High resolution imaging of a large calcite vein network from a

B.Sc.-Thesis by Sebastian Thronberens
2010
Supervisor: Prof. Janos Urai
1
communicated by Prof. Dr. J.L. Urai
LuF für Geologie – Endogene Dynamik
Structural Geology, Tectonics and Geomechanics
Lochnerstrasse 4-20
52056 Aachen
www.ged.rwth-aachen.de
Sebastian Thronberens
Rütscherstraße 155
52072 Aachen
Hiermit erkläre ich an Eides statt, dass ich die von mir vorgelegte Arbeit selbstständig
verfasst habe, dass ich keine weiteren Hilfsmittel als die angegebenen benutzt habe.
Darüber hinaus erkläre ich mein Einverständnis, dass diese Arbeit eingesehen
werden darf und Auszüge daraus kopiert werden dürfen. Bibliographische Angaben
zu dieser Arbeit dürfen sowohl im Bibliothekskatalog als auch auf den Internetseiten
des
Lehr-
und
Forschungsgebietes
Geologie
–
Endogene
Dynamik,
des
Geologischen Instituts und der Hochschulbibliothek der RWTH Aachen verzeichnet
werden.
Aachen, den 2. Dezember 2010
__________________________
Sebastian Thronberens
2
Abstract
This thesis presents a detailed high resolution image of a large calcite vein network on a
polished limestone outcrop of the Natih formation. The outcrop is located in the Oman
Mountains at the eastern slope of Jabal Sarrah. The observation is concentrated on a calcite
vein network, which spread all over the outcrop. We put a grid on the outcrop surface with a
2 by 2 m spacing. The next step was the acquisition of an image series with a telephoto lens
to generate a large panorama picture. Detailed observations on vein sets, structures and
overprinting were documented with a photo. Image processing comprised the optimization
and compilation of the image series to a 435 Megapixel outcrop panorama, which allows
zooming from a large scale view (101 m) to small scale (10-2 m). The pixel size is 0,5 mm. The
stitch has been rectified in ArcMap and detail observation images were georeferenced on
this basemap. This allows an office based investigation of the outcrop at various scales.
Furthermore, we used stereophotogrammetry to generate a point based 3D-Model from
four stereo images. The 3D-Model shows the surface structure and is another opportunity to
create an orthorectified image of the outcrop. It requires less data (images) but cannot
provide a comparable result regarding resolution. Our first evaluations of field observation
show that the vein network is composed of 4 different vein sets. Set 1 (210°/74°) consist of
the oldest veins. Set 2 gets overprinted by set 3 and 4 and strikes from NW to SE (55°/76°)
and Set 3 (75°/75°) striking from W to E, which just gets overprinted by set 4 (235°/75°).
3
Inhaltsverzeichnis
Abstract ................................................................................................................................................... 3
1.
2.
3.
Introduction ..................................................................................................................................... 5
1.1.
Motivation ............................................................................................................................... 5
1.2.
Location ................................................................................................................................... 5
1.3.
Evolution of the Oman Mountains .......................................................................................... 7
1.4.
Stratigraphy ............................................................................................................................. 8
1.5.
High resolution imaging of a calcite vein pavement ............................................................... 8
Fieldwork ......................................................................................................................................... 9
2.1.
Storage and getting around..................................................................................................... 9
2.2.
Gridding the pavement ......................................................................................................... 11
2.3.
Photographing the high resolution images ........................................................................... 12
2.4.
Documentation of detail observation ................................................................................... 14
Rework of the data ........................................................................................................................ 16
3.1.
Image Optimisation ............................................................................................................... 16
3.1.1.
3.2.
Working with Autopano Giga ................................................................................................ 18
3.2.1.
4.
5.
Rendering of the high resolution images ...................................................................... 20
3.3.
Rectification in ArcGis ........................................................................................................... 21
3.4.
Working with Photomodeler Scanner ................................................................................... 24
Integration of details ..................................................................................................................... 27
4.1.
Evaluation of the data ........................................................................................................... 27
4.2.
Description of overprintings .................................................................................................. 30
Results & Discussion ...................................................................................................................... 39
5.1.
6.
Working with Adobe Photoshop 6 ................................................................................ 16
Comparison of Results ........................................................................................................... 42
Conclusions & Outlook .................................................................................................................. 43
6.1.
Workflow refining.................................................................................................................. 43
7.
Ackknowledgement ....................................................................................................................... 45
8.
References ..................................................................................................................................... 46
9.
Appendix........................................................................................................................................ 47
9.1.
Measured vein data............................................................................................................... 47
9.2.
Detail Overview ..................................................................................................................... 48
9.3.
Zooming in the high resolution image................................................................................... 49
4
1. Introduction
1.1.
Motivation
The Oman Mountain anticline is a mountain belt in the north-eastern part of Oman. It is
famous for its Sumail Ophiolit. The processes which led to the development of the Oman
Mountains are still discussed and not revealed completely. To alleviate the evaluation of
field observations, compiling a digital database might be a first step. Through this Bsc.-Thesis
the author offers a first basic dataset on which further evaluations might become a part of
such kind of database. For that reason first high resolution panorama images of calcite vein
networks in the Oman Mountains have been taken. Using advanced hardware of
photographic equipment and optimization of methods that have been used in earlier field
works lead now to imagery with a higher amount of details. A refined workflow will give the
opportunity to optimize the results the author presents in this thesis.
1.2.
Location
Figure 1: Drive from Nizwa to Manda’a. Image from Google earth.
5
The study area this B.Sc.-Thesis is dealing with includes a polished limestone outcrop in the
Sultanate of Oman in the region of the Oman Mountains. The Oman Mountains extend in
the north-eastern parts of the Arabic Peninsula as a mountain belt running from NW to SE
direction. They cover a 700 Km distance from NW to SE and 40 to 120 Km width. The study
area lays in the western parts of Jebel Shams in a whadi at the south flank of Jebel Sarrah.
The outcrop is located at the coordinate (Figure 1):

513732 m east and 2573806 m north direction of the UTM coordinate system 40Q
Access to the outcrop is
possible from a village called
Manda’a. To reach Manda’a
when leaving Nizwa, it is
important to drive on the Road
21 in northern direction and
turn right at the roundabout in
Bahla (Figure 1), which is
reached after 38 Km following
Figure 2: Goat path leading from Manda’a to the Upper Gorge Pavement.
Image from Google earth.
the road. This road leads
straight to the village of Al
Hambra, the last spot to refuel before driving on towards the working area. The fuel station
is placed at the left hand side at the first roundabout when arriving in Al Hambra. From there
it is still a 30 km drive until reaching Manda’a. When reaching the roundabout in Al Hambra,
it is necessary to turn left and to follow the black top road in north-west direction out of Al
Hambra, passing the Mosque and Whadi Ghul, the entrance to Whadi Nakhar. After
following the black top road for another 29 Km, a water pump station appears on the left
hand side. At the next crossroad it is necessary to leave the black top road and to turn left
onto a gravel road. After a sharp right turn you reach the road which leads straight towards
Manda’a. From there, in northern direction, an old goat path(Figure 2) leads along a deep
gorge to the Upper and Lower (Raith et al.) Gorge Pavement. The upper one of the Gorge
Pavements is the working area dealt with in this thesis is just called the Upper Gorge
Pavement. The one km long walk from Manda’a to the Upper Gorge Pavement takes about
45 minutes.
6
1.3.
Evolution of the Oman Mountains
The whole development of the mountain belt is still not revealed. This chapter will give a
basic introduction into the tectonic processes which took place.
From Precambrian till Paleozoic, Arabia and Iran were a part of the supercontinent
Gondwana. Through extension during early Perm an intracontinental basin could develop at
the north-eastern margin of the Arabic Plate. In the Trias further rifting sequences started a
breakup of the crust when the Neo-Tethys Ocean opened and started the spreading. During
this period carbonate platform limestones were
deposited in the deepwater basin close to the northern
Arabic
Margin
and
generated
the
Hawasina
Formations, which contain the famous Melange Units
including the Oman Exotics. The ocean spreading
stopped in the mid Cretaceous and developed into an
intra-oceanic subduction zone dipping to the north (93
Ma). In this process the Hawasina Formation acted as
an accretion wedge until late Cenomanian. During the
mid Cretaceous the subduction of the Arabic
Figure 3: Development of Oman Mountain and
Sumail Ophiolit obduction. (Gealey, 1977)
Continental Margin caused an obduction of the
oceanic crust from northern direction and the Sumail
Ophiolit got obducted 400 Km onto the Arabic Platform. (Glennie, 1973) This process ended
in the late Cretaceous. Caused by the enormous pressure of the obduction the allochthone
nappes were formed by the continental margin and old Tethys Ocean sediments have been
emplaced on the autochthone Arabic continent after the obduction ended. To compensate
the ongoing convergence, a second subduction zone appeared along the Makran
Zone.(Glennie, 1973) This event relieved the strain from the first subduction zone and
afforded uplifting of the buried continental margin. The uplifting destroyed the structure of
the Sumail Ophiolits and of the allochthone nappes and let them spread over the Arabic
plate. Following shallow water sedimentation and erosion in the Maastrichtian caused the
present shape of the Oman Mountain belt. (Glennie, 1973)
7
1.4.
Stratigraphy
Here the author will just describe the stratigraphic settings concerning the
study area which is located in the Hajar Supergroup of Oman. The observed
limestone outcrop belongs to the Natih Formation. The Natih Formation
composes the upper carbonate unit of the Wasia Group of the creataceous
part of the Hajar Supergroup. At the Wasia Groups lower boundary lays the
argillaceous Nahr Umr Formation (Albian) which lays disconform with the
Shuaiba Formation limestones (early Aptian) of the older Kahmah Group.
The upper boundary of the Wasia Group is set between the limestones of
the Natih Formation (early Turonien) and the shale rich Fiqa Formation of
the younger Aruma Group. (Smith, 1990)
The Natih Formation is famous for its vein structures and in some parts of
Arabia even offers good reservoir rock properties.
Figure 4: Stratigraphic units in the Oman Mountains. (Smith, 1990)
1.5.
High resolution imaging of a calcite vein pavement
In this part of the thesis the author wants to give a short description of the idea behind
generating a high resolution image of a calcite vein outcrop in the Oman Mountains and will
mention the basic methods that were used.
The outcrop called Upper Gorge Pavement offers an extremely high density of calcite veins.
Polished limestone outcrops of such a high quality are a worldwide unique geological setting
which can be observed in the Oman Mountains only.
The compiled image consists of more than 300 overlapping high resolution images, taken
from one fixed spot over the outcrop. They have been merged together and rectified which
now allowes a satellite image-like view on the outcrop. The perpendicular view affords an
approximately realistic outcrop surface, like standing on the outcrop in the field. Zooming
into points of interest to achieve a more detailed image of the certain point is another
property the high resolution imagery provides.
8
2. Fieldwork
2.1.
Storage and getting around
Figure 5: Common SUV for rent.
There are not too many options in terms of how to get around in Oman. Almost no bus lines
operate between cities or villages, except for a shuttle service for students to get to school
and back.
To be able to work independently it is necessary to own or to hire a car. To reach the
working areas safely, it is recommended to hire an SUV. SUV’s are available for rent in
Muskat (Figure 5). The speed limit on Oman Roads is set at 120 km/h.
Driving on highways at nighttime is highly not recommended because of the great danger of
accidents by road kills.
Another major requirement for efficient work at different locations is good organized
storage of the equipment. One opportunity was offered by the GU-Tech University. In a shed
most tools which were not needed could get stored. Another opportunity for storage and
overnight stays was a flat which had been leased in the old town of Al Hambra. Here maps,
9
food, luggage and computer hardware could get mounted. The daily equipment had been
stored in the back of the rented SUV’s. That made it possible to work in teams at separate
spots.
The field equipment contained:

the Estwing hammer

a geological compass

a magnifying glass

flashlights

measuring tape and bricklayer cords
Figure 6: Basecamp for staying overnight in the field.
Additionally fieldbeds, mosquitonets and sleeping bags where used to save travelling time by
staying in the field overnight. All the camping gear, including the fieldbeds were available at
malls in Muskat. So it is not necessary to bring along a big amount of camping gear, which
allows travelling to Oman with less luggage.
10
2.2.
Gridding the pavement
Figure 7: : Team prepares the gridding of the Upper Gorge Pavement.
The gridding of the outcrops vein network was a basic step for generating the high resolution
image and for a precise orientation during the detail observation. The demand of the grid
was to divide the outcrop into different quadrants and to act as a plane which is orientated
from N to S direction parallel to the surface. Another important aspect for the process of
gridding laid in the rework of the taken picture: it provides one
method to rectify the image in the end. Further it is a way to proof if
all images overlap the right way for the stitching.
The Upper Gorge Pavement is the smallest outcrop of its kind in this
area. So the grid seized out of 2 by 2 m² quadrants seemed to be
sufficed. This size provided enough quadrants for an accurate result.
The irregular surface of the outcrop complicated the measuring to
approach the right grid dimensions. To achieve a precise grid, the
mixture of a couple of tools generated a baton combined from a
dipods leg with measuring tape on it (Figure 8) and a frame joint with
Figure 8: Baton made of a
dipods leg with measuring
tape.
an added air-level. By using the frame joint combined with the air11
level it was possible to adjust the baton to the correct angle, so that it is assured to be
always in a perpendicular angle to the outcrop.
Each point was marked as a cross. Capital letters on the ordinate running from A till I in
northern direction and numbers from 0 to 15 on the abscissa running in eastern direction
labeled the quadrants (Figure 9). It is important to mention that the letters and numbers
labeled on the crosses give the orientation to their surrounding quadrants. For example the
4 quadrants A2, B2, B3 and A3 surround one label.
Figure 9: Example of grid label and quadrants.
2.3.
Photographing the high resolution images
All high resolution images have been taken by a Nikon D90 Camera combined with a Sigma
120-400 mm F4.5-5.6 Telezoomlense with optical stabilizer. This setting allows the high
resolute conditions under which the images have been recorded. The camera was fixed on a
tripod, so that a precise change of the image direction could be provided. The method of
photographing the single images considers some important aspects to achieve accurate
image data. The camera spot had to be located in a certain height over the outcrop, which
endues an adequate overview of the outcrop and a large angle between outcrop surface and
the viewing angle, which simplifies the rectification process. The outcrop was photographed
from two different spots. From each spot, two series of minimal and maximal focal distances
have been shot. The first spot was located in the E of the outcrop, the second one in the S.
The first series was photographed in 400 mm focal distance and included 379 pictures from
spot 1 and 271 from spot 2. From each spot the second series was taken in the minimal focal
distance of 120 mm and included 37 images from spot 1 and 37 images in spot 2.
12
The pictures have been taken in a constant movement of the camera photographing the
outcrop in different rows from up to down. When the first picture was taken, the next
picture was adjusted by aiming to a spot at the right frame of the first picture which was
moved to the left frame of the next picture by changing the direction of the camera (Figure
10). Through this method the spot was visible in both pictures. This provides overlapping
which is an important task for the correct merging of the images in further steps.
Figure 10: Overlapping (red) between images guaranties a good stitching result.
When all series were taken it was necessary to measure the distance from the camera spot
to the outcrop.
Spot 1
Spot 2
Label B1/A2
272/24
Label H0/I1
283/16
Label G15/F16
341/35
Label D13/C14
315/41
Label G15/F14
55/19
Label D13/C14
56/26
Label B0/A1
355/40
Label H0/G1
355/26
13
Therefore measurements of the orientation of the camera angles have been made. The
measurements point on the four grid labels which are in the corners of the outcrop. They are
different ones in each spot because of their visibility caused by the different views. Over the
camera angle on each label on the surface we calculate the mean angle on the outcrop. But
it also shows the range of angle sizes differs from 19° to 40° in spot 2 and 16° to 41° in spot
1. In further step it became obvious that the image of spot 1 will not be rectifiable to an
image that can be viewed perpendicular to the bedding. Therefore just the distance between
camera and surface been calculated for spot 2. The values were used to calculate a triangle
so that it was possible to get the distance to the middle of the pavement with the help of
sinus functions. In spot 2 the camera distance measures 31 m to the western border of the
grid and 14 m to the middle of the outcrops surface.
2.4.
Documentation of detail observation
The last task of field work was the
detail observation on the outcrop.
The
observation
was
basically
concentrated on the calcite vein
network and especially on calcite
veins which are crossing each
other. Crossing veins sometimes
show
a
phenomenon
called
Figure 12: Displaying of an en-echeleon vein. Striking of wing-tips
marked in blue, striking of array marked in yellow.
overprinting (Figure 11). It describes the fact that an existing vein was cracked by a younger
tectonic event and got sealed afterwards. Out of this the logical consequence results that
the vein which got crossed belongs to an older event than the crossing vein. Therefore age-
Figure 11: Age relationship-019, yellow vein overprints red vein and offsets it dextral(left). Measuring of veins(right).
14
relationships can be defined between the different veins. Furthermore different properties
of the veins have been documented, like orientation and dip, aperture, fibrous or blocky
crystals and the orientation and dip of the wing tips when an en-echeleon set got observed
(Figure 12).
The detail observation took place in
a team of two (Figure 11). One did
the labeling of the observance, the
other one was writing down the
results,
properties
and
photographed the observances from
a map view angle and if possible
from a profile view angle. The map
view images were taken from a view
perpendicular to the surface. The
profile view images show a side
view of an observance if a side view
was given. The map view pictures
have been integrated in the finished
rectified
panorama
image.
The
labels on the observance included
an arrow in north direction for a
correct orientation of the taken
Figure 13: Remarkable property of this vein pavement: Erosion
creates big holes and caves. Gives rare opportunity to observe the
veins from above and below the bedding.
image. All age-relationships were
marked with ST-AR, which describes
the initials of the author and the
shortcut AR for age relationship. As a symbol for the older vein one dot was marked beside.
The next younger crossing vein got one additional dot. Often a pen or compass is seen on
the detail images for scale. Beside overprintings, exceptional vein structures got
documented too and got also integrated in the panorama picture. Using this method
different vein sets should be defined with help of the orientation and the documented
properties. Similar orientation and property between two veins might show that they belong
to the same kind of set or event.
15
3. Rework of the data
This chapter deals with image refining and the engendering of the high resolution image out
of the data acquisition from the field. Furthermore it contains the documentation of the
synthesis of a point based 3D-model of the outcrop based on stereophotogrammetry.
3.1.
Image Optimisation
During the field work two image series have been taken on each spot. The first series with
400 mm focal distance and the second with 120 mm focal distance. To achieve the most
optimal result for the panorama picture it was necessary to optimize the image settings of
the single pictures. Therefore the software Adobe Photoshop 6 has been used.
3.1.1. Working with Adobe Photoshop 6
Figure 14: Statring and working with the Image Processor in Adobe Photoshop C5.
The images of the four series have been saved in the raw format of Nikon called NEF. This
format provides the full content of quality. Using this format Adobe Photoshop offers
16
different optimization options which allow a fine tuning process for the images. Here the
points of interest laid in achieving a clear vein network on the limestone hostrock. Especially
in small scale view the calcite veins should appear in the best possible quality. The difficulty
of these tuning options was to find the optimal settings for each series.
To process all images of one series in one step the Image Processor has been used (Figure
14), placed under the function “Skripts”. The Image Processor is a Photoshop script
command. It is able to select any graphic file format and to batch process in the three file
formats JPEG, TIFF and PSD. (Adobe, 2009)
The first step when using the
Image Processor is to choose
the folder containing the
graphic files that are to be
processed.
necessary
location
It
to
is
define
where
also
a
the
processed images are going
to be saved. At last the
Figure 15: Finetuning options in Adobe Photoshop CS5.
saving file format has to be
chosen. We chose the JPEG
file format and set the Quality value on 12. JPEG is supported by most software and should
make further work steps easier. Because the files are in the raw format Photoshop offers a
preview of the first image in the folder for fine tuning options (Figure 15).
We concentrated on the tasks Basic, Detail and Lens correction. For an optimal result in 120
mm focal distance series and the one with 400 mm of spot 1 we set the Amount under
Details on +115 and regulated the Masking on a value of 67. Furthermore lens failures like
the chromatic aberrations had to be corrected under Lens correction. Therefore the color
values of Red/Cyan have been set on +13 and the ones of Blue/Yellow on -13. Under Basics
the Clarity got changed to +100 to achieve a good calcite vein to host rock contrast.
Photoshop will process all images of the chosen series the same way by pressing the Done
17
button. The series of spot 2 were set with an Amount of 70 and a Masking of 0. Here the
Lens correction was changed to a Red/Cyan value of +18 and a Blue/Yellow value of 0.
3.2.
Working with Autopano Giga
Now the image optimization had been completed and the merging of the series could take
place. Therefore the software Autopano Giga was disposed. Autopano Giga includes an auto
detection function for panorama images. It is able to analyze the content of a folder and
recognizes single images and sorts them by creating image groups automatically. Then
panoramas get generated out of the image groups (Figure 16).
Figure 16: Autopano Giga Interface. The Group window on left containing the single outcrop imagery. The Panorama
window on the left includes the quick view of the panoramas merged from the single images.
The interface of the software is kept simple and consists of three main areas: the main
toolbar, the group view and the panorama view. To start the detecting, the folders of the
different spots got dragged into the group view area. First a maximum time lapse in seconds
and an average number should be set by the user. Autopano Giga automatically detects the
4 series by detecting the maximum time lapse in seconds in which the images have been
taken. Therefore the software separates the images by detecting the shooting date. After
that different algorithms look for the longest time lapse between two following images.
When the time lapse is longer than the time lapse which had been set before, a new group
will be created till the average number of images in this group reaches the value which has
been set up before. By choosing a time lapse smaller than 2 minutes, the image data got
separated into 4 groups.
18
When activated Start panorama detection of a group each group got stitched together. The
panorama view area allows a first look on the merged panoramas. By double clicking on one
of them it is possible to edit the panoramas in the Panorama Editor (Figure 18). Here
Autopano Giga offers a tool to distort the panorama image to an image seen from the bird
eye view.
Figure 18: Panorama getting rectified by using the function Set verticals in the Panorama Editor.
By using the Set verticals tool the panorama can get straightened by means of vertical and
horizontal lines. We used this tool to distort the panorama by distorting point rows in the
grid on the outcrop. Because of the
camera perspective they do not
appear in lines. They are bended
concave to the image frames. So the
aim was to generate all quadrants on
the grid into their original rectangular
shape. This aim would be reached
when a perpendicular view on the
outcrop would be approached. The
panoramas
of
both
spots
got
Figure 17: Outcrop divided in parts B, C, A (up to down) displayed in
the panorama window.
threatened that way. But the irregular
outcrop surface and the relatively close camera distance to the outcrop made it difficult to
distort the outcrop in a reasonable way. The tool reached its limits quickly, especially in the
19
extremely eroded part of the outcrop in the center section of the panorama. The panorama
taken from spot 2 brought results more useful than the ones of spot 1. By dividing the
panorama in three sections (Figure 17), the distorting process worked much more effective.
The panorama got divided into a western part A, a middle part B and the eastern part C. The
advantage of the division was that distorting of one part would not take influence or cause
distortion on one of the other parts. This step made the distorting much more effective.
3.2.1. Rendering of the high resolution images
After processing the three parts in the
editor, all of them needed to get
rendered to achieve the best quality
small parts of the panorama got
rendered first and were compared
after thereafter. It revealed that the
image quality was still acceptable
when using just 85% instead of 100%
of the rendering size, which also had
the advantage of a smaller sized
panorama file. To be able to rework
the pictures with Photoshop it was
necessary to keep the size smaller than
30.000 by 30.000 pixels when using
Figure 19: Rendering options of before starting the rendering
process.
JPEG file format for the output file.
Beside the render size, we chose
planar projection mode and a bicubic interpolator (Kolor, 2009).The interpolator projects the
source images pixels on the panorama picture while rendering. This influences the sharpness
of the rendered panorama. There were no visible differences by changing the interpolator.
The Bicubic had been chosen because it especially provides highly contrasted lines which
might have had a visible effect on the vein network. The smartblend function of the blender
showed also the best results. (Kolor, 2009) This kind of blender uses the Multiband blender
and a picture analysis engine for the identification of common objects which are presented
in the source images of the image group. Based on those results the blender approaches
common features between the images. Moving objects like a person which has changed the
20
position between two shots get set aside. Disadvantageous was that Smartblend works
much slower than the other algorithms but it provides a much higher rendering quality
(Kolor, 2009). As a file format JPEG was set with 8 bit color depth on maximum quality and
72.00 DPI.
Autopano Giga uses cell-redering for the rendering operation. Therefore the software
renders the panorama in small cells. This method divides the whole work in smaller jobs. The
use of multicore processors splits the work on the different cores and through this method
achieves faster results. This reduces the amount of work on the panorama and lowers the
required amount of RAM. (Kolor, 2009)
Figure 20: Rendered images of the seperated outcrop parts A, B, C (left to right).
Under these conditions all of the three panorama files, part A, B and C, have been rendered
to get stitched together and rectified in the next step.
3.3.
Rectification in ArcGis
ArcGis is one of the most important geo information systems. It offers the possibility to
display, edit, query and analyze map data. Therefore it features a georeferencing mode
which allows distorting an image layer onto another georeferenced layer used as a kind of
base layer.
In our case we wanted to use the georeferencing mode to merge the three panorama parts
to one large high resolution image. The same step should provide the rectification to a view
which is perpendicular on the outcrops surface by stitching the three images together.
21
Therefore we took advantage of the grid which was labeled on the outcrop during field
work. In this step a coordinate system got added into ArcGis as a base layer (Figure 21). It
represents an orthorectified grid. The coordinate system was generated with Microsoft Excel
Figure 21: Base map layer for the rectifying process in ArcGis. Grid had been generated in Microsoft Excel.
as a table including x/y coordinates, using the same amount and sequence of labels we used
in the field. The table has been saved as a CSV file. This type of file can be transferred to
ArcGis by choosing add x,y data under the tool menu. Through this step ArcGis adds a
coordinate system by generating a layer in the table of content. After that the rendered
panorama JPEGs got loaded into ArcGis by clicking the add data button. They also appeared
as layers in the table of contents. Now the next step is to georeference the grid on the three
rendered images with the coordinate system of the base layer (Figure 22).
First the layer dropdown choice had to point on the layer (image) that needed to get
georeferenced. By right clicking its name in the table of content it is possible to zoom to layer
to make it visible in the map view. Now we had to add the control points using the add
control points tool. It is important to mark the control point in the image which gets
georeferenced first and then connect the point to the same spot of the base layer. In our
case we set the yellow points of the grid as control points. Because of the high resolution of
the images the labels were still detectable. They needed to get connected to the point with
the according label in the coordinate system. Doing this point by point, the image got
adjusted to the coordinate system. When more than ten points had been set, the function
spline under properties distorted the image in a way that all control points fit perfectly on
the coordinate system. Now the image had been georeferenced to the coordinate system
22
and therefore rectified perpendicular to the surface of the image. By clicking Georeferencing
in the Georeferencing toolbar it is possible to rectify the image by choosing Rectify. This
function provides the possibility to save a georeferenced copy of the image as a TIFF file
including all the projection information added to the image during the georeferencing
process. Besides the TIFF file format is the most adaptable raster format. It is able to store
pixel at several depths bit and is suited to get imported in any image editing application.
All of the three images have been processed this way and saved as TIFF files. The next step
was to open them all in ArcGis again. They were all referenced to the coordinate system now
(Figure 22). But through the distortion of the images caused by the low camera angle and the
irregular surface on the outcrop, the rectification caused extreme distortion in the upper
parts of image B. They had to get cut off in Photoshop. During the first rectification
processes in the Panorama editor of Autopano Giga, separating the image in three parts was
an advantage, because they were not able to influence each other while distorting the grid.
Figure 22: The three rendered parts A, B and C as layers in ArcGis rectified to the base map layer (grid).
But this also caused that the overlapping frames of the three images did not fit perfectly
anymore. That made the stitching more difficult.
To achieve better stitching we referenced the left frame of image B to the right frame of
image A and the right frame of image B to the left frame of image C. So image B had to get
23
rectified a second time. But through this method finally all three parts became one large
panorama picture. This image had been saved using the function export map in the file
menu. Here we generated a TIFF file choosing a size of 1600 dpi. So ArcGis generated the
high resolution image which now includes more than 400 megapixels.
3.4.
Working with Photomodeler Scanner
An alternative to rectification by gridding the outcrop on a certain grid is tried to achieve by
using Photomodeler Scanner. Here the way of generating a point based dense surface 3D
model by using stereophotogrammetry is described.
Stereophotogrammetry bases on photogrammetry, the idea of measuring geographic
properties of an object from an image. Stereophotogrammetry uses two or more images of a
not moving object. The points get measured from different camera angles and distances
which allows a three dimensional determination of any point of the object. By adding
triangulations between the intersections of those points, a 3D-surface gets generated which
equals the object on the image. (Eos Systems Inc.)
Photomodeler Scanner is working the same way.
Before adding image data to the program it is
necessary to calibrate the camera that has been
appropriated. This first step helps the software to
precisely measure the camera’s focal length, the
principal point, digitizing aspect ratio and the
lens distortion. After this step a new point based
project can get started. First Photomodeler
Scanner offers a dialogue box where the stereo
imagery can get chosen. During the study, 4
Figure 23: Photomodeler-Calibration-Grid used
for camera calibration. Have been photographed
from different positions. (Eos Systems Inc.)
stereo images have been added to start modeling. The images will get matched with the
camera from the calibration (Figure 23) library automatically. Under the task Project the
images got idealized to avoid any lens distortion. In the next step the idealized images got
referenced to each other in the referencing mode (Figure 24). Therefore one image got
chosen as a source image by setting a reference on a certain point in the image. In this case
24
grid points were used as these points. When a point in the source images was set, it also had
to get marked in all other images. The more reference points are set, the more precise the
resulting model will be. Using these points, Photomodeler Scanner has the possibility to
reference the images to each other and to calculate a 3D point cloud. (Eos Systems Inc.)
Figure 24: Setting of reference points on stereo images by using the referencing mode. Source image on the left
side.
When at least seven points have been set, the Trim Tool was used to mark the area of
interest on the source image. It is possible to choose the area outside or inside the trim to
create the 3D model. In the following step the point cloud got generated. Therefore the
Create dense surface dialogue was opened. Here the software shows up suggestions for the
image pairs it will use to create the dense surface. It is necessary to choose a pair with a low
angle between both images. The sampling rate controls the distance between each point of
the point mesh. The higher the distance is set, the faster the point mesh can be generated
but the quality decreases by the decreasing number of points. The sampling rate of the
outcrops point mesh was set on 10.350. By clicking Execute the point mesh begins. The
resulting 3D point mesh can be viewed under the task View by clicking Open 3D View. Now a
new window, which includes the generated point meshes showed up. In the 3D View
Options dialogue box Point Mesh has to be enabled as surface type to make the point cloud
visible. (Eos Systems Inc.)
25
Figure 25: The 3D-Point cloud with textures displayed in the 3D-View.
Further it is possible to change the Display Style into Quality textures which visualizes the
object with a more detailed surface by taking values from the source image (Figure 25). In a
last step the point cloud got meshed with triangulations between the points. Therefore the
point cloud got marked by clicking on it with the right click. Here it is possible to activate the
triangulation. Now a certain surface has been generated which finally makes a proper 3D
model out of the 3D point cloud. (Eos Systems Inc.)
26
4. Integration of details
This chapter deals with results of the data observation and contains descriptions of certain
points of interest which have been documented on the outcrop during the fieldwork. Each
point got documented in a profile view and a map view image. The map view images of all
points of interests have been georeferenced and integrated additionally into the rectified
high resolution image. (Figure 40)
4.1.
Evaluation of the data
During the field observation it was possible to document 51 points of interest concerning the
vein network. 33 of them showed age-relationships between the veins. 63 veins had a
measurable azimuth and dip. 28 veins offered just measureable strike values and 14 enecheleon sets have been documented. All in total the results presented in this chapter are
predicated on 94 measured veins.
In a first step the veins should get divided in different vein sets. Through the orientation of
the vein sets it is possible to draw conclusions about a certain geological event and its stress
field which had effects on the limestone hostrock. Therefore the 62 measured veins got
plotted in a stereonet concerning their dip directions (Figure 26).
Figure 26: Stereo Plot of 62 measured veins. Sets classified as set in legend for a first interpretation. Plot
got generated in Stereo32.
27
The data displayed in the stereo plot has been interpretated as at least 4 different vein sets
out of the 63 measurements. It shows one set striking from N to S with 21 counts, a second
one from W to E with 16 counts, a third one striking from SW to NE with 12 counts. The
fourth set seems to strike from NW to SE, but consists of just 2 measurements in this plot.
The last 10 measurements got relatively low dipping angle which makes it hard to classify
them to one of the other sets. It might also be possible to speak of a fifth vein set without a
considerable dip direction.
Now the sets are divided by their strikings. In a second step it was necessary to have a closer
look on the age relationships by overprintings between the veins. Here a method of plotting
have been used Marc Holland developed just for this reason (Holland, 2009b). This kind of
plot uses just the strike values of each vein. The documented age relationships get plotted in
a cluster from 0°-180° divided in steps of 10° (Figure 27). Here each vein of each age-relation
gets plotted by its strike value and its age-relation. If it acts as a crossing vein it got marked
red, if it got crossed it is the older one and got marked green. The advantage of this kind of
method lays in the good overview of the data. It provides analyzing age-relations and
directions at the same time and allows involving all 94 observed veins. A disadvantage might
be to find the right choice for the classification of the sets. Here all values have been
simplified to 10° steps.
But the results of this method show similar conclusions to the stereo plot. Here it is also
possible to define four vein sets. We also get an idea of their age-relations to each other.
The oldest set 1 gets crossed by all other sets and strikes with values from 170° to 10° from
N to S. The next younger set 2 gets crossed by set 3 and 4 and strikes from NW to SE with
values from 130° to 150°. Set 3 strikes from W to E with values from 70° to 110°. Here veins
even cross each other. The veins from 70° to 90° are crossing the ones from 100° to 110° for
three times. But they lay to close to each other to define another vein set. The youngest set
4 strikes from 30° to 50° in NE direction.
Now four vein sets have been defined. The fifth set which appeared through its low dip
angles in the stereo plot could not be analyzed through the plot of Marc Holland because
28
Figure 27: Overprinting-Cluster shows vein generation and gives conclusions about vein sets by strike direction. (Holland,
2009b)
dipping angles are not reasonable in this method. So the negligence of those dipping angles
might lead to a second interpretation in the stereo plot (Figure 28).
Finally the stereo plot offers a certain idea of the dipping of the different vein sets. For the
final classification the average values of the sets azimuth and the dipping angle have been
taken:
29
So the set marked with the + striking from N to S will be defined as Set 1 (210°/74°). The set
striking from W to E marked with • will be Set 3 (75°/75°), set 4 (235°/75°) is marked with
symbol ▲and set 2 is marked with the■ (55°/76°).
Figure 28: Second interpretation of vein sets. 62 measured veins plotted in stereo net with using Stereo32.
4.2.
Description of overprintings
Now the vein sets are defined by average values. This chapter provides a closer look on the
observations documented in the field. The defined sets will get analyzed by the documented
detail imagery and the measurements that have been taken.
Each vein got analyzed for the following properties:

Overprinting

Aperture

Tabular or curved structure

Crystallization (blocky or fibrous one)

inclination

Appearance of styloliths
30
When appears as en-echeleon vein:

inclination of wing tips and array

sigmoidal or lenticular structure
Images of set 1:
The veins of set 1 seem to be the veins belonging to the oldest tectonically event on the
outcrop. They appear striking from N to S. It was not possible to document a case where N-S
striking veins cross another vein. Some of them are the veins with the thickest aperture of
the pavement. Another interesting aspect might also be a kind of chess field-like structure
between set 1 and set 3 especially in the western part of the outcrop (Figure 37). When
having a look on the pavement overview it is obvious that the thick N-S striking veins appear
in the western part and the eastern part in the highest evidence.
ST-AR-009
Figure 29: Displays age relationship 009. Set 1 gets crossed by set 3.
31
The image presented here shows the age relationship 009 (Figure 29). It has been taken in
the quadrant E15. It shows a part of a set 1 vein oriented with 106°/90° crossed by a vein of
set 3 oriented with 015°/82°. The aperture of the set 1 vein measures 0,7 cm and the one of
the set 3 vein 1,5 cm. Both got blocky crystals and a stylolith is visible in the set 3 vein. The
structures are quite hard to describe. Both seem to be tabular, but belong to a vein network
without a clear visible end. The structure of one vein changes at many different locations.
Here we can see that the set 3 vein splits up in both directions. The crossed vein of set 1
even shows a bit of a sinistral offset.
ST-AR-021
Figure 30: Displays age relationship 010. Set 1 crossed set 3.
This image of age relationship 021 has been chosen because it shows a very clear crossing of
veins (Figure 30). It documents a set 1 vein crossed by a set 3 vein again. It has been taken in
quadrant E6. The set 1 vein got an inclination of 270°/66°, the one of set 3 an inclination of
357°/61°. The aperture of the set 1 vein measures 1 cm and it got blocky crystals. The
aperture of the set 3 vein constitutes 1,3 cm and it got blocky crystals, too. It is hard again to
define a clear structure for one of the veins. They seem to be mostly tabular in this part.
32
ST-AR-026
Figure 31: Displays age relationship 026. Set 1 gets crossed by set 2 and set 4. Set 2 gets crossed by set 4.
Age relationship 026 presents the overprinting of three vein generations (Figure 31). It was
documented in quadrant B2 and concerns set 1, 2 and 4. The set 1 vein got an inclination of
098°/82° and an aperture of 2,8 cm and gets crossed by both veins. The set 2 veins
inclination measures 049°/82° and got an aperture of 2,3 cm and gets just crossed by the set
4 vein which aperture counts 0,5 cm and which got an inclination 318°/87°. Again all veins
got blocky crystals. The set 4 vein offsets the ones of 1 and 2 sinistral. On the other hand the
set 1 vein got offset dextral by the set 2 vein.
Images of set 2
Set 2 concerns of veins striking from NW to SE and seems to be the next younger vein
generation. On the images we can see them getting crossed only by set 3 and 4, never by a
set 1 vein.
33
ST-AR-013
Figure 32: Displays age relationship 013. An en-echeleon vein of set 2 crosses a set 1 vein.
On this image age relationship 013 shows a set 2 vein in slightly sigmoidal en-echeleon
structure crossing a set 1 vein in quadrant E14 (Figure 32). The inclination of the array of the
set 2 vein is 052°/78° and the inclination of the wing tips measures 355°/86°. The crossed set
1 vein got an inclination of 284°/86°. Both got blocky crystals. The location of this
overprinting even offers a 3 dimensional measurement of the set 2 vein evoked through the
surface structures of the outcrop.
Images of set 3
The veins of set 3 describe a quite large amount of veins. The range counts strike values
from 70° to 110°. But in general we can say that they are striking from W to E and are part of
chess field-structure which it builds up with set 1 described in the western part of the
outcrop. They are the veins which show the most measured age relationships and get just
crossed by set 4. In some cases it even seems that veins of set 3 striking with 100° to 110°
are crossing set 3 veins striking with values from 70° to 90° like the age relationships ST-AR003 and 005 point out. But there are also cases the other way around shown in age
34
relationship ST-AR-006. That makes set 3 a kind of exceptional. When seen in a large view
scale set 3 is displayed as a big amount of veins striking from W to E. But when having a
closer look it becomes visible that they concern of a lot of smooth veins which run in a kind
of corrugated structure just more or less from W to E.
ST-AR-033
Figure 33: Displays age relationship 033. Set 1 gets crossed by set 3. Set 3 offsets set 1 sinistral.
Age relationship 033 shows an example of a set 3 vein crossing set 1 vein. It has been
documented in quadrant C3 (Figure 33). Here only the strike values could get measured. The
set 1 vein strikes with a value of 174° and got an aperture of 1,9 cm. The vein of set 3 crosses
striking with 269° and an aperture of just 0,3 cm. Both veins got blocky crystals. The set 3
veins documented here offset the set 1 vein sinistral at several positions. All veins seem to
be tabular.
35
ST-AR-029
Figure 34: Displays age relationship 029. Here set 3 gets crossed by set4.
On this image the case of set 3 getting crossed by set 4 got documented (Figure 34). The
picture was taken in quadrant A1 and displays age relationship 029. The inclination of the set
3 vein measures 007°/86° and it got an aperture of just 0,1 cm. The younger set 4 veins
inclination is 314°/63° and the aperture counts 0,6 cm. The image also documents the set 4
vein offsetting the set 3 vein slightly dextral. We can also say that the set 3 vein seems to be
slightly curved.
Images of set 4
The veins of set 4 are the youngest ones documented on the pavement. They strike from SW
to NE and cover a range of values from 30° to 50° striking. Just one case was documented in
which a set 3 vein even crossed a set 4 vein.
36
ST-AR-030
Figure 35: Displays age relationship 030. Set 4 gets crossed by set 3.
Here the single case is documented where it seems that a set 4 vein got crossed by a set 3
vein even though set 3 is generally the older set (Figure 35). The crossed set 4 vein got an
inclination of 141°/75° and an aperture of 0,4 cm. The set 3 vein could just get measured by
a strike value of 101° and its aperture is 0,2 cm. Its structure is quite irregular and not
defined. The set 4 vein is slightly curved. The picture has been taken in quadrant C 2. Again all
veins got blocky crystals.
37
ST-AR-032
Figure 36: Displays age relationship 032.Set 1 gets crossed by set 4 and set 3. Set 3 gets crossed by set 4.
The image has been taken in the quadrant F1 and shows an age relationship between veins
of set 1, 3 and 4. The set 4 vein crosses the ones of set 1 and 3 and got an inclination of
121°/82° and an aperture of 0,2 cm. The next older vein of set 3 got an inclination of
022°/41° and aperture of 0,2 cm too. The oldest set 1 vein got an inclination measured with
091°/82° and an aperture of 1,2 cm. Interesting on this image again are the different offsets.
The set 1 vein gets offset dextral by set 3 and sinistral by set 4. The set 3 vein on the other
hand gets offset dextral by the set 4 vein. So it offsets the veins of set 3 and 1 sinistral and
dextral while they lay next to each other which could be interpretated as a result of
overthrusting.
38
5. Results & Discussion
All the data which could be collected in the field, images and measurements, finally leaded
to a contenting data set.
High resolution image of the Upper Gorge Pavement
Figure 37: Scaled map view of the outcrop. Image provides 435 Megapixels. Interesting chessfield like vein textures in the
western part of the outcrop visible.
The high resolution images which have been taken from the outcrop could get stitched
together to one big panorama picture which provides a good large scale overview, but also a
high resolute small scale view to spot tiny details. By using a grid of 2 by 2 m² quadrants as
base map layer it was even possible to rectify the high resolution image in ArcGis to achieve
a view perpendicular view to the outcrops surface. Most of the images that were taken
during the detail observation in the field could get integrated into the panorama image. This
supports the view on small scale details like additionally. That makes it even simple to locate
39
overprintings and other points of interest that have been documented on the outcrop. An
overview map of all intergrated details shall offer a better orientation to observe the details
in ArcGis.
Classified vein sets
Out of the measured vein data different
vein sets were defined by strike direction
and age relationship. Each set got a
Figure 38: Vein sets defined by strike values.
certain orientation which might help to
define stress fields in further steps.
Point based 3D-Model
Figure 39: Image 1 & 2 of the point based 3D-Model of the Upper Gorge Pavement.
Furthermore a point based 3D-Model based on stereophotogrammetry had been generated
by using Photomodeler Scanner (Figure 39). Through this method it might be possible to
40
achieve an accurate orthorectification of the panorama images. Ortho-images of that model
could officiate as a base map of higher precision.
41
5.1.
Comparison of Results
Through the methods that have been used it was already possible to achieve good results.
But properties of the outcrop and the spots from where the images have been taken caused
certain difficulties during the image acquisition. Especially the fact that the camera spot was
located just a few meters over the outcrop caused a low angle between the surface and the
spot. During the rectification process of the panorama image we achieved the perpendicular
view on the pavement. But therefore the image had to get distorted. The distortion on the
image increases the bigger the distance between camera and photographed object gets.
Even the stitching of the three panorama images caused some distortion between the
connected image frames because they had to get adjusted in size and orientation. So the
choice of a camera spot which provides an angle as close to 90° to the surface as possible
will reduce the distortion which appears during the rework of the data and will even simplify
the rectification process.
Also the high density of veins made it quite complicated to find and define different vein
sets and to classify them in different age relationships to each other. The cluster which was
used here helped to organize the huge amount of measurements. The choice to use a cluster
by using 10° steps made it easier to classify sets than using steps of 5°. Changing this fact
might lead to different results. This shows that the vein sets presented in this thesis are in
some way still liable to the interpretation of the author. Another challenge to define
classifications between the veins will be the analysis of the extremely irregular structures of
the veins on this pavement. To have a look on the intern structure of the veins in micro scale
might help understand their way of generation and might show the reasons of their irregular
way of spreading over the outcrop.
42
6. Conclusions & Outlook
Finally the author achieved all aims he wanted to provide in this Bsc.-Thesis:
The generated panorama image displays the outcrop with 435 megapixels which offers
remarkable properties for further observation. Even without the integrated detail images it
is still possible to spot veins smaller than 0,2 mm, styloliths or to see even overprintings.
The documentation pictures from the detail observation are integrated in the panorama and
function as even higher resolute points of interest. Furthermore they document the data on
which the following vein sets could get classified:
-
Set 1 striking from N to S as the oldest set
-
Set 2 striking from NW to SE as the next younger vein generation
-
Set 3 striking from W to E constantly overprinted just by set 4
-
Set 4 striking from SW to NE as youngest vein generation on the outcrop
Lastly the generated point based 3D-Model offers a new more accurate way of
orthorectification of the panorama picture. Furthermore it allows viewers to get a realistic
idea of the surface conditions of the outcrop. Even the surface textures and vein structures
are displayed.
6.1.
Workflow refining
The results that have been discussed before are still of a high quality. But reworking of the
data can be more effective.
On the one hand when generating an orthorectificated image it shall be important to find a
camera spot with the most high angle between a range of 0° and 90°. This will minimize the
error that develops during the processing of the rectified panorama through distortions.
The gridding of the pavement that was besides orientation also used for rectification of the
panorama on the generated base map grid, has the disadvantage to includes errors by little
imprecisions caused through the limited accuracy of the measuring equipment used during
the gridding process. The availability of orthorectificated base map images generated by a
3D-Model based on 3D-stereophotogrammetry gives an opportunity for further work steps
43
to optimize the accuracy of the panorama and will make measurements on the panorama
image more meaningful.
44
7. Ackknowledgement
At this part of the thesis the author would like to thank Prof. Janos Urai for the amazing
opportunity to work on this unique outcrop in the Oman Mountains as term of this Bsc.Thesis. I really enjoyed the field work in our team and want to thank Simon Virgo and Max
Arndt for an excellent supervision during our trip and back home during the rework, for
inspiring ideas and good support always when needed. Finally I want to thank the rest of our
team: Patrick Wüstefeld, Alexander Raith and Ben Laurich for their good company and
motivation during the whole time.
Special thanks to Bastian Kreienbaum for his patience and his language skills.
45
8. References

Adobe. (2009). Photoshop CS3 Users Guide. San Jose, California.

Al-Latzki, A. S. (2002). A crustal transect across the Oman Mountains on the eastern margin of Arabia.
GeoArabia, Vol. 7, No. 1 , pp. 47-78.

Eos Systems Inc. (n.d.). http://www.photomodeler.com/. Retrieved September 2010, from
http://www.photomodeler.com/: http://www.photomodeler.com/products/scanner/tutorials.htm

Gealey, W. (1977, August). Ophiolite obduction and geologic evolution of the Oman Mountains and
adjacent areas. Geological Society of America Bulletin, v. 88 , pp. 1183-1191.

Glennie, K. M. (1973). Late Cretaceous Nappes in Oman Mountains and Their Geologic Evolution.
AAPG Bulletin, Vol. 57 .

Holland, M. U. (2009b). Evolution of fractures in ahighly dynamic thermal, hydraulic, and mechanical
system; (I), Field observations inMesozoic carbonates, Jabal Shams, Oman Mountains.
GeoArabia[Manama] 14(1) , pp. 57-110.

Immenhauser, A. S.-S. (1999, March). Late Aptian to late Albian sea-level fluctuations constrained by
geochemical and biological evidence (Nahr Umr Formation, Oman). Journal of Sedimentary Research
no. 2 , pp. 434-446.

Kolor. (2009). autopano pro & autopano giga users manual version 2.

Philip, J. B.-M. (1995, December). Cenomanian-Early Turonian carbonate platform of northern Oman:
stratigraphy and palaeo-environments. , Palaeogeography, Palaeoclimatology, Palaeoecology Volume
119, Issues 1-2 , pp. 29-69.

Smith, A. S. (1990, March). Cenomanian echinoids, larger foraminifera and calcareous algae from the
Natih Formation, central Oman Mountains. Cretaceous Research, Volume 11, Issue 1 , pp. 29-69.
46
9. Appendix
9.1.
Measured vein data
47
9.2.
Detail Overview
Figure 40: Overview image including all integrated details. Each detail got labeled: Age relationships in red, other points of interests yellow.
48
9.3.
Zooming in the high resolution image
Zoom overview
49
ZZoooom
m sseerriieess 11
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m sseerriieess 22
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Zoom series 5
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