OCT and NMR for non-invasive in-situ monitoring of the vulnerability of rock art monuments

Elizabeth Bemand, Martin Bencsik, Haida Liang

School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, United Kingdom

ABSTRACT

This paper will introduce a new application of Optical Coherence Tomography (OCT) to the monitoring of vulnerability of rock art monuments in-situ. The porosity of the host rock is an important factor affecting the susceptibility of rock art monuments to decay. Pore characteristics of rocks are one of the main factors that control the intensity of physical deterioration. OCT has successfully been applied to paintings and archaeological objects, including geological materials, to produce cross sectional images non-invasively. The stack of cross sectional images can be rendered as a volume to visualise the structure in depth over an extended area. Preliminary studies show that it can directly image the pores and subsurface structure to within 500microns of the surface depending on lithology. This study aims to analyse this stack of cross sectional images computationally to enable the description of the pore space distribution which will be compared with spatially resolved NMR porosity measurement for the samples.

Keywords; optical coherence tomography, porosity, sandstone, rock art, NMR, non-invasive, in-situ.

1. INTRODUCTION

Rock art monuments provide a link to our ancient cultural past, possessing seeming permanence over both physical and cultural landscapes. However, they often reside in rural landscapes that are under continuous threat from human and agro-industrial activity. The rock art monuments under consideration for this study are sandstone panels outcropping from bedrock within the Fell Sandstone Group at the locations of Chatton Park and Weetwood Moor in Northumberland, United Kingdom. The rock art consists of incised carvings of abstract cup and ring motif, varying from eroded cup marks to complex cups with multiple rings and interconnected grooves (Fig. 1). Surface recession due to weathering presents a serious threat to decorated rock surfaces in-situ.

Sandstones are principally composed of mineral grains and pore space. Porosity is defined as the ratio of volume of pore space to the bulk volume of the material. The volume and distribution of which affects the behaviour of stones over time 1,2,3,4. Pores form natural pathways for the flow of water through the rock5, enhancing chemical, physical and biological weathering processes. Standard methods for investigating porosity are dependent on sampling and as such are not suitable for continuous monitoring of culturally important materials. The demand for thorough characterisation of materials used in cultural heritage requires development of advanced diagnostic methods6,7.

O3A: Optics for Arts, Architecture, and Archaeology III, edited by Luca Pezzati, Renzo Salimbeni, Proc. of SPIE Vol. 8084, 80840H · © 2011 SPIE · CCC code: 0277-786X/11/$18 · doi: 10.1117/12.890084

Proc. of SPIE Vol. 8084 80840H-1

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

Figure 1. Rock art panel featuring elaborate motif of cups multiple rings and interconnecting grooves (Chatton Park Northumberland, U.K.)

2. MATERIALS

The two selected materials were collected from the sites of rock art panels at Weetwood Moor (WM) and Chatton Park (CP) in Northumberland. Samples were cobble sized and selected as proxy samples based on visual analysis compared to the rock art panels. Both samples were within the Fell Sandstone Group, a stratigraphical horizon predominately containing sandstones formed in a carboniferous age deltaic environment.

The sandstones were fine (WM) and medium (CP) grained quartz arenites, principally composed of quartz with minor amounts of feldspar. The samples were prepared by coring with a 25mm water cooled bit then sliced into ~7mm thick discs using a diamond edged water cooled saw.

3. OCT IMAGING OF SANDSTONE POROSITY

3.1 Image acquisition

This method uses a Thorlabs SROCT operating at a wavelength of 930nm, an axial resolution of 6.5µm and transverse resolution of 9µm. A piezoelectric scanning mirror within the OCT probe enables rapid scanning in the transverse x-range. Scans were taken using 600 depth scans (A-scans) over a 6mm transverse x-range, at a working distance of ~1cm. The probe is attached to a motorized micrometer linear stage; to obtain an image cube, the stage scans in the transverse y-direction perpendicular to the x-range of the scan. Figures 2 and 3 show image volumes for the Chatton Park and Weetwood Moor sandstone samples.

Figure 2. Volume rendering of Chatton Park sandstone sample (bounding box 6x6x0.9mm)

Proc. of SPIE Vol. 8084 80840H-2

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

Figure 3. Volume rendering of Weetwood Moor sandstone sample (bounding box 6x6x0.9mm)

3.2 Speckle reduction

Figure 4. Single image from stationary scan with no averaging (6mm x 0.9mm) of a WM sample

Like other coherent imaging techniques, OCT suffers from speckle noise. Most of the granular pattern on the image in Fig. 4 does not correspond to the real rock microstructure; the graininess produced in the image deteriorates the image quality. Speckle noise makes detection of boundaries and image segmentation problematic making it difficult to differentiate pore structures. Improvement of image quality after speckle reduction can be judged on how the desirable information, in this case how the pore microstructure can be perceived from the image. The following analyses are based on a fine grained sandstone sample from Weetwood Moor.

Figure 5 shows an image from a scan taken with an averaging factor of 5 which produces 3000 finely spaced depth scans and averages every 5 of them producing a final cross-section image of 600 A-scans.. The five A-scans averaged are not at exactly the same position and so will have different speckle patterns which average out. A comparison between Fig. 4 and 5 shows clear improvement in image quality after averaging in the transverse x-direction.

Figure 5. Image of a slice taken with an A-scan averaging factor of 5 (6mm x 0.9mm).

Proc. of SPIE Vol. 8084 80840H-3

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

Figure 6. Image contrast as a function of the number of slices averaged in the transverse y-direction to produce median intensity values using an A-scan averaging factor of 5 in the transverse x-direction.

An image contrast is defined as

minmax

minmax

IIIIcontrast

+−

=,

where maxI is the maximum intensity in the image and minI is the minimum intensity in the image. In this case the

contrast calculated is for the images in logarithmic intensity scales.

As shown in Fig. 6, the image contrast levels off at around 5 median averaged slices. Therefore an image produced from a median of 5 slices in the transverse y-direction (spatially separated by 2 microns) with an A-scan averaging factor of 5 in the x-direction is the optimum for removing speckle noise which results in a decrease in the image contrast caused by speckle (Fig. 7a)

3.3 Image segmentation

Segmentation is an important step for feature extraction, subdividing an image into its constituent regions or phases. Since the sample has only two phases, pore and sandstone, segmentation based on a threshold value is appropriate and computationally simple and fast.

A threshold value is selected and the grey scale image is divided into a binary image of groups of pixels having intensity values less than the threshold and groups of pixels with values greater or equal to the threshold. One threshold value was selected for the entire image based on the image histogram. Black colour in segmented image indicates pore space and white indicates the solid matrix of the sandstone (Fig. 7b).

Proc. of SPIE Vol. 8084 80840H-4

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

JIiii30 50

(a)

(b)

(c)

Figure 7a) Grey scale image produced from the median pixel intensity value of 5 slices with an A-scan averaging factor of 5 in the transverse x-direction (6mm x 0.9mm), where pore space is dark and sandstone matrix is bright (6mm x 0.9mm); b) Binary image

computed by thresholding image in a), where pore space is black and sandstone matrix is white; c) Outlines of pore regions identified from thresholded image b) superimposed on the grey scale image.

The accuracy of segmentation determines the eventual success or failure of automated pore analysis, for this reason it is necessary to select a proper threshold value to differentiate the pore space and solid matrix. The effect of the threshold value from a range of pixel intensity values used to discriminate the pore space from the solid matrix is given in Fig. 8 for the number of pore regions found.

Figure 8. a) Number of pore regions identified as a function of threshold level; solid line represents pore regions correctly identified, dashed line indicates regions incorrectly identified as pore space. b) Histogram of pixel intensities for the image used in the analysis.

Proc. of SPIE Vol. 8084 80840H-5

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

Using automated thresholding can result in large portions of the image being assigned to the wrong phase (pore or rock matrix) due to an incorrect threshold value. Visual inspection of slices is required to determine a proper threshold value. In this case visual evaluation of appropriate threshold level can be performed on a single slice of a stack.

In a test image area of 6000 x 300 microns, 23 pore regions were found by the automated analysis and an additional 13 pore regions were identified by visual inspection. The distribution of pore diameters found is shown in Fig. 9.

(a) (b)

Figure 9. Pore size distribution for diameter of pores found by automated analysis (a) and additional pores identified by visual inspection (b).

Automated analysis of pores indicates a porosity of 6.8% within a depth of 300 microns into the sample, but the total porosity is found to be 8.4% after including the additional pores found by visual identification. This porosity is likely to be a lower limit since OCT measurements can miss the small pores that are too small to be spatially resolved by the system. Given the resolution of the OCT, the minimum pore size that can be resolved is likely to be twice the resolution element, i.e. of the order of 20 microns. However, in reality this number is likely to be larger because light going through the rock matrix is multiple scattered which degrades the resolution even further.

4. NMR POROSITY MEASUREMENT

Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique which in this case relies on the detection of the spatially resolved NMR signal from a hydrogen containing fluid (e.g. water). It can therefore be used to investigate the presence of water in porous media. Our method uses a portable MRI device, the Mobile Universal Surface Explorer (NMR-MOUSE®) which allows scanning of samples placed outside the instrument. The NMR can provide depth profiles up to 10mm into the surface and measure the porosity and in some cases pore size distribution in fluid saturated samples. Such devices are used extensively in the oil industry for well logging8 but have recently also been shown to be suitable for cultural heritage applications9,10.

Proc. of SPIE Vol. 8084 80840H-6

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

Figure 10. Spatially resolved porosity measured by NMR for WM sample. The horizontal axis gives the depth in millimetres. The vertical axis gives the fractional porosity at each depth.

Prior to NMR measurement, dry sandstone cores were immersed in water for 120 hours to allow water to infiltrate the pore space as fully as possible under normal conditions. Gravimetric measurements were taken before and after NMR profiles to verify the porosity and monitor drying during the measurements. Gravimetric measurements indicate water filled porosity of 13.36% however this value may be affected by water adhering to the surface of the sample.

Figure 10 shows a profile of the porosity through the sample from NMR measurements, the peaks at the extremities of the profile are due to water pooling by capillary action within the sample holder. The mean porosity found excluding these peaks is (7.8±0.5)% for the Weetwood Moor sample. This value indicates the volume of pores infiltrated by water under normal conditions and is therefore an underestimate of the true porosity. The error estimate does not include such systematic effects.

5. CONCLUSIONS

It has been shown that the OCT used in this study can directly image the pore microstructure of sandstone to within 500 microns of the surface and have effectively detected pores of diameters greater than 40 microns within a depth of 300 microns. Automated pore volume analysis is currently ~80% effective, dependent upon the original quality of the image and the use of an appropriate segmentation technique prior to pore finding.

OCT measurements indicate a total porosity of 8.4% in the top 300 micron of the sample; this may be compared to the bulk effective porosity given by NMR measurement of 7.8%±0.5%. NMR can only detect the volume of pores that have been infiltrated by water; the presence of pores too small or insufficiently connected to be penetrated by water under normal conditions is likely to be missed. If such pores exist then the NMR porosity is also likely to be an underestimate. The gravimetrically measured porosity of 13.36% is likely to be an overestimate due to residual surface water.

This combination of techniques can be used to investigate the porosity and pore characteristics of sandstone non-invasively. If changes of the surface and subsurface structure are monitored at a microscopic scale, the decay of decorated rock surfaces may be detected at an early stage11,12,13; informing conservation decisions to protect rock art panels.

ACKNOWLEDGEMENTS

Thanks to Sebastian Payne and Kate Wilson from English Heritage and the technical staff at British Geological Survey Keyworth site for their help preparing rock samples; Rebecca Lange at Nottingham Trent University for help with software development..

Proc. of SPIE Vol. 8084 80840H-7

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms

This collaborative research project is jointly funded by the UK Arts and Humanities Research Council and the Engineering and Physical Sciences Research Council through the Science and Heritage Programme (www.heritagescience.ac.uk).

REFERENCES

[1] Turkington. A. V., Paradise, T. R. "Sandstone Weathering: a century of research and innovation." Geomorphology 67, 229-253 [2] Franklin, J.A., Dusseault, M.B,. [Rock Engineering Applications]. McGraw-Hill. 600pp. (1991) [3] Tugrul, A.,” The effect of weathering on pore geometry and compressive strength of selected rock types from Turkey”. Engineering Geology 75, 215-227 (2004) [4] Oguchi, C.T., Matsukura, Y., .” Effect of porosity on the increase in weathering-rind thicknesses of andesite gravel”. Engineering Geology 55, 77-89 (1999) [5] Yu, S., Oguchi. C.T., “ Role of pore size distribution in salt uptake, damage and predicting salt susecptability of eightypes of Japense building stones” Engineering Geology 115, 226-236 (2010) [6] Liang, H., Gomez Cid, M., Cucu, R.G., Dobre, G.M., Podoleanu, A., Pedro, J., Saunders, D., “En-face optical coherence tomography – a novel application of non-invasive imaging to art conservation”. Optics Express 13, 6133-6144 (2005) [7] Yang, M. L., Lu, C.W., Yang, C. C., “The use of optical coherence tomography for monitoring the subsurface morphology of archaic jades.” Archaeometry. 46, 171 (2004) [8] Kleinburgh, R. L. [Well Logging, Encyclopaedia of Nuclear Magnetic Resonance], John Wiley & Sons, 4960-4968. (2002) [9] Poli, T., Toniolo, L., Valentini, M., Bizzaro,G., Melzi, R., Tedoldi, F., Cannazza, G., Journal of Cultural Heritage 8, 134-140 (2007) [10] Borgia, G.C., Camaiti, M., Cerri, F., Fantazzini, P., Piacent, F., “Study of water penetration in rock materials by Nuclear Magnetic Resonance Tomography: hydrophobic treatment effects.” Journal of Cultural Heritage 1, 127-132. (2000) [11] Benavente, D. , Garcia del Cura. M.A., Fort. R., Ordonez. S., ”Durability estimation of porous building stones from pore structure and strength”. Engineering Geology 74, 113-127 (2004) [12] Ozturk, A.U., Baradan, B. “A comparison study of porosity and compressive strength mathematical models with image analysis”. Computational Materials Science 43, 974-979 (2008) [13] Pope, G.A. ‘Weathering of petroglyhs: direct assessment and implications for dating metods” Anitquity 74, 833-843 (2000)

Proc. of SPIE Vol. 8084 80840H-8

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/18/2013 Terms of Use: http://spiedl.org/terms