant throughout the 

depth interval from one sample to the next. This is very likely not the case for one or more 

samples. However, there should be an overall agreement between the GPR-calculated fouling 

depth and the fouling indexes of the samples. A second consideration is value of the GPRcalculated fouling depth in relation to maintenance priority. For example, the accuracy of the 

GPR-calculated clean ballast thickness is most important in the vicinity of the bottom of the 

ties. Therefore, whether the GPR-calculated clean ballast thickness is 2 in or 6 in it will not 

affect the ballast cleaning priority. The same reasoning is applicable for a very thick clean 

ballast, say 16 or 20 inches in thickness. The maintenance priority is not affected by GPRcalculated ballast thickness errors for depths less than the tie thickness and much greater than 

the tie thickness. This is important because the scattering attenuation method is inherently less 

accurate for very thin clean ballast layers and very thick clean ballast layers. 

Using this reasoning, the accuracy of the GPR-calculated ballast thickness for maintenance 

priority assignment will be as follows: 

• Full agreement: GPR depth in first 6-inch depth interval with fouling index greater 

than 20. 

• Likely agreement: GPR depth within 2 in of the first 6-inches depth interval with 

fouling index greater than 20. 

• Moderate fouling agreement: GPR Depth in Fouling Index greater than 10 and less 

than 20. 

• Thick ballast agreement: GPR depth greater than 12 in and ballast 12 in or less in 

thickness has fouling index less than 10. 

• Disagreement: None of above conditions apply. 
69

Table 6. GPR accuracy related to ground truth fouling data 

Sample # Full 

Agreemenet 

Likely 

Agreement

Moderate 

Fouling 

Agreement

Thick 

Ballast 

Agreement

Disagreement

1 Yes 

2 Yes

3 Yes

4 Yes 

5 Yes

6 Yes 

7 Yes 

8 Yes 

9 Yes

10 Yes 

11 Yes 

12* 

13 Yes 

14 Yes

15 Yes

16 Yes**

17 - Yes

18 - Yes**

19 Yes 

20 Yes

- 

 

*Signal-to-noise of GPR data too low to provide accurage GPR-calculated fouling depth. 

**A thin layer of fouled ballast was located above the ties with a thick clean ballast layer 

beneath. 

A close examination of Table 6 reveals that 79 percent of GPR-calculated fouling depths 

show agreement or likely agreement with the ground truth. Four ground truth locations are 

where there is disagreement. At two of these locations there was a thin layer of heavily fouled 

ballast above the ties and a thick layer of very clean ballast beneath. The GPR calculated 

fouling depth didn’t accommodate the thin layer on top. Overall, the results are considered to 

be very reasonable. Personnel at BNSF railroad were pleased with the GPR data. 

An obvious correlation exists between percent moisture and fouling index. The higher the 

fouling index, the greater the capacity to store moisture. The calculated fouling depth is 

compared to the percent moisture in the samples at the ground truth locations in Figure 57. 

The correlation between calculated fouling depth and the first sample exhibiting moisture 

greater than 2 percent is clear. 

The percent moisture versus fouling index is plotted for all the ground truth samples in Figure 

58. Clearly, a trend in increasing moisture with increasing fouling index exists. Also, a wide 
70

range is occurring in moisture content, even at low fouling indexes. This is surprising. Recent 

ground truth data obtained from the Alaska Railroad (see section 5.5) and at TTC showed a 

moisture content that rarely exceeded 15 times the fouling index for fouling indexes greater 

than 20. For example, the maximum percent moisture between fouling indexes of 20 and 30 

did not exceed 4 percent at those other locations. On the Orin line, however, several samples 

with fouling indexes between 20 and 30 have percent moisture over 8 percent. Although 

compositional analyses were not performed on the BNSF samples, it is likely that the cause 

for this is the high moisture retention ability of coal dust (Tutumluer et al., 2008). 

Figure 57. Comparison between GPR-calculated fouling depth and percent moisture. 
71

Figure 58. Plot of percent moisture versus fouling index from all the ground truth 

samples. The sample points are color-coded based on the depth they were extracted. 

Bold symbols indicate samples extracted 1–2 days after rainfall (0.3 -0.5 in). 

The high moisture retention ability of coal dust and the observed sensitivity of the GPR to 

moisture content can be examined in relation to GPR data obtained under different moisture 

conditions. Figures 59 and 60 show GPR data obtained on track Main 2 between MP 37 and 

MP 39. The data in Figure 59 were collected before a rainfall and the data collected in Figure 

60 were obtained during a rainfall event. At the time of data collection (mid-afternoon on July 

26) most of the rainfall had already occurred. A noticeable increase in the calculated fouling 

extent and decrease in clean ballast thickness is observed in Figure 60 (during rainfall) 

compared to Figure 59 (pre-rainfall). A change in calculated fouling extent is also observed in 

areas with moderate to heavy fouling. Figure 61 shows a section of Main 2 track between MP 

44 and MP 46 that contains mostly heavily fouled ballast with pockets of clean ballast at 

MP44.0-44.15, MP 45.4–45.6 and MP45.8–46.0. The processed data shown in Figure 62 was 

obtained along the same section of track during the rainfall event. A pronounced increase in 

calculated fouling occurs over most of the section except for two locations corresponding to 

the cleanest calculated ballast—MP44–44.15 and MP45.846.0 (left track side in Figure 61 

corresponds to the right track side in Figure 62) . 

 

The preliminary conclusion regarding the effect of precipitation on the GPR-calculated 

fouling is that significant precipitation events generally result in a noticeable increase in 

calculated fouling except in some locations with the cleanest ballast. The increased moisture 

in the ballast layer results in greater signal attenuation that appears in the processed data as a 

decrease in clean ballast thickness. It is suspected that most of this effect is associated with 

the fact that coal dust is a major fouling material along the data collection route and coal dust 
72

has an anomalously high absorption capacity. This conclusion is supported by other recent 

data collected in Alaska and obtained under two different moisture conditions that didn’t 

show nearly this level of calculated fouling sensitivity to precipitation (see Figure 79 in 

Section 5.5.3). The average percent moisture versus fouling index ratio for the samples 

extracted from the Alaska Railroad track was significantly lower than the Orin Subdivision 

samples (see Figure 78 in Section 5.5.3). 

Figure 59. Processed 2 GHz data obtained between MP37 and 39 on track Main 2 before 

a rainfall event. 

Figure 60. Processed 2 GHz data obtained between MP 37 and 39 on track Main 2 

during rainfall event. 
73

Figure 61. Processed 2 GHz data obtained between MP 44 and 46 on track Main 2 

before a rainfall event. Data has been reversed relative to data collection direction. As a 

result the left track side data in this figure corresponds to the right track side data in 

Figure 62. 

Figure 62. Two GHz data obtained between MP 44 and 46 on track Main 2 during 

rainfall event. 
74

5.3.3.4 Five Hundred MHz Horn Antenna Data 

The 500 MHz data were processed differently than the 2 GHz data. Processing was performed 

to remove banding in the data associated with reflections from the rails and ties with minimal 

impact to the layer interface reflections. 

A layer reflection was often observed in the 500 MHz data at 10–25-inches in depth. This 

reflection corresponds to the ballast bottom. Areas where this reflection is most visible, often 

correspond to the cleanest ballast as shown in Figure 63, which compares 500 MHz data to 

processed 2GHz data between MP 50 and 49. The strong reflection between MP 50 and MP 

49 observed in the processed 500 MHz data corresponds to a thick layer of clean ballast at the 

shoulders calculated from the 2 GHz data. 
75

(a) Processed 500 MHz data on track Main 2 between MP 51 and MP 49. A relative 

permittivity of 4.0 was used to calculate depth. 

(b) Processed 2 GHz data on track Main 2 between MP 51 and MP 49. 

Figure 63. Processed GPR data between MP 51 and MP 49 on track Main 2: (a) 500 

MHz data and (b) 2 GHz data. A significant reflection from the bottom of the ballast in 

the 500 MHz data the correlates to a thick clean section of ballast calculated from the 

2 GHz data. 

At ground truth locations where the subballast thickness could be measured, typically a 

reflection occurred between the subballast and subgrade interface. Exceptionally clean ballast 

is observed at the ground truth location shown in Figure 64. A significant moisture contrast 

occurred between the ballast and subballast layers as shown in Figure 64(c). Apparently a 

negligible moisture contrast occurred between the subballast and subgrade layers at the 

location of the ground truth hole. The 500 MHz data, shown in Figure 65, contains reflections 

that are in good agreement with the locations of the layer interfaces mapped at the hole’s 

location. 
76

(a) Location of ground truth (b) Cross-section of hole

 (c) Plot of fouling index (red) and percent moisture (blue) for each ground truth sample. 

Visual Measurements:

Ballast thickness: 24” 

Coal dust in ballast 21-

24” 

Subballast thickness: 

11.5” 

Figure 64. Ground truth of hole dug on track Main 3 at MP 52 +286 ft. 
77

(a) Processed 500 MHz GPR data at core location. Note, ground truth hole dug 

on shoulder and 500 MHz data obtained between rails. 

(b) Interpreted 500 MHz GPR data at core location. Relative permittivities used for ballast 

and subballast layers were 4 and 6.5, respectively. 

Ground 

Truth 

Location 

Figure 65. Processed (a) and interpreted (b) 500 MHz GPR data at ground truth 

location on track Main 3 near MP 52. Mapped layers in (b) are in good agreement with 

the actual locations of the ballast-subballast and subballast-subgrade interfaces. 

Layer reflections were also detectable in areas where the surface was significantly fouled. 

Figure 66 shows the ground truth from a hole located on track Main 1 measured 1,420 ft south 
78

of the crossing at MP 37.8. The near-surface at this location was heavily fouled as shown in 

the pictures and the fouling index plot. 

            

(a) Ground truth location (b) Ground truth hole 

(c) Plot of fouling index (red) and percent moisture (blue) for each ground truth sample. 

Visual Measurements:

Ballast thickness: 18.5” 

Subballast thickness: 10” 

Figure 66. Ground truth hole dug on track Main 1 at the crossing at MP37.8 + 1,420 ft. 

The 500 MHz GPR data corresponding to ground truth in Figure 66 is shown in Figure 67. It 

is noteworthy that there was no discernable reflection from the ballast-subballast interface. 

This is not uncommon for scenarios where significant fouling occurs, which lowers the 

dielectric contrast at the interface between the ballast and subballast. The depth of the 

subballast-subgrade interface reflection interpreted from the GPR data, using a dielectric 

constant of 8.7 for the depth calculation, corresponds well with the location observed in the 

ground truth hole. 
79

(a) Processed 500 MHz GPR data at core location. Note, ground truth hole dug on 

shoulder and 500 MHz data obtained between rails. 

(b) Interpreted 500 MHz GPR data at core location. Relative permittivitiy used for ballast 

and subballast layers was 8.7. 

Ground 

Truth 

Location 

Ground 

Truth 

Location 

Figure 67. Processed (a) and interpreted (b) 500 MHz GPR data at ground truth 

location on track Main 1, crossing at MP 37.8 + 1,420 ft south. Mapped layers in (b) are 

in good agreement with the actual locations of the ballast-subballast and subballastsubgrade interfaces. 
80

Most of the 500 MHz reflections observed in the data from the Orin Subdivision were from 

the ballast-subballast and subballast-subgrade interfaces. Reflections from other, deeper, 

layers were not common. However, there was a segment on track Main 2 from MP 8 to MP 

14.2, where numerous reflections extending beyond 6 ft in depth were observed. A segment of 

processed data from MP 8 to MP 10 is shown in Figure 68. 

Figure 68. Processed 500 MHz data from MP 8.0–10.0 on track Main 2. Reflections from 

layers along this segment extend beyond 6 ft in depth. A relative permittivity of 6.25 was 

used to calculate depth from two-way travel time. 

5.3.4 Alaska Railroad, August 2007 

Ground penetrating radar (GPR) data were obtained on the Alaska Railroad between August 

13 and August 15, 2007. The data were obtained using a 500 MHz air-launched antenna 

centered between the rails and two 2 GHz horn antennas mounted on the shoulders. Ground 

truth in the form of hand-dug holes was obtained at 20 locations on the shoulder of the track. 

5.3.4.1 Data Collection 

Table 7 summarizes the GPR data collection. Significant rains fell in Anchorage the evening 

of August 14, 2007. Consequently, a recollection of data occurred along the stretch between 

MP 172–151 on August 15, 2007. Subsequent to the data collection, rainfall records were 

obtained from two private weather stations located within 4 mi of the track between MP 154 

and MP 160. The weather stations recorded 0.29 in and 0.50 in of rainfall the evening of 

August 14. 
81

Table 7. Data Collection Summary on Alaska Railroad 

Date Track MP Start MP End Total Miles (km) 

8/13/07 Main 115.2 142.0 26.8 (43.1) 

8/13/07 Siding 119.8 121.4 1.6 (2.6) 

8/13/07 Siding 128.0 129.0 1.0 (1.6) 

8/14/07 Main 203 162.2 40.8 (65.7) 

8/14/07 Main 157.7 151.4 6.3 (10.1) 

8/15/07 Main 172 151.5 20.5 (33.0) 

5.3.4.2 Ground Truth 

Ground truth holes were dug concurrent with the data collection. All ground truth holes were 

located near road crossings or other well-registered locations along the track that could be 

accurately correlated with the GPR data. Near these registered locations, specific ground truth 

locations were chosen for the purpose of sampling the widest range of ballast and subballast 

conditions based on visual inspection of the GPR data. Figure 69 shows a typical hand-dug 

ground truth hole. The aggregate was sampled at 6-inch depth increments. Two samples of 

aggregate of about 2–3 lbs weight were collected and triple-bagged in zip-lock bags at each 6-

inch depth interval. 

Additional ground truth in the form of frost heave locations and track geometry defects from 

geometry car data were obtained. The geometry car data were from the spring of 2008. Frost 

heave locations were identified by sections of the track where shims were added during the 

winter of 2008. These data were especially useful for providing a type of ground truth to 

compare with anomalous features observed in the 500 MHz data because the hand dug holes 

rarely exceeded 3.5 ft in depth. It is anticipated that track geometry defects, such as warping 

and buckling, should, to some degree, be the manifestation of some track structural problems 

arising from the subballast and/or subgrade. In those instances where the track geometry 

defects are indicative of substructure issues, the 500 MHz data would be useful to assess the 

characteristics of the problem, such as length and depth extent. The added knowledge gained 

would enable better maintenance planning, therefore resulting in savings to the railroad 

company. 
82

Figure 69. Typical ground truth hole. The depth of this hole relative to the top of the tie 

was 3 ft. 

The most distinctive feature observed in most ground truth holes was a layer of blue-gray 

stone varying from 18–24-inches deep. This stone layer typically yielded a lower fouling 

index and hence lower moisture content than the ballast above and subballast below. Figure 

70 shows a close-up of the stone layer from the hole shown in Figure 69. The corresponding 

moisture and fouling index profile is shown in Figure 71. The blue-gray layer corresponds to 

the depth range from 18–24 in (Figure 71). Figures 72 and 73 reveal a similar layered 

structure at another ground truth location. The blue-gray layer at that location was located 

from 12-18-inches deep. 
83

Figure 70. Layer of distinct blue-gray crushed stone that is typically near 24-inches deep 

at most ground truth locations. The moisture content of the subballast beneath this layer 

was substantially higher. 

Figure 71. Fouling index and moisture content profiles at 6-inch deep increments at the 

sample location corresponding to Figure 70. Sample associated with blue-gray layer is 

18–24-inches deep. 
84

Figure 72. Another ground truth location exhibiting the layer of blue-gray stone. 

Figure 73. Fouling index and moisture content profiles at 6–inch depth increments at the 

sample location corresponding to Figure 72. Sample associated with blue-gray layer is 

12–18-inches deep. 

5.3.4.3 2 GHz Horn Antenna Data 

The processed 2 GHz horn antenna data revealed that the ballast was fouled along most of the 

track over the data collection route. This was not unexpected because ballast cleaning is not 
85

performed regularly. Some short sections of track did contain clean ballast. Examples of 

processed data from clean and fouled ballast are shown in Figures 74 and 75, respectively. 

Figure 74. Processed data from the right shoulder from MP 115-117.25. The processed –

data indicates that the shoulder ballast is generally clean to depths 12 in or greater along 

this segment of track. 

Figure 75. Processed data from right shoulder from MP 203–201. The processed data 

indicates the shoulder ballast is generally fouled at depths within 2 in relative to the 

bottom of the ties. 

In Figure 76 the fouling depth obtained from GPR data analysis is compared with the fouling 

index for each sample depth at each ground truth location. Good agreement exists between the 

GPR-predicted fouling depth and the onset of significant fouling. 

Close examination of Figure 76 shows that eight out of 20 of the ground truth locations have 

significant fouling less than 12 in below the top of the tie. In seven out of those eight 
86

instances, the GPR-predicted fouling depth was less than 12 in. Likewise for the 12 instances 

where the onset of significant fouling is 30 cm (12 in) or more, the GPR-predicted fouling is 

greater than 25 cm (10 in) in seven cases and 22 cm (9 in) or greater in three out of the 

remaining five cases. There is good agreement between the GPR-predicted fouling depth and 

the onset of significant fouling. 

Figure 76. Comparison of the GPR predicted fouling depth and the fouling index 

calculated from sieve analysis of samples at 6-inch depth increments. 

Table 8 shows the GPR-accuracy assessment using the methodology introduced in Section 

5.4.3. There is 85 percent full and likely agreement between the GPR data and the ground 

truth. The similar level of agreement with the Orin Subdivision track is encouraging 

considering the huge differences between the track structure and maintenance history on the 

Alaska track versus the Orin Subdivision track. A great deal more moderate fouling occurred 

with fouling indexes between 10 and 20 from the Alaska railroad track versus the Orin 

Subdivision track. The moderate fouling attenuates the GPR-void scattering at a higher rate 

relative to clean ballast, resulting in shallower GPR-calculated clean ballast thicknesses. This 

is observed in Figure 76 where many of the GPR-calculated clean ballast thicknesses are in 

the 9-12-inch range because of the moderate fouling condition of the ballast. 

A correlation exists between percent moisture and fouling index. The higher the fouling index 

is, then the greater the capacity to store moisture. The calculated fouling depth is compared to 

the percent moisture in the samples at the ground truth locations in Figure 77. The correlation 
87

between calculated fouling depth and the first sample exhibiting moisture greater than 2 

percent is clear. 

Table 8. GPR accuracy related to ground truth fouling data 

Sample # Full 

Agreemenet 

Likely 

Agreement

Moderate 

Fouling 

Agreement

Thick 

Ballast 

Agreement

Disagreement

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Yes

Yes

Yes

 

Yes

 

 

 

 

 

 

Yes

Yes

Yes

 

-

-

-

-

 

 

 

 

Yes

 

 

Yes 

Yes 

 Yes 

 

 

 

Yes 

 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes

Yes

Yes
88

Figure 77. Comparison between GPR-calculated fouling depth and percent moisture. 

The percent moisture versus fouling index is plotted for all the ground truth samples in 

Figure 78. There is a clear trend in increasing moisture versus increasing fouling index. 
89

Figure 78. Plot of percent moisture compared to fouling index from all the Alaska 

ground truth samples. The sample points are color-coded based on the depth they were 

extracted. Bold symbols indicate samples extracted 1–2 days after rainfall between 0.3–

0.5 in. 

A fortuitous rainfall event provided the means to obtain repeat data over a section of track 

with different moisture conditions. Repeat data were collected along a 20-mile segment of 

track between MP 172–152 before and after a rainfall event likely between 0.3–0.5 in. Figure 

79 shows (a) processed data obtained prior to the rainfall and (b) processed data obtained after 

the rainfall event. The fact that the data are very similar indicates the method is robust to 

some change in moisture. Clearly, moisture in the ballast layer existed prior to the rainfall the 

evening of August 14. One of the local weather stations reported approximately 0.5 in of 

rainfall 10 days before data collection. (No data were available from the other weather 

station.) 
90

(a) Data obtained prior to rainfall between MP 159.4–156.4 

(b) Data obtained day following rainfall of 0.3 and 0.5 in between MP 159.4–156.4. 

Figure 79. Comparison of processed 2 GHz data before and the day after a rainfall 

estimated to be 0.3 and 0.5 inches 

5.3.4.4 500 MHz Horn Antenna Data 

A layer reflection was often observed in the 500 MHz data at 15–25-inches in depth. 

Comparison between the 500 MHz data and ground truth indicates this reflection is likely 

associated with a moisture increase at the bottom of the blue-gray stone layer. A good 

example of this layer reflection is shown in Figure 80. 
91

 (a) Picture of ground truth location (b) Sieve and moisture analyses 

of samples 

(c) Processed 500 MHz data. Note the strong reflection at 20–25-inches deep. 

Sample location (sample 

obtained outside edge of 

ties and 500 MHz data 

obtained between rails) 

Reflection likely associated 

with bottom of blue-gray 

crusted stone 

Figure 80. Comparison of 500 MHz data and ground truth at one location. 

The reflection between 15–25-inches in depth was generally most prominent in the data from 

MP 115–132, which corresponds to a section of track with generally clean ballast. This is 

anticipated because there would be a greater moisture contrast between clean ballast and 

subballast. 

Numerous sections of 500 MHz data that contained high subballast or subgrade reflections 

were at depths 3–7 ft. These high-amplitude reflections often are associated with a material 

change that traps moisture. Anomalous high-amplitude reflections can easily automatically be 

located in the data and exported to a Google Earth KML file. The highest amplitude 

reflections from MP 135–203 are shown in the Google Earth display in Figure 81. The yellow 

and red colors in the figure are indicative of high-amplitude zones. It is clear from the figure 
92

that the high-amplitude reflection zones are localized because most of the plotted amplitude 

data are blue. 

Figure 81. Automatically picked highest amplitude reflections between 30- and 75-inches 

deep. Color key: Blue < 67.5 dB, Green 64.5–70 dB, Yellow 70–72.5 dB, Red > 72.5 dB. 

The locations of frost heaves and track surface geometry defects are plotted along with the 

GPR amplitudes in Figure 82. The Google Earth interface permits the user to easily compare 

the track condition data with the high-amplitude reflection zones. Comparisons of the defect 

locations with GPR data are shown in Figures 83 and 84. In general, there is no definitive 

correlation between the location of the surface track geometry defects and anomalous high 

amplitude zones identified in the 500 MHz data. 

Two instances, where the frost heave locations correspond to local anomalous high-amplitude 

zones in the GPR data, are shown in Figures 85 and 86. The comparisons are favorable and 

suggestive that there may be a correlation between the anomalous reflection amplitudes and 

the frost heaves at these locations. However, there are many other high amplitude zones 

where no frost heaves exist. Likewise, there were frost heave locations without any significant 

high amplitudes in the 500 MHz data. At this time, the value of the 500 MHz horn antenna 

data for predicting frost heave locations cannot be determined. Much more data and ground 

truth would need to be obtained to make a more definitive conclusion. 
93

Figure 82. Locations of Surface track geometry defects (orange pushpins) and frost 

heaves (red pushpins) superimposed on top of the highest amplitude GPR reflections 

between 30- and 75-inches in depth. GPR Color key: Blue < 67.5 dB, Green 64.5-70 dB, 

Yellow 70-72.5 dB, Red > 72.5 dB. 
94

Figure 83. Locations of two surface track geometry defects (orange pushpins) near MP 

194 superimposed on top of the highest amplitude GPR reflections between 30- and 75-

inches deep (top), and corresponding 500 MHz GPR data (bottom middle) and 

processed 2 GHz data (bottom). 
95

Figure 84. Locations of two track geometry-surface defects (orange pushpins) between 

P 164 and 165 superimposed on top of the highest amplitude GPR reflections between 

0- and 75- inches deep (top) and , corresponding 500 MHz GPR data (bottom middle), 

and processed 2 GHz data (bottom). 

M

3
96

Figure 85. Location frost heave at MP 170.7 superimposed on top of the highest 

amplitude GPR reflections between 30- and 75-inches deep (top) and corresponding 500 

MHz GPR data (bottom). 
97

Figure 86. Location frost heave at MP 196.3 superimposed on top of the highest 

mplitude GPR reflections between 30- and 75-inches deep (top) and corresponding 500 

MHz GPR data (bottom). 

a
98

6. CURRENT HARDWARE AND SOFTWARE STATUS 

6.1. Equipment 

A fully functional GPR configuration has been established during this phase of the project. This 

configuration consists of off-the-shelf components. The GPR control unit and antennas are 

standard GSSI equipment. Mounting hardware has also been developed during the course of the 

project. The two 2 GHz horn antennas are mounted approximately 6 in beyond the end of the ties 

on this fixed setup. This setup does not conform to Plate C specifications, which require a 

clearance height of approximately 24 in above the top of the ties. In the future it may be 

desirable to design a setup that accommodates obstacles that impede the antenna. It was found 

that the clearance requirement was generally not a problem for a hi-rail vehicle traveling about 

25 mph on the Orin Subdivision and Alaska Railroad tracks. However, the passenger platforms 

along the Amtrak railroad were frequently located so that one of the outer antennas had to be 

moved to prevent it from being damaged while the hi-rail vehicle traversed the length of the 

platforms. 

6.2. Data Collection Rate 

Data collected during the project was obtained at scan densities no less than 4 scans/ft. at speeds 

up to 25mph, which was possible by setting the transmit rate of the control unit to 300 KHz. 

Current FCC regulations require that the transmit rates of equipment manufactured for sale in the 

United States to be limited to 100 KHz. This means that maximum scan density of purchased 

equipment traveling at 25 mph is 1.3 scans/ft. This data density is sufficient for network level 

surveys. All of the large-scale processed data presented in this report was first decimated to 0.5 

scans/ft prior to processing. For detailed high-resolution surveys with a data collection density of 

4 scans/ft, the maximum speed will be approximately 8 mph. Table 9 summarizes these 

parameters. 

Figure 87. Current ballast evaluation survey types and maximum data collection speeds. 

Survey Type Data Density (scans/ft) Maximum Speed (MPH) 

High resolution 4 8 

Network level 1.3 25 

Network level 0.5 65 

 

6.3. Sensitivity to External Noise 

Current FCC regulations limit the transmit power of the 2 GHz horn antennas. The data from 2 

GHz antennas meeting this power limit were significantly impacted by external radio frequency 

(RF) interference at the Amtrak data collection site. Recently, GSSI has developed a RF filter 

that removes frequency modulation (known as FM) interference from the data. This filter is 

currently being evaluated. 
99

6.4. Data Collection Software 

Standard data collection software is used to obtain the GPR data. No antenna calibration is 

required. Data collection settings are stored on the control unit. Thus, the data collection is 

simple to initiate and needs no user input during data collection. 

6.5. Data Post-Processing Software 

Data post-processing can be performed on the same unit as data collection. The data processing 

is integrated in GSSI’s data post-processing software, RADAN. All of the 2 GHz horn antenna 

data processing steps have been combined into a single macro which can be run by pressing a 

single button. The outputs from the data processing are the color-coded data shown in this report 

and an ASCII comma separated variable (CSV) file. There are no user options or adjustable 

parameters. Every dataset is processed in precisely the same way. The data post-processing 

software is commercially sold as the BallastVue Module. 

RADAN can also be used to process the 500 MHz antenna data. Some processing steps, such as 

mapping the anomalous high reflection amplitude areas, can be performed automatically. 

Detailed layer mapping can be performed in an interactive manner. 
100

7. Summary and Findings 

An investigation of railroad ballast using recently developed 2 GHz horn antennas has yielded 

data that contain scattering from the void space between individual ballast aggregate. A simple 

GPR data processing approach was introduced to yield scattering amplitude envelopes. 

Comparison of the processed GPR data to ground truth samples collected from cross trenches, 

locations of surface splashing, and undercutting records revealed good agreement between the 

characteristics of the scattering amplitude envelope and ballast distress. 

The color-coded processed data provides a very intuitive data display for minimally trained 

users. Ballast characterization can be achieved on both large and small scales. The extent of 

recently undercut ballast is clearly observed from the processed network-level data. Likewise, 

very localized ballast fouling, extending over several ties, can be observed from high-resolution 

data obtained at slow speeds. 

Two key indications of distressed ballast are easily identified from this type of display focusing 

on the area near the bottom of the ties: (1) very low amplitude areas indicated by red and black 

colors, and (2) high-amplitude reflections indicated by blue areas. The former corresponds to 

severely fouled ballast, and the latter corresponds to a significant reflecting boundary between 

clean ballast and ponding water or fouled ballast. 

GPR and ground truth data from 382 mi of track are presented in this report. Table 10 

summarizes the data locations and ground truth. The data were obtained on five tracks with 

different substructure conditions. In Colorado, GPR data were obtained in a dry environment 

with stable, sandy subgrade. Data from the northeastern United States were obtained in an area 

with a shallow water table and unstable subgrade. Data from Wyoming were obtained on 

relatively stable sandy soil and much of the data from Nebraska were obtained on track placed 

over expansive clays. The track in Alaska was susceptible to frost heaves. The 2 GHz data 

processing approach developed from analysis of the Colorado data was applied to the other 

datasets and yielded very good correlation with available ground truth. 

This developed methodology for ballast fouling assessment presents a feasible approach for 

routine inspection of working railroads and is especially suited to assess the ballast fouling 

condition near the bottom of railroad ties. 
101

Table 9. Summary of data collection areas and ground truth. 

Location Miles Subgrade Samples and # Mudspots Records of 

Data Type Cross Trenches Undercutting 

And Shoulder cleaning

(miles) 

TTC 15 Sand 15 0 - 

Providence, RI – 29.5 Sand/Clay 0 5 29.5 

Sharon, MA 

West Bill, WY- 75 Sand 5 260 21.9 

Shawnee Junction, 

WY 

Crawford, NE- 34.9 Expansive Clay 0 54 0.55 

Ardmore, SD 

Gillette Area, 131 Sand 20 - - 

Wyoming 

Alaska Railroad 97 Sand/Clay 20 - - 

The 500 MHz horn antenna data were useful for mapping reflections that were associated with 

subballast and subgrade layering. Anomalous high amplitude zones between 2.5 and 6.25 ft in 

depth were automatically mapped and could be exported to Google Earth for quick, efficient 

analysis. The geometry car-detected surface defects track geometry defect locations and frost 

heave locations were compared to the anomalous high amplitude zones in the Alaska Data. No 

definitive correlation was observed between the high amplitude zones in the 500 MHz GPR data 

and the surface defects. Examination of the ballast fouling condition on the shoulders calculated 

from the 2 GHz antenna data at numerous defect locations indicates considerable shallow 

fouling. The ballast fouling condition likely may contribute to some of the observed track 

geometry defects. 

Some correlation was observed between several frost heave locations and high amplitude 

reflections. At other locations there were high amplitude reflections but no frost heaves. 

Consequently, at this time, it can only be inferred that in some subset of the frost heaves, there is 

a relationship with high amplitude reflections in the 2.5–6.25 ft depth interval in the 500 MHz 

data. 
102

8. Recommendations for Future Research and Development 

The evolution of the 2 GHz horn antenna data processing methodology for ballast fouling 

assessment has led to a commercial product. The accuracy of the method has been validated 

on more than 382 mi of track. Industry response to the use of GPR data for ballast condition 

assessment has indicated the current method provides useful information for classifying 

ballast fouling. The technique is efficient and should provide the industry with considerable 

cost savings by enabling railroad companies to locate and prioritize sections of track for 

scheduled undercutting and shoulder cleaning. The fact that automatic data processing is 

performed on the data is a very marketable feature. The data can be obtained and processed by 

minimally trained users. From a GPR manufacturer perspective, the product has achieved a 

very sellable level. Enhancing the visibility of the method in the industry should be a priority. 

The 500 MHz antenna clearly is useful for mapping strong layer reflections. This was 

demonstrated in Wyoming, where the subballast and subgrade interfaces were mapped. On 

the Alaska Railroad, several of the anomalous high amplitude areas correlated to frost heave 

locations. The data provided by the 500 MHz antenna may, therefore, be useful in certain 

situations. Anomalous reflection amplitudes can be mapped automatically. The mapping of 

layer reflections in general, however, requires a highly trained user. This drives up the 

expense of the method and discourages end users from purchasing the product. The usefulness 

of the 500 MHz antenna will likely be evident over time as the information provided by the 

antenna will help in planning and prioritizing track maintenance. 

It is recommended that future research and development be focused on the attainment of a 

large quantity of high quality 2 GHz and 500 MHz GPR data concurrent with data from other 

instrumentation typically used to assess track structural quality. The resultant combined 

dataset will achieve numerous objectives including, but not limited to: 

• Providing the necessary information to make an unbiased assessment of the 

relationship between subsurface features characterized with the 500 MHz GPR and 

track structural quality; 

• Enabling a better understanding of the cost-benefit analysis of using GPR; and 

• Quantifying, to a greater extent, the relation between ballast fouling and track 

structural problems. 
103

Acknowledgments 

The authors gratefully acknowledge the very helpful assistance of the staff at TTC during data 

collection and cross-trenching. The generous assistance of numerous individuals at Amtrak is 

appreciated, especially Michael Trosino, who coordinated the GPR data collection and gathered 

the ground truth. Hank Lees and Mark Meyer of BNSF provided invaluable help during data 

collection and ground truth verification. Special thanks to Steve Pfeiffer and Lloyd Tesch of the 

Alaska Railroad who coordinated the data collection and ground truth effort in Alaska. The 

ground truth samples were analyzed for moisture content and aggregate size distribution by Zach 

Dombrow and Hai Huang from the University of Illinois. The authors are also grateful to Dr. 

Ted Sussmann of the Volpe National Transportation System Center for his guidance during the 

course of the project and Mr. Mahmood Fateh of FRA, who served as the Contracting Officer’s 

Technical Representative for this project. 

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106

Acronyms 

BNSF Burlington Northern Santa Fe 

CBR California bearing ratio 

DCP dynamic cone penetrometer 

DMI distance measuring instrument 

FM frequency modulation 

EM electromagnetic 

FCC Federal Communication Commission 

FRA Federal Railroad Administration 

GPR ground penetrating radar 

GPS global positioning system 

GSSI Geophysical Survey Systems Incorporated 

HTL high-tonnage loop (at TTC) 

LED light-emitting diode 

MP Milepost 

RFI radio frequency interference 

STFT Short-Time Fourier Transform 

TTC Transportation Technology Center