Basic GPR data processing


Note:

Table of Contents

Objectives of this tutorial

Note that his tutorial will not explain you the math/algorithms behind the different processing methods.

Preliminary

File organisation

I suggest to organise your files and directories as follows:

/2014_04_25_frenke   (project directory with date and location)
    /processing      (here you will save the processed GPR files)
    /rawGPR          (the raw GPR data, never modify them!)
    RGPR_tutorial.R  (this is you R script for this tutorial)

Install/load RGPR and set the working directory

Getting help

If you need help about a specific function, check the documentation with either help("FUNCTION_NAME") of ?FUNCTION_NAME, example:

?traceStat

Read GPR data

The raw GPR data are located in the directory /rawGPR. The data format is the Sensors & Software format. Each GPR data consists of

To read the GPR data, enter

x <- readGPR(dsn = "rawGPR/LINE00.DT1")   # the filepath is case sensitive!
class(x)
## [1] "GPR"
## attr(,"package")
## [1] "RGPR"

Basic processing steps

First wave break and time zero estimation

Here, we define time-zero, $t_0$ as the time at which the transmitter starts to emit the wave.

Maybe is time zero not correctly set. To get the time-zero for each traces of x use the function time0():

time0(x)

The first wave break, $t_{\mathrm{fb}}$, is estimated for each traces (it is the time of the first wave record) with firstBreak():

tfb <- firstBreak(x, w = 20, method = "coppens", thr = 0.05)
plot(pos(x), tfb, pch = 20, ylab = "first wave break",
     xlab = "position (m)")

plot first wave break time

Convert the first wave break time $t_{\mathrm{fb}}$ into time-zero $t_0$ with firstBreakToTime0().

Here we define

where $a$ is the distance between the transmitter and receiver and $c_0$ is the wave velocity in the media between the transmitter and receiver (in our case, air). The value $a/c_0$ corresponds to the wave travel time from the transmitter to the receiver.

t0 <- firstBreakToTime0(tfb, x)
time0(x) <- t0     # set time0

Note that:

Check the results (do you see the difference between time zero in red and first wave break time in blue?):

plot(x[, 15], xlim = c(0, 100))  # plot the 15th trace of the GPR-line
abline(v = tfb[15], col = "blue")  # first wave break time

plot single trace with time0 and first wave break time

You can apply at once all the previous steps (first wave break estimation + set time-zero) with the function estimateTime0() (which has the same arguments as the functions firstBreak(), firstBreakToTime0() plus an extra argument - FUN - for function to apply to the estimated time-zero, e.g. mean(); see the documentation), i.e.:

x <- estimateTime0(x, w = 20, method = "coppens", thr = 0.05, FUN = mean)

DC shift removal

Plot a single trace:

plot(x[, 15])  # plot the 15th trace of the GPR-line

plot single trace

Notice how the trace samples before the first wave arrival (before $t = 0\,ns$) are slightly shifted below $0\,mV$? This shift is called direct current offset (DC-offset) and you will remove it from the data. The direct current offset is estimated on trace samples before time-zero.

  1. Determine which samples will be used to estimate the direct current offset (i.e., the samples before the first wave arrival). Identify the samples before $t = 0\,ns$ by ploting the first $n$ samples of the traces. For example, for $n = 110$:

    # plot the first 110 samples of the 15th trace of the GPR profile
    plot(x[1:110, 15])
    

plot single trace, first 110 samples

You can visualise the DC-offset on the trace plot by adding an horizontal lines (abline(h=...)) with the argument h equal the DC-offset, i.e., the mean of the first $110$ samples (mean(x[1:110,15]):

```r
plot(x[, 15])  # plot the 15th trace of the GPR-line
# add a green horizontal line
abline(h = mean(x[1:110, 15]), col = "green")
```

plot single trace + dc-shift

  1. Remove the DC-offset estimated on the first n samples usind the function dcshift(). This function takes as argument the GPR object and the sample index used to estimate the DC shift (in this case, the first $110$ samples):

    x1 <- dcshift(x, u = 1:110)   # new object x1
    
    ## [1] 21
    

Have a look at x1:

x1
## *** Class GPR ***
##  name        = LINE00
##  filepath    = rawGPR/LINE00.DT1
##  1 fiducial(s)
##  description =
##  survey date = 2014-04-25
##  Reflection, 100 MHz, Window length = 399.6 ns, dz = 0.4 ns
##  223 traces, 55.5 m
##  > PROCESSING
##    1. time0<-
##    2. dcshift//u=1:110
##  ****************

Compared with x or print(x), three additional lines are displayed. The two last line show the applied processing step:

Each time a GPR object is processed with a function, the name of the function as well as some of its arguments are stored in the GPR object. This enables to track the data processing, i.e., to know exactly which processing steps where applied to the data. This is a first step toward reproducible research.

The processing steps can be extracted with the function processing():

proc(x1)
## [1] "time0<-"          "dcshift//u=1:110"

Time zero correction

To shift the traces to time-zero, use the function time0Cor (the method argument defines the type of interpolation method)

x2 <- time0Cor(x1, method = "pchip")
## [1] 21
plot(x2)

plot after time0Cor()

Dewow

Remove the low-frequency components (the so-called “wow”) of the GPR record using:

  1. a running median filter (type = "runnmed")
  2. a running mean filter (type = "runmean")
  3. a Gaussian filter (type = "Gaussian")

For the two first cases, the argument w is the length of the filter in time units. For the Gaussian filter, w is the standard deviation.

x3 <- dewow(x2, type = "runmed", w = 50)     # dewowing:
## [1] 21
plot(x3)                                     # plot the result

plot after dewow

Can you see the difference with x1? Plot x3 - x2 to see the removed “wow”.

plot(x3 - x2)                           # plot the difference

plot difference after dewow

See the dewowing by comparing the traces before (blue line) and after (red line):

plot(x2[,15], col = "blue")      # before dewowing
lines(x3[,15], col = "red")      # after dewowing

plot single trace dewow

Frequency filter

Let’s have a look at the amplitude-frequency and phase-frequency plot (the spectrum given by the Fourier decomposition):

spec(x3)

plot spectrum

The curve in red is the averaged amplitude/phase over all the trace amplitudes/phases.

On the first plot, notice

Eliminate the high-frequency (noise) component of the GPR record with a bandpass filter. We define as corner frequencies at $150\,\mathit{MHz}$ and $260\,\mathit{MHz}$, and set plotSpec = TRUE to plot the spectrum with the signal, the filtered signal and the filter.

x4 <- fFilter(x3, f = c(150, 260), type = "low", plotSpec = TRUE)

plot frequency filter

## [1] 21
plot(x4)

plot frequency filter

Let see the difference

plot(x4 - x3, clip = 50)

plot difference

Ideally, the objective of processing is to remove the noise component without altering the signal component to improve the signal/noise ratio. When ploting the difference in processing (after - before), one should only observe the noise that is filtered out. Here, removing and attenuating some frequencies change the signal amplitude.

Amplitude gain

Apply a gain to compensate the signal attenuation. Three types of gain are available:

We will first apply a power gain and then an exponential gain. To visualise the amplitude envelope of the GPR signal as a function of time, use the function plotAmpl() as follows:

# compute the trace amplitude envelopes (with Hilbert transform)
x4_env <- envelope(x4)
## [1] 21
# plot all the trace amplitude envelopes as a function of time
trPlot(x4_env, log = "y", col = rgb(0.2,0.2,0.2,7/100))
# plot the log average amplitude envelope
lines(traceStat(x4_env), log = "y", col = "red", lwd = 2)

plot amplitude

## [1] 22

On the plot above, there is a sharp increase of the amplitude envelope at the begining of the signal. This sharp increase corresponds to the first wave arrival. Then the amplitude decreases until about $220\,\mathit{ns}$.

Power gain

From $0\,\mathit{ns}$ to $20\,\mathit{ns}$ the power gain is set equal to the gain at $20\,\mathit{ns}$, i.e., $x_g(20)$ (constant value, tcst = 20). The gain is only applied up to $220\,\mathit{ns}$.

x5 <- gain(x4, type = "power", alpha = 1, te = 220, tcst = 20)
## [1] 21

Compare the amplitude before (red) and after (green) the power gain:

plot(traceStat(x4_env), log = "y", col = "red", lwd = 2)
## [1] 22
lines(traceStat(envelope(x5)), log = "y", col = "green", lwd = 2)

plot amplitude after power gain

## [1] 23
## [1] 22
plot(x5)      # how does it look after the gain?

plot amplitude after power gain

Exponential gain

Ideally, the parameter $\alpha$ in the exponential gain should be close to the slope of the log amplitude decrease. This slope could be estimated by fitting a straight line to the amplitude decrease. After some trials, we apply the exponential gain only between $0\,\mathit{ns}$ (t0) and $125\,\mathit{ns}$ (te for $t_\mathit{end}$):

x6 <- gain(x5, type ="exp",  alpha = 0.2, t0 = 0, te = 125)
## [1] 21
plot(traceStat(envelope(x6)), log = "y", col = "blue", lwd = 2)
## [1] 23
## [1] 22

plot ampliude exponential gain

Oops! Set alpha to a smaller value!

x6 <- gain(x5, type = "exp", alpha = 0.11, t0 = 0, te = 125)
## [1] 21
plot(traceStat(x4_env), log = "y", col = "red", lwd = 2)
## [1] 22
lines(traceStat(envelope(x5)), log = "y", col = "green", lwd = 2)
## [1] 23
## [1] 22
lines(traceStat(envelope(x6)), log = "y", col = "blue", lwd = 2)

plot amplitude after exponential gain

## [1] 23
## [1] 22
plot(x6)    # how does it look after the gain?

plot amplitude after exponential gain

inverse normal transformations

Have a look at the histogram of the values of x6

hist(x6[], breaks = 50)

plot hist

This histogram is very narrow, meaning that a lot of values are very close to zero and therefore many details are not really visible. To widen this histogram, we can transform it to make it more normally distributed with a rank-based inverse normal transformation:

x7 <- traceScaling(x6, type = "invNormal")
## [1] 21

Histograms before and after

par(mfrow = c(1, 2))
hist(x6[], breaks = 50)
hist(x7[], breaks = 50)

plot histogram comparison Have a look at the results of the transformation:

plot(x7)

plot inverse normal transformation results

Median filter (spatial filter)

A non-linear filter to remove noise:

x8 <- filter2D(x7, type = "median3x3")
## [1] 21
plot(x8)

plot median filter

Let see the difference

plot(x8 - x7)

plot difference after median filter

Frequency-wavenumber filter (f-k-filter)

The function spec() with the argument type = "f-k returns a list containing the frequencies (f), the wavenumbers (k), the amplitude of the GPR data.

FKSpec <- spec(x8, type = "f-k")
area <- list(x = c(0, min(FKSpec$wnb), min(FKSpec$wnb), max(FKSpec$wnb), max(FKSpec$wnb), 0),
             y = c(max(FKSpec$fre), 800, 0, 0, 800, max(FKSpec$fre)))
lines(area, type="o")

plot fk-filter

x9 <- fkFilter(x8, fk = area)
## [1] 21

With the f-k-filter you can successfully remove the artifacts but still the information gained is very small in this case (the quality of the raw GPR data is already bad):

plot(x9, clip = 50)

plot fk-spectrum

spec(x9, type = "f-k")

plot fk-spectrum

Let see the difference

plot(x9 - x8)

plot difference after fk-filter

Processing overview

Let review the processing step applied on the GPR record:

proc(x9)

Other processing functions

Trace average removal

Check the help page for the function traceStat

?traceStat
x10 <- traceStat(x9, FUN = mean)  # compute average trace of all traces
x10 <- traceStat(x9, FUN = median)  # compute average trace of all traces
x10 <- traceStat(x9, FUN = median)  # compute average trace of all traces
# compute windowed average trace (average of 20 traces)
x10 <- traceStat(x9, w = 20, FUN = median)

plot(x10)

Eigen Image Filter

?eigenFilter

# remove first eigenimage = keep all except the first one
plot(eigenFilter(x9, eigenvalue = c(2:ncol(x1))))
plot(eigenFilter(x9, eigenvalue = 1)) # the removed eigenvalue

# remove the first two eigenimages
plot(eigenFilter(x9, eigenvalue = c(3:ncol(x1))))
plot(eigenFilter(x9, eigenvalue = 1:2)) # the removed eigenvalue

Background matrix substraction

See See Rashed and Harbi (2014) Background matrix subtraction (BMS): A novel background removal algorithm for GPR data doi: 10.1016/j.jappgeo.2014.04.022

It is a slow function!

?backgroundSub

x10 <- backgroundSub(x9, width = 21, trim = 0.2, s = 1, eps = 1, itmax = 5)
plot(x10)
plot(x10 - x9)

Save and export

Save the processed GPR record into the directory /processing. Use the .rds format (this is a R internal format)

writeGPR(x9, fPath = file.path(getwd(), "processing", paste0(name(x9), ".rds")),
         format = "rds", overwrite = TRUE)
## *** Class GPR ***
##  name        = LINE00
##  filepath    = /mnt/data/huber/Documents/WORKNEW/GPR_Project/RGPR-gh-pages/2014_04_25_frenke/processing/LINE00.dt1
##  1 fiducial(s)
##  description =
##  survey date = 2014-04-25
##  Reflection, 100 MHz, Window length = 351.6 ns, dz = 0.4 ns
##  223 traces, 55.5 m
##  > PROCESSING
##    1. time0<-
##    2. dcshift//u=1:110
##    3. time0Cor//method=pchip
##    4. dewow//type=runmed+w=50
##    5. fFilter//f=150,260+type=low+plotSpec=TRUE
##    6. gain//type=power+alpha=1+te=220+tcst=20
##    7. gain//type=exp+alpha=0.11+t0=0+te=125
##    8. traceScaling//type=invNormal
##    9. filter2D//type=median3x3
##    10. fkFilter//fk=x<-c(0, -2, -2, 2, 2, 0),y<-c(1250, 800, 0, 0, 800, 1250)
##  ****************

Export a high quality PDF:

plot(x9, type = "wiggles", clip = 30, pdfName = file.path(getwd(), "processing", name(x9)),
          lwd = 0.5, wsize = 2.5)

Read the saved GPR data

procA <- readGPR(fPath = file.path(getwd(), "processing", paste0(name(x9), ".rds")))

Some final thoughts

Warning: processing can introduce artifacts in the data and lead to wrong interpretations.


What really matters is that the final interpretation is valid, and although processing is important, ultimately, the key to good data interpretation is good data collection in the first place. in Cassidy (2009) Chapter 5 - Ground Penetrating Radar Data Processing, Modelling and Analysis, In Ground Penetrating Radar Theory and Applications, (Eds Harry M. Jol,), Elsevier, Amsterdam, pp: 141-176, ISBN 9780444533487.


A good practical mantra for most users to adopt is if it cannot be seen in the raw data – is it really there? As such, processing steps should be used to improve the raw-data quality, therefore, making interpretation easier. In practice, this means increasing the signal-to-noise ratio of coherent responses and presenting the data in a format that reflects the subsurface conditions accurately. in Cassidy (2009) Chapter 5 - Ground Penetrating Radar Data Processing, Modelling and Analysis, In Ground Penetrating Radar Theory and Applications, (Eds Harry M. Jol,), Elsevier, Amsterdam, pp: 141-176, ISBN 9780444533487.


Processing of GPR data tends to improve the appearance of data, but rarely does processing substantially change the interpretation. in Daniels et al. (1997) Coincident Antenna Three-Dimensional GPR. Journal of Environmental and Engineering Geophysics, Vol. 2, No.1, pp. 1–9.