the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jun 2024
Ground ice estimation in permafrost samples using industrial Computed Tomography
Abstract. The distribution and abundance of ground ice in permafrost is a fundamental property that determines the potential for thaw subsidence and terrain effects of permafrost landscapes. However, most methods to characterize permafrost are destructive and of low resolution. Here, some of the limitations of traditional destructive methods are overcome using industrial computed tomography (CT) scanning to systematically log permafrost cores, visualize cryostructures, measure frozen bulk density, and estimate volumetric and excess ice contents non-destructively. The results show strong agreement with destructive analyses as well as recent developments using a multi-sensor core logger (MSCL), demonstrating that these approaches can produce consistent results, and provide the added benefit of enhanced digital archives of permafrost physical properties. Development of standardized and interoperable methods for permafrost characterization will build more robust permafrost datasets and strengthen efforts to understand future thaw trajectories of permafrost landscapes.
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RC1: 'Comment on egusphere-2024-1353', Michel Allard, 05 Jul 2024
Comments on manuscript “Ground ice estimation in permafrost samples using industrial Computed Tomography.”
By M. Roustaei, J. Pumple, J. Harvey, and D. Froese
Over roughly the past 20 years, computed tomography scanning (CT-Scan) has been proven as a fantastic technology to image undisturbed, frozen, permafrost cores. We must add that Ct-Scan appeared in permafrost science about at the same time as portable drills became of widespread use after the initial publications of Calmels et al. (2005, 2004), making easy the sampling of intact permafrost cores by field researchers. Before the application of Ct-Scan to permafrost, destructive methods were almost exclusively available to measure permafrost properties and quantitatively determine its composition in sediments, ice, water and gaz. This manuscript brings forward useful and interesting new advances in the use of CT-Scan.
I am of the opinion, however, that the manuscript needs substantial revisions in order to be raised to the level of a strong journal paper. As it is now, it appears more as a methodological technical report of medium quality. It presents comparative results between automated methods and destructive methods, making a good validation of CT-Scan analysis (and MCSL) and a demonstration of capabilities. The paper can be very much improved on three aspects: 1- it should relate the approaches and the results with some fundamental permafrost concepts and key properties, for instances cryotexture, cryostructure and phase composition. 2- it should have at least an integrated paragraph on general theorical aspects of CT-Scan in permafrost explaining basic concepts such as energy, scanning time, sample size, voxel size (defining resolution and limits of detection of constituents), and image segmentation based on distribution of density units. 3- Important: improve the writing, argumentation and style. Make technological details easily understandable for general readers of the permafrost community.
In the following lines, I refer to key comments already in the revised pdf that I am also sending back with this assessment. More notes, mostly stylistic suggestions, are to be found on the revised pdf.
Lines 10-20: The introductory statements could be stronger. Why not say that permafrost contains various types of ground ice: pore, segregation, aggradational (a variant of segregation ice), wedge ice, intrusive, massive, etc. that create a variety of cryostructures and cryotextures (cite permafrost glossary, NRC, 1988). Those types of ground ice are associated with diverse landforms. Melting of ground ice generates thaw settlement. Being able to analyse frozen, undisturbed, cores of permafrost allows to better understand how it formed, internal permafrost transforming processes and predict the amplitude of thaw settlement and consolidation. CT-scan offers the capability to analyse cores and, even, measure some basic properties from image analysis, such as density and thermal conductivity.Line 18. You seem to focus on excess ice because it is important for engineering and geomorphic applications. But excess ice is only one component. With CT-scan much more can be done. Excess ice (i.e. ice content greater than natural void ratio) is only one case. CT-scan can image all kinds of ice and support development of new knowledge. Quantifying excess ice is one problem among many.
Line 20. It is the reverse that is true. It is thaw settlement that is proportional to ground ice content.
Line 28. I suggest you consult the definition of cryotextures (micro, sub-visual) and cryosructures (macro, visible) in the permafrost glossary and in some key papers (see for instance Shur and French and Murton and French...).
Lines 30-35. See also Ducharme et al. (2015) (in GeoQuébec) for the determination of thermal conductivity of permafrost based on sediment, ice and air contents through CT-scan.
Lines 38-39. What classification methods are you refereeing to here? what are these classification methods? How are permafrost cryostructures classified, on what principles? by who? add references. Classification is a mental exercise. How can methods can be be limited?
Line 40. Five cores make for a very small sample size given the immense variety of permafrost settings and characteristics in natural conditions. It seems your results are more a report on a few methodological tests to ultimately develop the technological application on a wider scale. You are making a demonstration.
Line 43. It is unclear to me if the water standard that you scan with the core is liquid or ice at the time of scanning since it is enclosed within insulation cooled with freeze packs. One calibration point of density is air (?), the other is either liquid water or ice (no?).
Lines 48-49. I agree that results of this study will improve data acquisition. But too much generalization seems an over ambitious goal statement at this testing stage.
Line 52. Why try to limit lateral heterogeneity? drilled cores are by nature unidimensional samples.
Line 79. You introduce the word “cuboid method” in the text. Not all readers know of your lab terminology. You must explain. Why subsampling small cubes is necessary? Is it just to make comparisons with destructive methods or also to allow scanning at higher resolutions (25 micrometers voxels) on subsamples of smaller sizes? both?
Line 85. “takes advantage of the ice-rich properties of frozen cores which allow for a greater degree of sampling precision” A kind of overstatement. I guess cutting nice and smooth-faced cubes in coarser soils (sand, granules...) would be more difficult...
Line 93. Lin et al. 2020. Not in references at the end. What is this equation? I gave a quick look at this reference and did not see an equation for volumetric ice content (?), only eq. 1. Check.
Line 121. How many minutes or seconds is the duration of a scan for one sample? does the sample have the time to start melting? Do you have a freezer at hand from where you can take the sample out for a very short duration?
Line 124. Specify in the text what is the maximum sample size (height, width, volume) than can fit in your industrial CT-scanner.
Line 126. A general comment. I think it would be useful to have a paragraph describing the relationship in CT-scanning between sample volume, resolution (minimum voxel size), scanning duration and energy. You could comment on the choice of these parameters for different goals of research (general permafrost characterization, search of maximum details possible in cryotexture, defining standardization methods, interpret permafrost incipience and growth, etc.
Line 138. Please explain more...why aluminium? I guess a given density (?) frozen or not? it is better explained in Pumple et al., 2024
Line 139. Is this in the frozen water vial? other question: was that standard made of water frozen in the freezer? it may have contained less air bubbles than the excess permafrost ice, hence the apparent density difference (?).
Line 142. i.e. you identified on images a group of pixels on a slice with the same density as the standard. Correct?
Line 166. Subtitle of section 3. Reorganize subtitles and some text accordingly: I think it is better to separate results (presentation of obtained results) and discussion (comments on new methodological improvements and findings, limitations met, new potential, some autocriticism..).
Line 170. Replace “close densities” by “narrow unimodal density distributions”.
Lines 173-174. “The voxel size can impact the image segmentation through the partial volume effect which relates directly to the finite spatial resolution of the scan and for geological samples, the grain size distribution” This is uselessly complex language and poor pedagogy. This simply means that the voxels in CT-scan currently always contain mixtures of sediments, ice, unfrozen water and gaz. This is more so for finer grain size materials because their pore size is smaller.
Line 179. In fact, you do not differentiate 5 different materials, but rather 5 different classes of sediment/ice composition ratios. Do you observe some different micro-structural elements (mico-lenses, cristals, etc.) in those different classes?
Line 184. On figure 5, the air distribution fits closely with the sediment-poor ice (segregated ice lenses in the matrix of the till). How air is discriminated: in bubbles (black in F)?
Line 231. This highlight the effects of difference in location of ROIs in the permafrost mass. General remark: when you make measurements of small volumes or sub-samples in a heterogenous medium like till, it is normal to find spatial variations. This raises the question of what sampling or sensing volume is pertinent for a given geological material in a permafrost characterization study.
Line 243. Here I find it interesting that despite the absence of visible micro ice lenses (or not mentionned), the uniformly textured permafrost contains a fraction (5% red line on C) of excess ice, i.e. above void volume. But you show no Cuboid-EIC...Why?
Line 251. What do you think was the effect of pressing peat samples a little bit like sponges? Discuss.
Line 253. Same question/comment. To accurately determine what is ice and what is sediment, one needs to detect at a resolution equal or smaller than pore space. This is barely possible in sand but difficult with these methods in silt, and worse in clay. With your capacity to reach a 25 micron resolution, you are seriously improving the potential use of CT Scan, but it requires subsampling in the larger volume. Then you have to recompute the total across the whole volume. An interesting problem. But we could learn a lot about permafrost by doing this.
Line 281. A question: in figure 14, peat samples on the graphs seem to plateau at about 45% excess ice (?). Would you think that the ice-forming capability or ice concentration process in peat is different than in mineral soils? only saturation by pore ice between organic grains and fibers? Is there possibility of water migration and segregation?
Line 293. You shouid comment on why Ct-Scan provides valid measurements of excess ice contents. Is it because most of the excess ice content occupies volumes larger than voxel size (25 micrometers), i.e. larger than the average void size?
Line 300. Is there a reason why you do not refer to resolution as voxel size, and keep a distinction between the two in the text? Maybe I am wrong and do not understand something..(?)
Line 304. Why do you think excess/visible ice has a lower density than pore ice? presence of air bubbles? see Ducharme et al., 2015 and also Slusarchuck and Watson, 1975.
Line 307. Do really organic material and ice have close densities? This is an artefact of the method. In fact organic matter is very light. Therefore, saturated peat always contains abundant ice. As a result, the average density of saturated frozen peat is slightly below density of ice (0.9).
Line 321. My opinion: there is so much yet to discover and for improving our understanding of permafrost with Ct-Scan. Prioritizing automation, machine learning, big databases and AI will not compensate for scientific culture and will lead to more technological wandering and not so many new discoveries.
Figures:
Figure 1. The caption should provide better definitions of what it contains; the reader needs to search the info in text : black cuboids-true sub-samples for destructive analysis; Pink virtual cuboids for comparisons by CT-scan; center line for MSCL at 10 mm resolution, overlapping blue circles for comparative high resolution spots (5mm 25 microns) CT, etc.
Figures 6-10. I suggest that the axis title in C should simply be Ice content (%), because ice contents from many modes of measurements are shown, not only excess ice.
- RC2: 'Comment on egusphere-2024-1353', Anonymous Referee #2, 14 Jul 2024
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