Geoscience Reference
In-Depth Information
23.3.1 f
i n d i n g
t h e
o
P t i M a l
o
f f s e t
We acquired several CMP surveys at the beginning of the March field experiment to determine
the optimal offset to collect the WOR data (Figure 23.2). The optimal offset was based on the time
separation of the air and ground waves. We wanted to avoid interference between the two phases
so we could accurately pick the ground wave arrival time and amplitude. If the separation is too
close, the air wave will interfere with the later arriving ground wave and potentially cause a mis-
interpretation of the arrival time and the amplitude. From the March CMPs, we chose an optimal
antenna separation of 3.5 m. The character of the GPR data changed substantially during the May
and September field experiments. Fortunately, the 3.5 m offset still allowed picking of the ground
wave. Attenuation of the wave's amplitude due to larger separation would have made picking the
ground wave unreliable.
Processing of the GPR data consisted of a few standard procedures. We removed low-frequency
noise due to the electronics in the radar unit from the data. For the CMP analysis, we bandpass
filtered the data between 25 and 200 MHz to increase the signal-to-noise ratio of the arrivals. The
WOR data were not filtered. For plotting the images, we used a gain function to emphasize the later
arriving events (AGC) with a 25 ns window. Figure 23.2 compares CMPs from the same location
for the four acquisition dates. For the March and January data, the ground wave is a strong event.
The May data shows a weaker, less-extensive ground wave. (We have displayed the highest-quality
CMP survey from the May profiles). The September CMPs show weak ground wave arrivals. In
the March and January CMPs, the ground wave projects to arrive at 10 m antenna separation at
approximately the same time (~86 ns for March; ~88 ns for January). Thus, their slopes are nearly
equal, indicating that the EM velocity of the ground wave is about the same for each date. In May,
the ground wave projects to arrive at ~75 ns at 10 m antenna separation. This earlier arrival time
indicates that the EM velocity is faster in May.
23.3.2
w
i d e
-o
f f s e t
s
u R v e y s
The GPR data were acquired with the same parameters for each survey, except the stacking change
mentioned earlier. Each survey was started with a walkaway to help reliably identify the air and
ground waves (Figure 23.4). By starting the survey with the walkaway geometry, we can more
confidently pick the air and ground waves from the slope and the time intercept of the phases
(Figure 23.5 and Figure 23.6). In GPR data, the input waveform has a central, large amplitude peak
flanked by two smaller peaks. We picked the central peak for analysis. In addition, the air wave has
the opposite polarity from the ground wave providing more confidence in the event picking (Du and
Rummel, 1994). Thus, in the presented data, the air wave has a negative (white) amplitude, whereas
the ground wave has a positive (black) amplitude. The surface spatial location of these events is
known, so we can reference our findings to the ground location.
Two aspects of the character of the WOR GPR surveys are easily observed in Figure 23.5 and
Figure 23.6. First, the ground wave is a strong-amplitude, coherent event in the March and January
data. The ground wave in the May and September data is weaker in amplitude, more difficult to see,
and not as coherent. Fortunately, the walkaway start of each survey makes picking the ground wave
more reliable. Second, the arrival time of the ground wave varies between about 35 and 40 ns in
the March and January data but occurs at about 30 ns in the May and September data. The earlier
ground wave arrival indicates that the EM velocity is faster in May and September compared to
March and January.
The GPR data indicate changes in the radar response along the profile and throughout the year.
The changes in radar response along the profile are indicative of spatial differences in water content.
Because of the specifications used to minimize variability in the silt-loam layer, spatial differences
in moisture are mostly due to differences in evapotranspiration. Temporal changes in the radar char-
acter are due to changes in soil moisture content resulting from decreasing precipitation and high
