close

Вход

Забыли?

вход по аккаунту

?

GPR microwave tomography for diagnostic analysis of archaeological sitesthe case of a highway construction in Pontecagnano Southern Italy.

код для вставкиСкачать
Archaeological Prospection
Archaeol. Prospect. 16, 203–217 (2009)
Published online 29 July 2009 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/arp.362
GPR MicrowaveTomography for
Diagnostic Analysis of Archaeological
Sites: the Case of a Highway
Construction in Pontecagnano
(Southern Italy)
R. CASTALDO1, L. CROCCO2, M. FEDI1, B. GAROFALO1, R. PERSICO3,
A. ROSSI4 AND F. SOLDOVIERI2*
1
Dipartimento di Scienze DellaTerra, Universita' ‘Federico II’di Napoli, Largo San
Marcellino 10, 80138 Napoli, Italy
2
Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle
Ricerche,Via Diocleziano 328, 80124, Napoli, Italy
3
Istituto per i Beni Archeologici e Monumentali, Consiglio Nazionale delle Ricerche,
Prov.le Lecce-Monteroni, - 73100 Lecce, Italy
4
Dipartimento di Beni Culturali, Universita' degli Studi di Salerno,Via Ponte Don Melillo,
84080, Fisciano (SA), Italy
ABSTRACT
Interpretation of ground-penetrating radar (GPR) data usually involves data processing similar to that
used for seismic data analysis, including also migration techniques. Alternatively, in the past few
years, microwave tomographic approaches exploiting more accurate models of the electromagnetic
scattering have gained interest, owing to their capability of providing accurate results and stable
images. Within this framework, this paper deals with the application of a microwave tomography
approach, based on the Born Approximation and working in the frequency domain. The case study
is a survey performed during the realization ofthe thirdlane ofthe most important highway in southern
Italy (the Salerno-Reggio Calabria, near Pontecagnano, Italy). It is shown that such an inversion
approach produces well-focused images, from which buried structures can be more easily identified
by comparison to traditional radar images. Moreover, the visualization of the reconstruction results
is further enhanced through a three-dimensional volumetric rendering of the surveyed region, simply
achieved by staggering the reconstructed GPR two-dimensional profiles. By means of this rendering
it is possible to follow the spatial continuity of the buried structures in the subsurface thus obtaining
a very effective geometrical characterization. The results are very useful in our case where, due to
important civil engineering works, a fast diagnosis of the archaeological situation was needed. The
quality of our GPR data modelling was confirmed by a test excavation, where a corner of a building
and the eastern part of another house, with its courtyard, were found at the depth and horizontal position suggested by our interpretation. Copyright # 2009 John Wiley & Sons, Ltd.
Key words: Archaeologicalprospecting; microwavetomography; inverse scattering; three-dimensional target rendering; ground-penetrating radar
* Correspondence to: F. Soldovieri, Istituto per il Rilevamento
Elettromagnetico dell’Ambiente, Consiglio Nazionale delle
Ricerche, Via Diocleziano 328, 80124, Napoli, Italy.
E-mail: soldovieri.f@irea.cnr.it
Copyright # 2009 John Wiley & Sons, Ltd.
Received 8 April 2009
Accepted 8 June 2009
204
Introduction
Ground-penetrating radar (GPR) is one of the
most widely adopted tools for monitoring shallow
subsurface structures, as it allows a non-invasive
diagnosis of the investigated domain in a fast and
simple way. As such, it represents a convenient
technology for archaeological prospecting applications. Ground-penetrating radar works by
emitting a modulated electromagnetic pulse into
the ground and by recording the strength of the
echo, produced by the interaction between the
impinging waves and the buried objects, received
at the air–ground interface, usually in a monostatic or bistatic configuration. In the former case
the locations of the transmitting and the receiving
antennae are coincident, whereas they are
different in the latter case. By moving the antenna
along a selected profile above the ground surface,
a two-dimensional reflection profile (radargram)
is obtained in which the delay time of the
recorded echoes (that can be related to the depth
of the underground reflectors) is drawn versus
the antenna position (Daniels, 1996).
In order to extract information from the
radargram, the interpretation of GPR data
usually takes advantage of migration techniques
and thus exploits much of the standard processing used for reflection seismic data analysis
(Stolt, 1978; Yilmaz, 2001). Although this kind of
processing is widespread, a possible alternative
processing strategy has gained an increasing
interest in the past few years: the so-called
microwave tomographic (MT) approach (Crocco
and Soldovieri, 2003; Leone and Soldovieri, 2003;
Catapano et al., 2006). The MT approaches rely on
suitable models of the electromagnetic scattering
that properly describe the interactions between
the wave and the target. By so doing, these
methods are in principle capable not only of
detecting, locating and retrieving the shape of the
buried objects, but also in providing information
on their electromagnetic properties (dielectric
permittivity and conductivity). To pursue this
goal MT approaches tackle the GPR data
processing as an inverse scattering problem, in
which the measurement of the electromagnetic
field backscattered by the buried objects are
the data and the electromagnetic properties of
the objects are the unknowns. In particular, the
Copyright # 2009 John Wiley & Sons, Ltd.
R. Castaldo et al.
electromagnetic properties of the buried targets
are searched for as ‘anomalies’ with respect to the
background (ground).
However, the solution of an inverse scattering
problem is a very difficult issue that poses several
mathematical challenges (Colton and Kress,
1992). This kind of problem is ill-conditioned
and non-linear. The ill-conditioning arises from
the limited information contained in the data,
and this is further worsened by the finiteness of
the number of GPR measurements and from the
fact that the antennae do not encircle the scene to
be investigated, due to the configuration of the
measurement in reflection mode. Accordingly,
the information that can be gained by the GPR
measurements and then exploited in the inverse
problem has a finite content, so that one should
determine an optimal way to exploit it in order to
achieve a reliable, accurate and stable image of
the investigated scene. This is an open question
that can be rigorously answered only in particular situations. The second difficulty is related to
the non-linearity of the inverse scattering problem, which entails the existence of several
solutions that correspond (within the considered
accuracy) to the same dataset. As a consequence,
it may happen that a solution completely
different from the ‘ground truth’ (a false solution)
is achieved from the inverse problem. Since one
cannot a priori appraise the quality of the
obtained solution, this circumstance means that
the results of the overall diagnostics procedure
may be completely wrong. Of course, it is easy to
work out that such an issue becomes even more
relevant when the available information is
reduced (owing to the aforementioned constraints).
Due to these mathematical difficulties, the
validity of MT approaches for GPR imaging
based on exact models of the electromagnetic
scattering has been proved only in simulated
cases or under controlled situations (Catapano
et al., 2006) and they do not seem at this stage
viable in practical conditions. Nevertheless, it is
possible to take advantage of some of the features
of MT methods by considering reconstruction
algorithms based on simplified models. This is
for instance the case of the Born Approximation
(BA) or the Kirchhoff Approximation (KA)
(Chew, 1995), which overcome the nonlinearity
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
of the inverse problem thus allowing remarkable
simplification. Approaches of this kind have been
widely adopted in GPR surveys for different
application frameworks such as the civil engineering and sub-services detection and mapping
(e.g. Soldovieri et al., 2007; Pettinelli et al., 2009)
and the obtained results confirm the capabilities
of these methods as compared to traditional ones.
This paper aims at indicating how simplified
MT methods are suitable for GPR in archaeological prospecting. In particular, the inverse
scattering approach we consider here is based on
the BA in the frequency domain. The BA leads to
a very effective solution algorithm able to deal
with large investigation domains (in terms of the
radiated wavelength) in a quasi-real time and
thus is well suited for archaeological purposes.
The adoption of mathematical tools that are
specifically designed to handle linear inverse
problems (Bertero and Boccacci, 1998), makes it
possible to mitigate the effect of noise on data and
uncertainties on the investigated scenario.
We applied the BA based inversion method on
data collected during a GPR survey performed at
an archaeological site in Pontecagnano, in
southern Italy. The aim of this archaeological
survey was to infer the presence of buried
remains near to an excavated zone within which
ancient artefacts and walls dating to before the
Pre-Roman period were discovered. The GPR
investigation aimed at establishing whether
remains of archaeological interest exist within
the area as it lies on the proposed third lane of the
most important highway in southern Italy (the
Salerno-Reggio Calabria).
By referring to the real data collected in such a
GPR survey, we first present in detail the results
achieved by the microwave tomographic approach
(in two-dimensional geometry). The achieved
results show that the tomographic inversion
algorithm provides well-focused reconstructions. The importance of adopting suitable
parameters in the regularization of the linear
inverse problem has been investigated allowing
us to improve the reconstruction, both in terms of
spatial resolution and depth of investigation.
Results of the overall survey are visualized as
parallel fence diagrams obtained by staggering
the two-dimensional tomographic reconstructions (GPR profiles). This simple post-processing
Copyright # 2009 John Wiley & Sons, Ltd.
205
makes the archaeological interpretation of the
tomographic reconstruction results easier to be
interpreted, and proved to be consistent with the
outcomes of subsequent excavation.
Archaeological site and
measurement survey
The investigated settlement in Pontecagnano was
at the height of its commercial and cultural development in the –ninth to third centuries BC, when
it was part of the territory of the Etruscan and
then of the Sannita people. Between the seventh
and fourth centuries BC, the settlement was
divided into two areas: the western one where
the civil, administrative and religious structures
were present and the eastern one comprising the
commercial and public sites (Strøm, 1993).
The seventh century BC was a phase of great
expansion, but after the foundation of the Greek
city of Paestum-Posidonia, Pontecagnano started
to lose its commercial relevance. In 268 BC the
Romans founded Picentia on the ruins of
Pontecagnano (Giglio, 2001).
At the Pontecagnano site, excavations were
carried out as preliminary operations during the
construction of the third lane of the SalernoReggio Calabria motorway. These excavations
revealed ancient artefacts and wall structures
dating before the Pre-Roman period (sixth to
fourth centuries BC) (Cinquantaquattro, 2000). A
geophysical survey was subsequently performed
to provide indirect evidence of how the buried
remains were linked to the excavated ones (Fedi
et al., 2008). The occurrence of so many ancient
remains in the area created the necessity to
investigate the non-excavated areas thereby
avoiding possible destruction of buried remains
of archaeological interest.
Both excavations and the GPR survey were
performed, in the framework of a collaboration
between the archaeologists of the University of
Salerno and the group of the ‘Mobile Laboratory
of Archaeo-geophysics’ of the Regional Centre of
Competence INNOVA.
The GPR survey was carried out with the
SIR3000 system and antennae of 400 MHz and
600 MHz centre frequency (in air), with the two
antennae operated in a continuous mode. The
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
R. Castaldo et al.
206
Table 1. The parameters adopted in the GPR survey
Mode
Samples
Rate (scan s1)
Gain (dB)
Range (ns)
Time
512
64
13, 13, 39, 39, 40
50
acquisition parameters are reported in Table 1.
The shallow layers of the ground were investigated to a maximum depth of investigation of a
few metres. The material is composed of pyroclastic material interspersed with limestone, where
an electromagnetic wave velocity range of 9–
11 cm ns1 was estimated by hyperbola fitting,
corresponding to a relative dielectric permittivity
around 10. The velocity estimate is in agreement
with those found in the literature for similar
materials (Gomez-Ortiz et al., 2005). The survey
was performed close to the excavated zone and
consisted of 11 profiles of varying lengths spaced
50 cm apart (Figure 1). Figure 2 depicts the
structures discovered in Trench 29.
The microwave tomographic
approach
The MT approach adopted in the data processing
simplifies the problem as linear inverse scattering
by means of the Born Approximation (Chew,
1995; Leone and Soldovieri, 2003; Crocco and
Soldovieri, 2003). The approach is described in
several papers (e.g. Leone and Soldovieri, 2003;
Persico et al., 2005). Here, we briefly outline the
main features of the approach.
The approach is formulated with respect to the
two-dimensional geometry that is depicted in
Figure 3. The inhomogeneous background scenario is modelled as two homogeneous half-spaces
separated by a planar interface at z ¼ 0. The
upper half-space is the air (dielectric permittivity
e0), whereas the lower one represents the ground,
having a relative dielectric permittivity eb and
an electrical conductivity sb. The incident field
source is simply modelled as a time-harmonic
(time dependence exp(j2pft)) filamentary y-directed
electric current, invariant along the y axis. The
data are collected under a multifrequency
Figure 1. The location ofthe GPR profiles and the excavated trenches.This figure is available in colouronline at www.interscience.
wiley.com/journal/arp
Copyright # 2009 John Wiley & Sons, Ltd.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
207
Figure 2. The remains discovered at theTrench 29.This figure is available in colouronline at www.interscience.wiley.com/journal/arp
multimonostatic configuration in the band [fmin,
fmax], where the transmitting and receiving
antennae are placed at the same point. The
targets to be imaged are assumed to be invariant
along the y axis and their cross-section is
enclosed in the rectangular investigation
domain.
In particular, the unknown relative dielectric
permittivity profile and the conductivity profile
are assumed to be located inside the investigation
domain D and represent a variation with respect
to the characteristics of the background scenario
(in this case the hosting ground of parameters eb
and sb). Accordingly, the inverse problem is
recast in terms of the contrast function, which
embeds such an ‘alteration’ from the background
and is defined as:
xðx0 ; z0 Þ ¼
"eq ðx0 ; z0 Þ "eqb
;
"eqb
(1)
with
sb
and "eq ðx0 ; z0 Þ ¼ "0 "r ðx0 ; z0 Þ
2pf
sðx0 ; z0 Þ
j
2pf
"eqb ¼ "0 "b j
being the equivalent complex dielectric permittivity of the targets and of the ground, respectively. Notice that x ¼ 0 when (x0 ,z0 ) does not
belong to the target.
Under the Born Approximation, the relationship in the frequency domain between the
unknown contrast function and the scattered
field data is provided by a linear integral
equation of Fredholm type (first kind):
Z
2
Es ðxs ; vÞ ¼ ks Ge ðxs ; v; ~
r0 ÞEinc ðxs ; v; ~
r0 Þxð~
r0 Þd~
r0 ;
Figure 3. Geometry of the Inverse problem. This figure is
available in colour online at www.interscience.wiley.com/
journal/arp
Copyright # 2009 John Wiley & Sons, Ltd.
D
(2)
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
R. Castaldo et al.
208
where v ¼ 2pf is the angular frequency, Es(xs,v) is
the scattered electric field along the air–ground
interface collected at the abscissa xs and
frequency f and ks is the wave-number in the soil.
By definition, the scattered field Es is given by
the ‘difference’ between the total field and the
unperturbed field. The total field is the sum of
two contributions: the field reflected by the soil
and the field backscattered by the buried objects.
The unperturbed field coincides with the field
reflected by the soil when the objects are absent,
and therefore accounts for the reflection/transmission at the air–ground interface. Under the
measurement configuration at hand, the scattered field is collected over a rectilinear observation domain at the air–soil interface with xs
ranging from –xM to xM (see Figure 3). Ge ðxs ; v; ~
r0 Þ
is the Green’s function of the problem, while
Einc is the unperturbed or incident field in the
investigation domain D. Both these quantities
are known after defining the sources, the reference
scenario and the measurement configuration.
The numerical implementation of the solution
algorithm requires the discretization of Equation
(2). This task is pursued by resorting to the
method of moments (MoM) (Harrington, 1961).
In particular, the linear integral relationship in
Equation (2) is discretized into a linear algebraic
system, where the unknowns are the expansion
coefficients of the contrast function along the
chosen functional basis and point-matching is
adopted in the data space (Crocco and Soldovieri,
2003; Persico et al., 2005).
The inversion of the resulting matrix L is
performed by a scheme based on the truncated
singular value decomposition (TSVD) (Bertero
and Boccacci, 1998), which provides robust solutions with respect to the uncertainties and the
noise on data. This ‘regularized solution’ is given
by:
~ ð~
x
rÞ ¼
N
X
1
hEs ; vn iun
s
n¼0 n
(3)
where fs n ; un ; vn gKn¼0 is the singular system of
the matrix L, h; i denotes the scalar product
in the data space; K denotes the minimum
between the number of measurements and the
pre-chosen number of expansion coefficients for
the discretization of the problem, and N K.
Copyright # 2009 John Wiley & Sons, Ltd.
In particular, sn are singular values, while vn
and un are the singular vectors in the data and
unknown spaces respectively (Bertero and Boccacci, 1998). The choice of the N index is
performed to ensure the trade-off between the
contrasting needs of accuracy and resolution
(that tends to increase such an index) and the
stability of the solution (that limits the N index).
Setting the tomographic approach to
tackle the experimental data
The description of the BA tomographic approach
given above is referred to the general case of
subsurface sensing tackled under a reflection
measurement configuration. This section will
detail the steps that have been necessary to apply
the method in practice to the GPR data gathered
during the archaeological survey carried out at
Pontecagnano.
Preprocessing of the measured data
The aim of this preprocessing stage is to
‘translate’ the time-domain GPR measurements
to the frequency-domain scattered field data
suitable for tomographic inversion. This has been
done for each of the GPR profiles collected and
has been accomplished by means of the following
procedure.
The first step consists of time zero correction
and background removal. A zero time correction
is performed to correct for the effects related to
the propagation path of the signal within the
cables and the antennae of the GPR. As a result,
the corrected signal takes into account only the
propagation of the electromagnetic signal from
the transmitting antenna to the structure and
then to the receiving antenna. Background
removal performs the reduction of the spatially
constant signal in the GPR profile related to the
direct coupling and the reflection from the air–
ground interface. Within a fixed-length window
(for instance 100 traces) the average signal is then
subtracted from the current trace. Figure 4
depicts four of eleven profiles (25 m in length)
of GPR survey before and after the zero time
correction and background removal.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
209
Figure 4. Raw and time-domain processed realdata: zero time and backgroundremoval.This figure is available in colouronline at
www.interscience.wiley.com/journal/arp
The second step of the preprocessing consists
in the Fourier transform of the frequency domain
within which the inversion algorithm works.
Figure 5 depicts the frequency spectrum of the
400 and 600 MHz antennae for the same spatial
point. From this figure we can first note that, as
usually occurs when the antenna radiates in the
soil, a lowering of the peak frequency (with
respect to the nominal frequency in air) arises. In
addition, we observe that the working frequency
band is larger for the 400 MHz antenna compared
with the 600 MHz antenna; this spectral beha-
Copyright # 2009 John Wiley & Sons, Ltd.
viour has dictated the choice of the different
frequency bands exploited in the tomographic
inversion presented in the section below.
Single profile reconstruction
The MT approach has been applied separately to
each of the preprocessed GPR profiles. Here, we
present some reconstruction results in terms of
tomographic images of the GPR profiles for both
of the antennae deployed in the survey.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
210
R. Castaldo et al.
Figure 4. (Continued).
400 MHz antenna
The parameters adopted in the inversion are
reported in Table 2. The main computational
effort required in performing the tomographic
approach consists in the evaluation of the SVD of
the matrix L (given by the discretization of the
integral equation in Equation (2)). In particular,
such an effort increases with the size of the
investigated domain, so that the imaging of
the whole region underlying a GPR profile can be
exceedingly time consuming. Therefore, in order
to keep the required computational burden as
low as possible, we have tackled the inversion of
the data as a sequence of inversions by sub-
Copyright # 2009 John Wiley & Sons, Ltd.
dividing the GPR profile into 24 (or 16) 1-m
subprofiles, each one providing the tomographic
image of the domain beneath it. When moving
along the GPR profile, the domain is shifted
accordingly and the corresponding segment of
measurements provides the input for the inversion algorithm. By assuming that the background
characteristics do not change along the profile,
we can tackle each subprofile by means of the
same matrix L (and its SVD), which for the case at
hand is computed with respect to a rectangular
investigation domain D having a horizontal
extent of 1 m and a depth ranging from 0 and
1.5 m. For the inversion, the contrast function in
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
211
Figure 5. Frequency spectrum of the antenna: 400 MHz (A) and 600 MHz (B). This figure is available in colour online at www.
interscience.wiley.com/journal/arp
Equation (1) has been expanded within the
investigation domain D and built up of 33
Fourier harmonics along the horizontal direction
and 33 vertical steps (Leone and Soldovieri,
2003).
Finally, the overall tomographic reconstruction
corresponding to the GPR profiles is obtained by
juxtaposing the ‘partial’ reconstructions obtained
by moving along the entire length of the profile.
In TSVD inversion (see Equation (3)), the choice
of the truncation index N has been performed by
observing the singular values behaviour of the
appropriate matrix (see Figure 6). In particular,
we chose the index N ¼ 300 that represents the
‘boundary’ between the two different behaviours
of the singular value curve: in fact, for lower
values of the index, the singular values have a
smooth decay. This procedure allows us to
exploit all the main singular value information
in TSVD, which improves the accuracy of the
solution. For index values higher than this
‘boundary index’, the decay of the singular
values becomes faster and this entails that any
improvement of the accuracy (increase of the
index N) will result in a reduction of the stability.
It is worth noting that the computational cost
needed to the offline filling of the matrix requires
about four hours on a standard PC and the
evaluation of its SVD requires about five minutes.
Table 2. Parameters of the inversion for the 400 MHz
antenna
Model relative dielectric permittivity "b
Model conductivity s b
Effective frequency band
Frequency step Df
Measurement domain
Spatial step Dx
Investigation domain
10
0.005 S m1
100^604 MHz
28 MHz
1m
0.02 m
1m [0^1.5] m
Copyright # 2009 John Wiley & Sons, Ltd.
Figure 6. Singular values behaviour for the case of 400 MHz
antenna. This figure is available in colour online at www.
interscience.wiley.com/journal/arp
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
212
Afterwards, the inversion itself and is performed
as multiplication of vectors and requires only a
few seconds. The images are given in terms of the
modulus of the contrast function, normalized
with respect to the maximum achieved throughout the profile. For the sake of interpretation, the
regions where the modulus of the contrast
function is significantly different from zero
correspond to an estimate of the position and
geometry of the buried anomalies.
Figure 7 describes the tomographic reconstructions of the four GPR profiles of the survey for
which time domain data have been presented in
the Figure 4. As can be seen, the tomographic
processing allows for focalizing various structures, which are persistent across the different
profiles. The main anomalies are localized between
5 and 10 m. In particular, we see a sharp contrast in
all four profiles around x ¼ 8 m and another one
R. Castaldo et al.
around x ¼ 13 m. Their depth is approximately
0.9 m, which is about 0.2 m deeper than expected
from nearby excavations. This difference is
possibly due to the fact that the (constant)
electromagnetic velocity assumed for the background (the half-space model depicted in
Figure 3) is slightly different from the actual one.
600 MHz antenna
The same analysis has been performed with
the data collected by the 600 MHz antenna. The
parameters adopted in the inversion are reported
in the Table 3. In this case, we have chosen a
smaller working frequency band compared with
that for the 400 MHz data. This choice arises from
the investigation of the spectral behaviour of the
data collected by the two antennae, as presented
earlier in Figure 5.
Figure 7. The tomographic reconstruction of the first four GPR profiles with the 400 MHz antenna.This figure is available in colour
online at www.interscience.wiley.com/journal/arp
Copyright # 2009 John Wiley & Sons, Ltd.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
213
Table 3. Parameters of the inversion for the 600 MHz
antenna
Model relative dielectric permittivity "b
Model conductivity s b
Effective frequency band
Frequency step Df
Measurement domain
Spatial step Dx
Investigation domain
10
0.005 S m1
100^520 MHz
30 MHz
1m
0.02 m
1m [0^1.5] m
We present for comparison the first four GPR
profiles reconstructed by the tomographic approach. For the TSVD inversion, we have retained
the first 205 singular values (see Figure 8 for the
singular values behavior). By examining these
reconstruction results (Figure 9), we can gain information about the behaviour of the anomalies. In
these reconstructed images we observe two main
anomalies at depths of 0.65 and 0.9 m, especially in
Figure 8. Singular values behaviour for the case of 600 MHz
antenna. This figure is available in colour online at www.
interscience.wiley.com/journal/arp
Figure 9. The tomographic reconstruction of the first four GPR profiles with the 600 MHz antenna.This figure is available in colour
online at www.interscience.wiley.com/journal/arp
Copyright # 2009 John Wiley & Sons, Ltd.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
214
R. Castaldo et al.
Figure 10. Pseudo-three-dimensionalvisualizationofthereconstructionby 400 MHzdata.Thisfigureisavailableincolouronlineat
www.interscience.wiley.com/journal/arp
Figure 11. Pseudo-three-dimensionalvisualizationofthereconstructionby 600 MHzdata.Thisfigureisavailablein colouronlineat
www.interscience.wiley.com/journal/arp
Copyright # 2009 John Wiley & Sons, Ltd.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
215
Figure 12. Test excavation: verification of the results. This figure is available in colour online at www.interscience.wiley.com/
journal/arp
the regions with x ranging from 5 m to 10 m and
with x ranging from 20 m to the end of the profiles.
As a possible interpretation, these two anomalies
can be associated to the upper and lower sides of
the same structure. If we compare these reconstructions with those of the 400 MHz antenna, we
can see that the 400 MHz antenna is able to
resolve only a significant anomaly at the rough
depth of 0.9 m.
Interpretation
In this section we present the tomographic reconstruction results in terms of a pseudo-three-dimensional
visualization of the subsurface by simply staggering
the obtained two-dimensional after a suitable
normalization (Catapano et al., 2006; Solimene
et al., 2007). The first result concerns measurements
collected using the 400 MHz antenna. Figure 10
shows the three-dimensional representation. We
are confident that the strong anomalies at x ¼ 6–
7 m and x ¼ 15 m, spanning spatially both x and y
directions, are representative of walls. The
strongest anomaly around x ¼ 8–10 m is in our
interpretation associated with a road, which is
Copyright # 2009 John Wiley & Sons, Ltd.
the continuation of a road visible in the excavated
trenches. In addition, the other anomalies that are
detected give indications of the foundations of
some structures. The anomalies detected by GPR
correspond to two structures, built by small limestone blocks, directed NE–SW, crossed by a road,
4.50 m in width, that take advantage of a slight
morphological dip of the travertine (Rossi, 2005).
The second interpretation concerns the measurements collected by the 600 MHz antenna. Figure 11
shows a pseudo-three-dimensional representation:
the main anomalies already pointed out for the
400 MHz antenna are evident also for the 600 MHz
antenna reconstruction. We have again evidence
of a significant anomaly between x ¼ 8 and
x ¼ 10 m, associated with a buried road. In
addition, more anomalies are detected and they
can be associated to the foundations of some
structure. Finally, we point out also an anomaly
in the zone at x ¼ 21–24 m, which was not
detected in the 400 MHz antenna results.
Conclusions
In this paper we have described the application
of a microwave tomography approach to the
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
R. Castaldo et al.
216
processing of the GPR data gathered at the
archaeological site of Pontecagnano. The inverse
scattering approach exploits a consistent,
although simplified, model of the electromagnetic scattering and allows well-focused images
from which features of the buried structures can
be properly inferred. The interpretation is further
supported by means of a simple three-dimensional rendering made by staggering the reconstructed GPR profiles, so providing an overall
visualization of the investigated site. The results
of GPR survey, elaborated by the inverse
scattering approach, have been tested by excavation 5 m northeast of Trench 28 (Figure 12),
where a corner of the building identified by our
GPR data modelling was found. This information
allows the reconstructing the plan of the ancient
house found inside this trench (Santoriello and
Rossi, 2005). In the same way, on the east side of
Trench 29 archaeological excavation confirmed
the GPR survey by locating the eastern part of
another house with its courtyard.
With regard to the computational cost of the
inverse scattering algorithm, the offline construction of each matrix required about 5 hours on a
PC and the calculation of its SVD required about
5 minutes, although the inversion in itself is a
real-time procedure.
As a final comment, it can be said that the inverse
scattering technique, with respect to the standard
GPR procedure of processing, implicitly contains
a focalization procedure and allows us to adopt
regularization schemes that make the inversion
potentially more robust against noise and parametric uncertainties. Therefore, this technique
appears to be extremely significant in supporting
the experience of the human operator, since it has
a definite advantage when creating reliable and
more interpretable images. As a future development, we will conduct a comparison between the
microwave tomography approach and the state of
art traditional processing.
On the other hand, it has to be said that, due the
complexity of the scenario usually dealt with, one
cannot completely rely on a single processing
approach. Accordingly, multisensor and multiprocessing approaches can be exploited in order
to further help the operator through the comparison of the results arising from different
techniques.
Copyright # 2009 John Wiley & Sons, Ltd.
References
Bertero M, Boccacci P. 1998. Introduction to Inverse
Problems in Imaging. Institute of Physics: Bristol.
Catapano I, Crocco L, Persico R, Pieraccini M, Soldovieri F. 2006. Linear and nonlinear microwave
tomography approaches for subsurface prospecting: validation on real data. Antennas and Wireless
Propagation Letters 5: 49–53.
Chew WC. 1995. Waves and Fields in Inhomogeneous
Media. Institute of Electrical and Electronica1
Engineers: Piscataway, NJ.
Cinquantaquattro T. 2000. Pontecagnano (SA): saggi
stratigrafici nell’abitato antico. Bollettino di Archeologia 28–30: 121–171.
Colton D, Kress R. 1992. Inverse Acoustic and Electromagnetic Scattering Theory. Springer-Verlag: Berlin.
Crocco L, Soldovieri F. 2003. GPR prospecting in a
layered medium via microwave tomography.
Annals of Geophysics 46: 559–572.
Daniels DJ. 1996. Surface Penetrating Radar. The
Institution of Electrical Engineers: London.
Fedi M, Florio G, Garofalo B, La Manna M, Pellegrino C, Rossi A, Soldovieri MG. 2008. Integrated
geophysical survey to recognize ancient Picentia’s buried walls, in the archaeological park of
Pontecagnano-Faiano (southern Italy). Annals of
Geophysics 51(5–6): 867–875.
Giglio M. 2001. Picentia, fondazione romana? Quaderni degli Annali dell’Instituto Orientale di
Napoli (AION), 8: 119–131.
Gómez-Ortiz D, Martı́n-Velázquez S, Martı́n-Crespo T, Márquez A, Lillo J, López I, Carreño F.
2005. Characterization of volcanic materials using
ground penetrating radar: a case study at Teide
volcano (Canary Islands, Spain). Journal of Applied
Geophysics 59: 63–78.
Harrington RF. 1961. Time-Harmonic Electromagnetic
Fields. McGraw Hill.
Leone G, Soldovieri F. 2003. Analysis of the distorted Born approximation for subsurface reconstruction: truncation and uncertainties effects. IEEE
Transactions Geosciences Remote Sensing 41: 66–74.
Persico R, Bernini R, Soldovieri F. 2005. On the
configuration of the measurements in inverse
scattering from buried objects under the distorted
Born approximation. IEEE Transactions on Antennas and Propagation 53: 1875–1886.
Pettinelli E, Di Matteo A, Mattei E, Crocco L, Soldovieri F, Redman JD, Annan AP. 2009. GPR
response from buried pipes: measurement on
field site and tomographic reconstructions. IEEE
Transactions on Geoscience and Remote Sensing DOI:
10.1109/TGRS-2009-2018301
Rossi A. 2005. Contesto ambientale e dinamiche
insediative tra l’ età del Ferro e l’età Arcaica.
Annali Istituto Orientale di Napoli – sezione Archeologia e Storia Antica (N.S.) 11–12: 225–234.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
GPR Microwave Tomography for Diagnostic Analysis
Santoriello A, Rossi A. 2005. Aspetti e problemi sulle
divisioni agrarie nella piana di Pontecagnano
(Salerno): una prima riflessione. Annali Istituto
Orientale di Napoli - sezione Archeologia e Storia
Antica (N.S.) 11–12: 245–257.
Soldovieri F, Hugenschmidt J, Persico R, Leone G.
2007. A linear inverse scattering algorithm for
realistic GPR applications. Near Surface Geophysics
5: 29–42.
Solimene R, Soldovieri F, Prisco G, Pierri R. 2007.
Three-dimensional microwave tomography by a
Copyright # 2009 John Wiley & Sons, Ltd.
217
2-D slice-based reconstruction algorithm. IEEE
Geoscience and Remote Sensing Letters 4: 556–
560.
Stolt RH. 1978. Migration by Fourier transform.
Geophysiscs 43: 23–48.
Strøm I. 1993. Pontecagnano-Picentia. A Hellenistic
town in the former Etruscan Campania, The
Danish Excavations. Acta Hyperborea 5: 107–
126.
Yilmaz Ö. 2001. Seismic Data Analysis. Society of
Exploration Geophysicists (SEG): Tulsa, OK.
Archaeol. Prospect. 16, 203–217 (2009)
DOI: 10.1002/arp
Документ
Категория
Без категории
Просмотров
5
Размер файла
936 Кб
Теги
gpr, sitesthe, tomography, archaeological, diagnostika, microwave, construction, italy, southern, highway, analysis, case, pontecagnano
1/--страниц
Пожаловаться на содержимое документа