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Impact of Data Processing and Antenna Frequency on Spatial Structure Modelling of GPR Data.

De Benedetto D, Quarto R, Castrignanò A, Palumbo DA - Sensors (Basel) (2015)

Bottom Line: The results showed that the type and range of spatial structures of GPR data did not depend on data processing at a given frequency.It was also evident that the noise tended to decrease with the complexity of processing, then the most error-effective procedure was selected.The results highlight the critical importance of the antenna frequency and of the spatial scale of soil/subsoil processes being investigated.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Scienze della Terra e Geoambientali, University of Bari, Aldo Moro, Bari 70125, Italy. daniela.debenedetto@entecra.it.

ABSTRACT
Over the last few years high-resolution geophysical techniques, in particular ground-penetrating radar (GPR), have been used in agricultural applications for assessing soil water content variation in a non-invasive way. However, the wide use of GPR is greatly limited by the data processing complexity. In this paper, a quantitative analysis of GPR data is proposed. The data were collected with 250, 600 and 1600 MHz antennas in a gravelly soil located in south-eastern Italy. The objectives were: (1) to investigate the impact of data processing on radar signals; (2) to select a quick, efficient and error-effective data processing for detecting subsurface features; (3) to examine the response of GPR as a function of operating frequency, by using statistical and geostatistical techniques. Six data processing sequences with an increasing level of complexity were applied. The results showed that the type and range of spatial structures of GPR data did not depend on data processing at a given frequency. It was also evident that the noise tended to decrease with the complexity of processing, then the most error-effective procedure was selected. The results highlight the critical importance of the antenna frequency and of the spatial scale of soil/subsoil processes being investigated.

No MeSH data available.


A part of GPR common-offset radar section acquired along a N–S oriented profile (Profile 14) with the 1600 MHz antenna. The depth is represented by time and the green line represented a discontinuity.
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sensors-15-16430-f003: A part of GPR common-offset radar section acquired along a N–S oriented profile (Profile 14) with the 1600 MHz antenna. The depth is represented by time and the green line represented a discontinuity.

Mentions: From the visual inspection of GPR radar sections and CMP data, three main layers were synthetically disclosed: a first layer at time ranging between about 3 ns to about 5 ns (0.09–0.15 m depth, considering a velocity of 0.06 mns−1), visible only in the radar sections acquired with 1600 MHz frequency (an example is reported Figure 3), a second layer between 10 ns and 14 ns (0.3–0.4 m depth with a velocity of 0.06 mns−1) and a third layer between 20 ns and 22 ns (0.6–0.66 m depth with an average velocity of 0.1 mns−1), detectable from all the radar sections acquired with 250 and 600 MHz frequencies and from CMP data (example of CMP data is reported in Figure 4). For convenience these reflections are referred to as the “first”, “second” and “third” reflection, respectively. The “first” and “second” reflected layers may be related to interfaces in the soil, probably due to shallow ploughing or soil compaction caused by tractor passage and/or tillage. Conversely, the “third” reflected layer was ascribed to the soil-bedrock interface because of its wide amplitude, denoting a strong electromagnetic contrast, and on the basis of pre-existing pedological profile (Figure 1b). The bedrock reflection was generally characterized by marked roughness (more or less evident in different parts of the site) and many anomalies of various types (hyperbolic signals) were observed in the overlying soil layer. The radar sections of the different antennas showed varying features in terms of resolution, and it was preferred not to select only one antenna because all antennas jointly captured the scale-dependent variation of soil/subsoil. Moreover, the propagation velocity was equal to 0.06 mns−1 up to 10 ns and for longer times the average velocity was 0.1 mns−1, assuming a subsoil model with horizontal stratification and constant lateral velocity (Figure 4b).


Impact of Data Processing and Antenna Frequency on Spatial Structure Modelling of GPR Data.

De Benedetto D, Quarto R, Castrignanò A, Palumbo DA - Sensors (Basel) (2015)

A part of GPR common-offset radar section acquired along a N–S oriented profile (Profile 14) with the 1600 MHz antenna. The depth is represented by time and the green line represented a discontinuity.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541886&req=5

sensors-15-16430-f003: A part of GPR common-offset radar section acquired along a N–S oriented profile (Profile 14) with the 1600 MHz antenna. The depth is represented by time and the green line represented a discontinuity.
Mentions: From the visual inspection of GPR radar sections and CMP data, three main layers were synthetically disclosed: a first layer at time ranging between about 3 ns to about 5 ns (0.09–0.15 m depth, considering a velocity of 0.06 mns−1), visible only in the radar sections acquired with 1600 MHz frequency (an example is reported Figure 3), a second layer between 10 ns and 14 ns (0.3–0.4 m depth with a velocity of 0.06 mns−1) and a third layer between 20 ns and 22 ns (0.6–0.66 m depth with an average velocity of 0.1 mns−1), detectable from all the radar sections acquired with 250 and 600 MHz frequencies and from CMP data (example of CMP data is reported in Figure 4). For convenience these reflections are referred to as the “first”, “second” and “third” reflection, respectively. The “first” and “second” reflected layers may be related to interfaces in the soil, probably due to shallow ploughing or soil compaction caused by tractor passage and/or tillage. Conversely, the “third” reflected layer was ascribed to the soil-bedrock interface because of its wide amplitude, denoting a strong electromagnetic contrast, and on the basis of pre-existing pedological profile (Figure 1b). The bedrock reflection was generally characterized by marked roughness (more or less evident in different parts of the site) and many anomalies of various types (hyperbolic signals) were observed in the overlying soil layer. The radar sections of the different antennas showed varying features in terms of resolution, and it was preferred not to select only one antenna because all antennas jointly captured the scale-dependent variation of soil/subsoil. Moreover, the propagation velocity was equal to 0.06 mns−1 up to 10 ns and for longer times the average velocity was 0.1 mns−1, assuming a subsoil model with horizontal stratification and constant lateral velocity (Figure 4b).

Bottom Line: The results showed that the type and range of spatial structures of GPR data did not depend on data processing at a given frequency.It was also evident that the noise tended to decrease with the complexity of processing, then the most error-effective procedure was selected.The results highlight the critical importance of the antenna frequency and of the spatial scale of soil/subsoil processes being investigated.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Scienze della Terra e Geoambientali, University of Bari, Aldo Moro, Bari 70125, Italy. daniela.debenedetto@entecra.it.

ABSTRACT
Over the last few years high-resolution geophysical techniques, in particular ground-penetrating radar (GPR), have been used in agricultural applications for assessing soil water content variation in a non-invasive way. However, the wide use of GPR is greatly limited by the data processing complexity. In this paper, a quantitative analysis of GPR data is proposed. The data were collected with 250, 600 and 1600 MHz antennas in a gravelly soil located in south-eastern Italy. The objectives were: (1) to investigate the impact of data processing on radar signals; (2) to select a quick, efficient and error-effective data processing for detecting subsurface features; (3) to examine the response of GPR as a function of operating frequency, by using statistical and geostatistical techniques. Six data processing sequences with an increasing level of complexity were applied. The results showed that the type and range of spatial structures of GPR data did not depend on data processing at a given frequency. It was also evident that the noise tended to decrease with the complexity of processing, then the most error-effective procedure was selected. The results highlight the critical importance of the antenna frequency and of the spatial scale of soil/subsoil processes being investigated.

No MeSH data available.