Archaeological Exploration, Excavation, and Analysis for a Richer
Interpretation of the Past Chola Heritage City- Poompuhar
T. SASILATHA1, M.ASHOKKUMAR2, T.BALDWIN IMMANUEL3,
G. MOHENDRAN4
AMET Deemed to be University, Chennai, INDIA
Abstract -The article deals with the process of collecting underwater data from the historical
area in South India and explicitly showcasing its ancient civilization. Various methods are used
to investigate the environmental and structural variables that regulate the coastal structures.
Geostatistical interpolation is a technique to predict the values of a spatially continuous
bathymetry or depth of a submerged shoreline from a few sample data measurements. The
bathymetric investigation of the submerged region surrounding the ancient port of Poompuhar
consists of numerous processes conducted in stages. The survey was carried out with the use
of an integrated measuring system developed by the National Institute of Technology, Chennai,
which included various levels of Multi Beam Echo Sounder (MBES), GPS, sonar, and ROV.
To discover continuous surfaces required for analyzing the morphology of the bottom of
submerged Poompuhar, a suitable interpolation technique must be used to get estimated values
in regions that were not physically surveyed.
Bathymetric and topographic data, which typically are gathered independently for various
purposes, were included in the spatial data used for elevation surface modeling. Data is
gathered in multiple forms with varying resolutions and accuracy; as a result, a standard surface
model that will allow for quick and accurate analysis is currently lacking. The major purpose
of this study was to create a high-accuracy model of a coastal area's surface using input data
from numerous sources. ArcGIS is a popular software platform for analyzing and visualizing
geographic data, and it contains several geostatistical interpolation techniques. The research
involves the erosion of the shoreline as well as the rise in water level. The study uncovered
new scientific and methodological information on Poompuhar's bathymetric properties and
submerged surface.
Keywords: Geostatistical Interpolation, bathymetric survey, submerged surface, ArcGIS, Poompuhar
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1 Introduction
One of the richest ancient literature in Tamil
Nadu, known as Sangam literature, provides
extensive historical records on Poompuhar.
Kaveripoompattinam or Poompuhar or Puhar
(11°08'33"N; 79°1'31"E), an ancient Tamil
port, was important in India's maritime history.
The Tamil epics Silappathikaram and
Manimekhalai, as well as Sangam period
literature like Pattinappalai and Ahananaur,
mention Poompuhar as the early Cholas capital
port city, occupying an area of roughly 76.8
square kilometers. It stretched west to the
existing villages of Karuvindanathapuram and
Kadarankondan, south to Thirukadavur, north
to Kalikamur, and east to the Sea of Bengal
(Rao 1991). The disappearance or submerging
of several port cities and coastal regions is
mostly due to coastal erosion, changes in sea
level, neotectonic activity, and other causes,
including tsunamis. Marine archaeological
investigations have been carried out around the
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DOI: 10.37394/232022.2023.3.20
T. Sasilatha, M. Ashokkumar,
T. Baldwin Immanuel, G. Mohendran
E-ISSN: 2732-9984
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Poompuhar region and observations made on
the coastal landmarks and other discoveries.
Excavations on the site of two Warves (Rao,
1987; Athiyaman, 1999) found two structures
on an old Kaveri waterway. The results of
offshore exploration, at least 8 meters of water
depth, indicate that a portion of habitation has
been submerged in the sea. The ancient
shoreline of Poompuhar may be shifted some
distance offshore (Sundaresh et al. al. 1997), as
it was expected to be between 8 and 10 meters
above sea level during the Sangam period.
Fig 1: Diagram showing the sites selected for
underwater explorations in Poompuhar
Spatial interpolation, which comprises
geostatistical and deterministic interpolation, is
a technique for estimating data in contiguous
areas and predicting information that is
unknown or cannot be obtained using current
observable data. (Chai et al. 2011; Losser et al.
2014).
2 Study area
Poompuhar, a port city, was established at the
mouth of the Cauvery River. The Poompuhar
port was selected for the study area. The port
runs for up to 8 kilometers into the Bay of
Bengal Sea and for around 20 kilometers along
the shore (Table 1). Marine archaeology
research at Poompuhar has uncovered terracotta
ring wells, brick houses, intertidal storage
containers, brick structures, stone structures,
and ceramics from offshore projects that
strongly suggest habitation. Excavation both on
land and sea was required to reconstruct
Poompuhar’s early history and the people's
social, economic, and religious lives, as well as
their role in the cultural development of India in
Southeast Asia. The major goal of the survey
was to conduct a bathymetric investigation and
locate wrecks or structural remains using side
scan sonar, echo sounders, and magnetometers.
The exploration area spread from Vanagiri to
Nayakkankuppam, approximately 20
kilometers along the coast and approximately 8
kilometers from the sea (Rao, S.R. 1988).DST
has proposed a large investment to rebuild the
famous ancient port.
Table 1: Location details of the study area
3 Research materials and methods
3.1 Equipment used
The depth of the seabed at sea level is most
commonly referred to as bathymetry. Using
multibeam echo sounder equipment, the
archaeology department provided unique data.
The National Institute of Ocean Technology
created high-resolution geophysical devices to
collect data. An incorporated measuring system
was used to take the bathymetric
measurements. The shape of the seafloor makes
it undesirable to use installed equipment like a
multi-beam echo sonar or a laser sonar for
measuring.
3.2 Processing the Bathymetric data
Bathymetric data obtained from multibeam
echo sounders is one of the fundamental data
types used in seafloor system modeling (Fig 2).
The collected MBES data, along with previous
Name
Location
Length of
Coast
Poompuhar
79°51'29.417"E
11°5'31.301"N
79°51'27.332"E
11°9'19.252"N
10 Km
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and current satellite images, are used in
geospatial analysis of shoreline changes in the
Poompuhar port area. This study used
topographic maps and satellite images to
identify shoreline shifts in the coastal region.
Fig 2: Bathymetry and reflectivity of a
multibeam echo sounder
The resultant maps and charts give vital
information on the shape, features, and
properties of the ocean bottom, which is
important for finding the submerged land
portions, scattered old structures, and
civilization of the ancient period.
3.3 Geostatistical Interpolation
Interpolation is the technique of calculating a
value at an unknown place from the values in
the grid data set. Interpolation methods predict
a value at a given position by taking a weighted
average of the known values in the point's
surroundings. Many researchers have used
spatial interpolation methods to find new data
points from the known data procured. (Zhou et
al., 2007). Several authors (Heritage et al.,
2009; Guarneri and Weih Jr., 2012; Tan and
Xiao, 2014) examined the performance of
spatial interpolation methods in extensive
published research. According to certain
research, geostatistical interpolation
approaches outperform other interpolation
techniques (Li and Heap, 2008). Numerous
studies have been done to determine the
accuracy of interpolation techniques used in the
development of the Digital Elevation Model
(DEM).
Geostatistical interpolation can be used to
estimate the depth of a submerged coastline
based on a limited number of depth
measurements. The most appropriate methods
have been chosen, based on minimum value,
maximum value, range, sum value, mean value,
variance, and standard deviation.
Fig 3: Flow Geostatistical interpolation using
ArcGIS
3.4 Kriging Methodology
Kriging is the most often used geostatistical
interpolation method. Kriging is a
mathematical approach for predicting the value
of a phenomenon in unsampled places that
takes into account the spatial autocorrelation
between sample measurements. Kriging is an
advanced geostatistical process that creates an
estimated surface from a scattering of z-valued
points. Before deciding on the ideal estimation
technique for creating the output bathymetry
surface, you have to do a detailed analysis of
the spatial behavior of the phenomena
represented by the x, y, and z-values (Table 1).
Kriging is based on the assumption that the
spatial correlation structure may be represented
by a stationary variogram, which depicts how
the spatial correlation between sample values
varies with distance.
Interpolate the depth values
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X
Y
Z
398402.5
1225903
-71.19
398407.5
1225903
-71.43
398412.5
1225903
-71.43
398417.5
1225903
-71.47
398422.5
1225903
-71.29
398427.5
1225903
-71.18
398432.5
1225903
-71.6
398437.5
1225903
-71.97
398442.5
1225903
-71.75
Table 1: Processed data Collected from the
Study Area
The basic equation for kriging is:
Z*(s) = ΣλiZi
Where Z*(s) is the estimated value of the
variable at location s, ΣλiZi is the weighted sum
of the sample measurements, and λi is the
weight assigned to the ith sample measurement.
The weights λi are determined based on the
spatial correlation between the sample
measurements and the distance between the
unsampled location and the sample locations.
The Kriging estimator seeks to minimize the
variance of the estimation error, subject to the
constraint that the weights sum to one:
min Var[Z(s) - Z*(s)] subject to Σλi = 1
The weights λi are typically calculated using the
Kriging system of equations, which takes the
form:
Kλ = b
Where K is the matrix of spatial covariances
between the sample measurements, b is the
vector of covariances between the unsampled
location and the sample measurements, and λ is
the vector of weights.
Fig 4: Kriging structural map of input values
The kriging system of equations can be solved
to obtain the weights λi, which can then be used
to calculate the estimated value Z*(s) at the
unsampled location (Fig 4)
4. Result and Discussion
To construct continuous regions required for
the study and comprehension of the Poompuhar
submerged land, it was necessary to determine
values in places that were not directly sampled.
This was accomplished through the use of
several interpolation algorithms. The efficiency
of interpolation techniques was investigated.
During the first step, several locations were
utilized to create a bathymetry model and
compare interpolation algorithms. The second
phase covered the same locations as the first.
Interpolation parameters were automatically
improved for each interpolation technique
using the ArcGIS application within the
Geostatistical Analyst tool.
Based on the analysis, kriging interpolation
from ArcGIS yielded the best interpolation
surface because the Digital Depth Model
(DDM) generated was consistent with the
slopes and curvatures of the submerged land
surface. The 3-D (bathymetric) grid was used to
generate bathymetric contour, shaded relief,
longitudinal depth profile, and 3-D derivatives
for the submerged Poompuhar. Fig 5 is the
digital depth model (DDM), while the contour
map of the lower depth of the study area is
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presented in Fig 6. The DDM (Fig 5) shows the
study area relief with different ranges (Itoro
Udoh et al. 2022). Each contour line defines the
depth of points in the study area concerning the
mean water level (Fig 6). The different section
ranges are depth, shallow areas are shown. The
model also revealed that major parts of the sea
bed are not even. It shows several structural
remnants, including fallen walls, scattered
dressed stone blocks, shipwrecks, etc. inside the
submerged area.
Fig 5: Interpolating submerged levels at
Various Depths below the Sea Surface in
DDM
The survey was limited to certain areas with a
depth of 7–23 meters, and the slope was found
to be steeper in the shallow area. The slope
changes sharply at a depth of about 17 meters,
after which the slope is gentle. Echo images,
when correlated with sonography, show that the
sea floor is covered with sand. There is no
penetration to a depth of 7–8 meters in the
coastal region. The presence of acoustically
transparent clay elsewhere in the area is
indicated by an echo sounder, which shows
penetration of 2–3 meters.
Fig 6: Contour map of the lower depth of the
submerged area
5. Conclusion
This study provided an overview of scattered
data spatial interpolation algorithms applicable
to GIS applications. There has been significant
progress over the last decade in terms of
accuracy, multivariate frameworks, and
robustness. GIS should provide a variety of
interpolation algorithms that allow the user to
select the most appropriate way to locate
submerged particles in Poompuhar.
In this analysis using the geostatistical
Interpolation technique, Poompuhar has eroded
129 m in the past 36 years. Our cultural heritage
includes submerged sites and sunken
shipwrecks. India has a 7516.6 km long
coastline (including the islands of Andaman
and Nicobar) but only a small portion of it has
been explored by Sila Tripati et al. (2003). If
any archaeological remains are discovered, they
should be reported immediately to the
archaeological authorities, since evidence can
never be recovered once it's been lost. Even
though very few shipwrecks have been
discovered in India, the salvage rate is great.
This may result in the irreversible destruction of
evidence unless it is prevented. Submerged
ports and shipwrecks, on the other hand, might
be promoted as tourist destinations. Onshore
exploration has occurred in a variety of locales,
but underwater exploration has occurred in only
a handful.
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Submerged and buried remains cannot be
brought to light unless underwater exploration
is carried out in the future, which may reveal
some clues about our country's heritage. Further
research will provide a clear image of the
scarred structures beneath Poompuhar port city
using advanced tools such as ROV surveys,
underwater profiler surveys, underwater optical
and sonar photography, and their processing.
6. Acknowledgment
The first author Dr. T. Sasilatha Professor and
Dean sincerely acknowledges the financial
assistance received from the Department of
Science and Technology (DST) - Digital
Poompuhar Project, India.
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