Relevant bibliographies by topics / Rock image

Academic literature on the topic 'Rock image'

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Published: 14 March 2023

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Journal articles on the topic "Rock image"

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Otiede, David, and Ke Jian Wu. "The Effect of Image Resolution on the Geometry and Topological Characteristics of 3-D Reconstructed Images of Reservoir Rock Samples." International Journal of Engineering Research in Africa 6 (November 2011): 37–44. http://dx.doi.org/10.4028/www.scientific.net/jera.6.37.

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The effect of image resolution on the measured geometry and topological characteristics of network models extracted from 3-D micro-computer tomography images has been investigated. The study was conducted by extracting geologically realistic networks from images of two rock samples, imaged at different resolutions. The rock samples involved were a Castlegate Sandstone and a Carbonate-28 reservoir rock. Two-dimensional images of these rocks were obtained at a magnification of ×50. The carbonate sample was studied at two different resolutions of 0.133 microns and 1.33 microns, while the sandstone was studied at 5.60 microns. Three-dimensional images of these 2-D images were obtained via image reconstruction, to generate the pore architecture models (PAMs) from which networks models of the imaged rocks were extracted with the aid of Pore Analysis software Tools (PATs). The measured geometry and topology (GT) properties included Coordination Number, Pore Shape Factor, Pore Size Distribution, and Pore Connectivity. The results showed that the measured geometry-topology (GT) characteristics of a network model depend greatly on the image resolution used for the model. Depending on the micro-structure of the reservoir rock, a minimum image resolution is necessary to properly define the geometrical and topological characteristics of the given porous medium.
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Saxena, Nishank, Ronny Hofmann, Amie Hows, Erik H. Saenger, Luca Duranti, Joe Stefani, Andreas Wiegmann, Abdulla Kerimov, and Matthias Kabel. "Rock compressibility from microcomputed tomography images: Controls on digital rock simulations." GEOPHYSICS 84, no. 4 (July 1, 2019): WA127—WA139. http://dx.doi.org/10.1190/geo2018-0499.1.

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Rock compressibility is a major control of reservoir compaction, yet only limited core measurements are available to constrain estimates. Improved analytical and computational estimates of rock compressibility of reservoir rock can improve forecasts of reservoir production performance and the geomechanical integrity of compacting reservoirs. The fast-evolving digital rock technology can potentially overcome the need for simplification of pores (e.g., ellipsoids) to estimate rock compressibility as the computations are performed on an actual pore-scale image acquired using 3D microcomputed tomography (micro-CT). However, the computed compressibility using a digital image is impacted by numerous factors, including imaging conditions, image segmentation, constituent properties, choice of numerical simulator, rock field of view, how well the grain contacts are resolved in an image, and the treatment of grain-to-grain contacts. We have analyzed these factors and quantify their relative contribution to the rock moduli computed using micro-CT images of six rocks: a Fontainebleau sandstone sample, two Berea sandstone samples, a Castelgate sandstone sample, a grain pack, and a reservoir rock. We find that image-computed rock moduli are considerably stiffer than those inferred using laboratory-measured ultrasonic velocities. This disagreement cannot be solely explained by any one of the many controls when considered in isolation, but it can be ranked by their relative contribution to the overall rock compressibility. Among these factors, the image resolution generally has the largest impact on the quality of image-derived compressibility. For elasticity simulations, the quality of an image resolution is controlled by the ratio of the contact length and image voxel size. Images of poor resolution overestimate contact lengths, resulting in stiffer simulation results.
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Tawfeeq, Yahya Jirjees, and Jalal A. Al-Sudani. "Digital Rock Samples Porosity Analysis by OTSU Thresholding Technique Using MATLAB." Iraqi Journal of Chemical and Petroleum Engineering 21, no. 3 (September 30, 2020): 57–66. http://dx.doi.org/10.31699/ijcpe.2020.3.8.

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Porosity plays an essential role in petroleum engineering. It controls fluid storage in aquifers, connectivity of the pore structure control fluid flow through reservoir formations. To quantify the relationships between porosity, storage, transport and rock properties, however, the pore structure must be measured and quantitatively described. Porosity estimation of digital image utilizing image processing essential for the reservoir rock analysis since the sample 2D porosity briefly described. The regular procedure utilizes the binarization process, which uses the pixel value threshold to convert the color and grayscale images to binary images. The idea is to accommodate the blue regions entirely with pores and transform it to white in resulting binary image. This paper presents the possibilities of using image processing for determining digital 2D rock samples porosity in carbonate reservoir rocks. MATLAB code created which automatically segment and determine the digital rock porosity, based on the OTSU's thresholding algorithm. In this work, twenty-two samples of 2D thin section petrographic image reservoir rocks of one Iraqi oil field are studied. The examples of thin section images are processed and digitized, utilizing MATLAB programming. In the present study, we have focused on determining of micro and macroporosity of the digital image. Also, some pore void characteristics, such as area and perimeter, were calculated. Digital 2D image analysis results are compared to laboratory core investigation results to determine the strength and restrictions of the digital image interpretation techniques. Thin microscopic image porosity determined using OTSU technique showed a moderate match with core porosity.
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Nababan, Benyamin Elilaski, Eliza Veronica Zanetta, Nahdah Novia, and Handoyo Handoyo. "ESTIMASI NILAI POROSITAS DAN PERMEABILITAS DENGAN PENDEKATAN DIGITAL ROCK PHYSICS (DRP) PADA SAMPEL BATUPASIR FORMASI NGRAYONG, CEKUNGAN JAWA TIMUR BAGIAN UTARA." Jurnal Geofisika Eksplorasi 5, no. 3 (January 17, 2020): 34–44. http://dx.doi.org/10.23960/jge.v5i3.34.

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Reservoir rock permeability and porosity are physical properties of rocks that control reservoir quality. Conventionally, rock porosity and permeability values are obtained from measurements in the laboratory or through well logs. At present, calculation of porosity and permeability can be calculated using digital image processing / Digital Rock Physics (DRP). Core data samples are processed by X-ray diffraction using CT-micro-tomography scan. The result is an image model of the core sample, 2D and 3D images. The combination of theoretical processing and digital images can be obtained from the value of porosity and permeability of rock samples. In this study, we calculated porosity and permeability values using the Digital Rock Physics (DRP) approach in sandstone samples from the Ngrayong Formation, North East Java Basin. The results of the digital image simulation and processing on the Ngrayong Formation sandstone samples ranged in value from 33.50% and permeability around 1267.02 mDarcy.
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Chen, Yulong, and Hongwei Zhang. "An Improved C-V Model and Application to the Coal Rock Mesocrack Images." Geofluids 2020 (July 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/8852209.

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In order to accurately and comprehensively obtain information about coal rock mesocrack images, image processing technique based on partial differential equation (PDE) is introduced in order to expound on the active contour model without edges and overcome the deficiency of the C-V model. The improved C-V model is adopted in order to process mesoimages of coal rocks containing single and multiple cracks and obtain high-quality binary images of coal rock mesocracks and the effective characteristic parameters of coal rock mesostructures through quantitative processing, which will lay solid foundations for the follow-up research into coal rock seepage computation and damage calculation. Studies have shown that, compared to the original C-V model, the improved model achieves better image segmentation effects and more accurate quantitative information about coal rock mesostructures for coal rock mesoimages with low contrast ratios and nonuniform grayscale, a fact showing that it can be applied to the calculation of coal rock permeability and damage factors.
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Guo, Chenlu, and Zhiyuan Li. "Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction." Advances in Multimedia 2022 (March 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/3982892.

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Lithology identification of rocks is an important part in the field of oil and gas exploration, mineral exploration, and geological analysis. How to accomplish rock classification is a key issue for the further development of the geology industry. The current main method for classifying rock pictures containing background is to select sample points or disregard the disturbance of the background. For more accurate classification, the rock part extraction method for rock images containing boundaries is designed to eliminate the influence of background. First, the rock parts are extracted based on the image gradient information and color information, respectively. Then, the two images are intersected to realize the refinement of pixel-level information to obtain a pure rock image. Ensemble ResNet18 (ERN18) is designed as an image classification model. It contains basic blocks to reduce the loss of features during the training process. The method breaks the neglect of most previous studies on background interference. The effect of misclassification in certain regions on the results is eliminated by ensemble learning based on the voting method. The classification results are further improved. Compared with the effects of LeNet, AlexNet, and ResNet, ERN18 has achieved significant results.
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Guha, A., and K. Vinod Kumar. "Potential of thermal emissivity for mapping of greenstone rocks and associated granitoids of Hutti Maski Schist belt, Karnataka." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 423–30. http://dx.doi.org/10.5194/isprsarchives-xl-8-423-2014.

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In the present study, different temperature-emissivity separation algorithms were used to derive emissivity images based on processing of ASTER( Advanced spaceborne thermal emission and reflection radiometer) thermal bands. These emissivity images have been compared with each other in terms of geological information for mapping of major rock types in Hutti Maski schist Belt and its associated granitoids. Thermal emissivity images are analyzed conjugately with thermal radiance image, radiant temperature image and albedo image of ASTER bands to understand the potential of thermal emissivity in delineating different rock types of Archaean Greenstone belt. The emissivity images derived using different emissivity extraction algorithms are characterised with poor data dimensionality and signal to noise ratio. Therefore, Inverse MNF false-colour composites(FCC) are derived using bands having better signal to noise(SNR)ratio to enhance the contrast in emissivity. It has been observed that inverse-MNF of emissivity image; which is derived using emissivity-normalisation method is suitable for delineating silica variations in granite and granodioritic gneiss in comparison to other inverse- MNF-emissivity composites derived using other emissivity extraction algorithms(reference channel and alpha residual method). Based on the analysis of ASTER derived emissivity spectra of each rocks, band ratios are derived(band 14/12,band 10/12) and these ratios are used to delineate the rock types based on index based FCC image. This FCC image can be used to delineate granitoids with different silica content. The geological information derived based on processing of ASTER thermal images are further compared with the image analysis products derived using ASTER visible-near-infrared(VNIR) and shortwave infrared(SWIR) bands. It has been observed that delineation of different mafic rocks or greenstone rocks(i.e. separation between chlorite schist and metabasalt) are better in SWIR composites and these composites also provide comparable results with thermal bands in terms of delineation of different types of granitoids.
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Wang, Cong, Zian Zhang, Yongqiang Zhang, Rui Tian, and Mingli Ding. "GMSRI: A Texture-Based Martian Surface Rock Image Dataset." Sensors 21, no. 16 (August 10, 2021): 5410. http://dx.doi.org/10.3390/s21165410.

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CNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is affected. In this paper, we introduce a new dataset called “GMSRI” that is a mixture of real Mars images and synthetic counterparts which are generated by GAN. GMSRI aims to provide a set of Martian rock images sorted by the texture and spatial structure of rocks. This paper offers a detailed analysis of GMSRI in its current state: Five sub-trees with 28 leaf nodes and 30,000 images in total. We show that GMSRI is much larger in scale and diversity than the current same kinds of datasets. Constructing such a database is a challenging task, and we describe the data collection, selection and generation processes carefully in this paper. Moreover, we evaluate the effectiveness of the GMSRI by an image super-resolution task. We hope that the scale, diversity and hierarchical structure of GMSRI can offer opportunities to researchers in the Mars exploration community and beyond.
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Schneider, Marc H., Patrick Tabeling, Fadhel Rezgui, Martin G. Lüling, and Aurelien Daynes. "Novel microscopic imager instrument for rock and fluid imaging." GEOPHYSICS 74, no. 6 (November 2009): E251—E262. http://dx.doi.org/10.1190/1.3261801.

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Core analysis from reservoir rock plays an important role in oil and gas exploration as it can provide a large number of rock properties. Some of these rock properties can be extracted by image analysis of microscopic rock images in the visible light range. Such properties include the size, shape, and distribution of pores and grains, or more generally the texture, mineral distribution, and so on. A novel laboratory instrument and method allows for easy and reliable core imaging. This method is applicable even when the core sample is in poor shape. The capabilities of this technique can be verified by core images, image interpretation, and dynamic measurements of rock samples during flooding. A microscopic imager instrument is operated in video acquisition mode and can measure additional properties, such as fluid mobility, by detecting the emergence of injected fluids across the core sample.
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Yosi, Klementin Fairyo. "GAMBAR CADAS PRASEJARAH DI TELUK WONDAMA SEBARAN DAN CERITA RAKYATNYA." Jurnal Penelitian Arkeologi Papua dan Papua Barat 12, no. 2 (January 21, 2021): 97–113. http://dx.doi.org/10.24832/papua.v12i2.233.

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ABSTRAKKeberadaan gambar cadas di Teluk Wondama ditulis oleh Galis tahun 1948. Balai Arkeologi Papua tahun 2016 di Pulau Roon. Hasilnya bersifat eksplorasi, belum terfokus pada tipologi gambar cadas. Tahun 2019 Balai Arkeologi Papua melakukan penelitian Tipologi gambar cadas prasejarah di kawasan Teluk Wondama. Selain mengkaji tipologi gambar cadas, juga mengungkap cerita rakyatnya. Tujuan penulisan adalah mengetahui tipologi gambar cadas, seperti apa sebarannya dan apa saja cerita rakyat terkait gambar cadas. Metode penelitian eksploratif dan deskriptif kualitatif. Tahapan penelitian adalah studi kepustakaan, penelitian lapangan, tahap pengolahan data. Dalam pengolahan data menggunakan juga software plugin Dstretch pada aplikasi imajiJ untuk memperjelas gambar. Hasil penelitian menemukan tujuh situs gambar cadas yaitu situs Suanggini, Ambesibui 1, Ambesibui 2, Ambesibui 3, Sanepa, situs Pulau Nuasa dan situs Inuri Kiari. Motif gambar berupa gambar manusia, kadal, ikan, penyu, lingkaran, penanda arah, segitiga, garis, dan gambar tidak teridentifikasi. Kata kunci : Penelitian, Situs, Gambar, Cadas, Pulau. ABSTRACTThe existence rock images in Wondama Bay was written by Galis in 1948. Papua Archaeological in 2016 on Roon Island. The results exploratory, not focused typology rock images. In 2019 Papua Archaeological conducted a typology study rock images prehistoric in Wondama Bay area. In addition to studying typology of rock images, also reveals folklore. The purpose writing is know typology rock images, what are their distribution and what are the folklore related to rock images. Explorative and descriptive qualitative research methods. The stages of research literature study, field research, data processing stage. In processing data also use dstretch plugin software on imageJ application to clarify image. The results found seven rock image sites, Suanggini site, Ambesibui 1, Ambesibui 2, Ambesibui 3, Sanepa, Nuasa Island site and Inuri Kiari site. Image motifs form images humans, lizards, fish, turtles, circles, direction markers, triangles, lines, and images are not identified. Keywords: Research, Site, Image, Cadas, Island.
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Dissertations / Theses on the topic "Rock image"

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Lindström, Håkan. "Rock property measurements using image processing." Thesis, KTH, Mark- och vattenteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99338.

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Shape and size of rocks are important physical characteristics of aggregates used in engineering and for interpretation of the genesis of naturally occurring sediment. Several image processing programs are available for measure the size and shape of various types of objects. The accuracy and reproducibility of results of a new imaging method and new matlab based 3D imaging program has been studied. 3D results are obtained by coupling two images of particles one of their largest and one of the smallest projected areas. The accuracy of results depends on the focal length used for imaging as well as the positioning of particles in the view field.
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Djouama, Mohamed Cherif. "Assessment of rock cutting and rock fragmentation by blasting using image analysis." Thesis, University of Nottingham, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278384.

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Bedair, Ayman. "Digital image analysis of rock fragmentation from blasting." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40319.

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A novel digital image analysis technique to measure the size of fragments on the surface of a muck-pile is presented in this thesis. The technique takes into consideration the physical characteristics of fragment representation and measurement problems. Using an adaptive smoothing filter prior to edge detection, each fragment on the surface is represented by a group of edge segments outlining its boundaries. These segments are then grouped to form continuous contours.
A multi-layer analysis of the digital image is then formulated where fragments on the surface are grouped into three layers, each of which is categorized by global characteristics and is related to other neighbouring layers by local characteristics. These local relationships between the layers are used to approximate the missing parts of the fragment contour.
An extensive analysis of the sieving process is used in building the relationship between the shape and the size of individual fragments. Using this relation, a new multivariable measure for each fragment is developed. These measures are used in estimating the size distribution of the muck-pile and compared with other existing measurement techniques. This comparison proves the robustness of the technique developed in this thesis.
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Devgan, Ashutosh 1968. "Analysis of rock fragmentation using digital image processing." Thesis, The University of Arizona, 1992. http://hdl.handle.net/10150/278195.

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The success of rock fragmentation due to blasting depends on many variables, such as rock properties, in-situ fracturing, and blast design. Traditionally, the size distribution of fragmented rock particles has been determined through screen sieving. Modern techniques using video images and computer image processing techniques have the potential for analyzing rock fragmentation accurately and efficiently. A procedure has been developed for analyzing rock fragmentation which uses a high-resolution video camera for capturing images in the field, and specialized computer algorithms for processing these images. First of all, computer algorithms have been developed to delineate the individual rock fragments in the images. Secondly, a set of experiments have been conducted in the laboratory, in which the two dimensional information from the images is correlated with sieve results. Based on these experiments, a set of probabilities have been determined for correctly determining the size and volume of rock fragments from two dimensional images. Using these probabilities along with the particle delineation algorithm, the size distribution for the rock fragments is calculated. The computer algorithms can also combine information from many images to take into account sampling and images taken at different scales.
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Sharafisafa, Mansour. "Characterization of quasi-static and dynamic fracture behaviour of rock-like materials using digital image correlation." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/24320.

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Rock masses contain geological features which play key role in failure mechanism. Amongst these features are flaws, layers, joints, faults, etc. In this thesis digital image correlation (DIC) is utilized to study crack development behaviour of rock-like materials containing different geological features. 3D printing specimens having pre-existing flaws, layered and bimrock specimens are studied under quasistatic and dynamic impact loadings. Both unfilled and filled flaws are studied in 3D printed specimens. Based on the static testing, it is observed that the flaws configuration have significant effect on the type of cracks. In particular, tensile or shear movement of the flaws controls the type of newly formed cracks. It is observed that filling improves the strength characteristics of the specimens. Filled specimens develop much larger fracture process zone (FPZ) prior to the peak load. Specimens having their filling fractured exhibit an enhancement of the peak load larger than that of the filled specimens without cracking in the fillings. Similar to quasi-static loading, not only the specimens with filled flaws can carry more load than the corresponding unfilled flaw specimens, but also their cracking pattern is different as compared to the unfilled flaw counterpart. However, it is interesting to note that the dynamic peak loads are not dependent on the flaw inclination angle, while the quasi-static peak loads show obvious flaw inclination angle dependence. Moreover, DIC results reveal that under specific flaws configurations, the filling material undergoes shear cracking. In layered specimens, the 0°-30° orientation angles exhibit dominant strain accumulation inside the layers, at 45°-60° mixed tensile-shear cracking is observed in layers and interfaces, and at the layers of 75°-90° only show tensile splitting crack at an interface governs the behaviour. Under dynamic loading, dominate observation is tensile crack development and failure of all the specimens, except that specimens with layers oriented at 60° and 75° showing shear crack initiation in the pre-peak loading stage followed by the development of tensile cracks. In bimrocks the size of blocks remarkably influences the failure trend. Large blocks exhibit both tensile and shear. Moreover, the development of FPZ is highly dependent upon the blocks sizes and the large blocks exhibit obvious development of FPZ.
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Gamboa, Erwin. "Stress corrosion cracking of rock bolts /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18302.pdf.

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Mawdesley, Clare A. "Predicting rock mass cavability in block caving mines /." St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16404.pdf.

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Dahle, Benjamin P. "Evaluating Shallow-Flow Rock Structures as Scour Countermeasures at Bridges." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2544.pdf.

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Banini, George Agbeko. "An integrated description of rock breakage in comminution machines /." [St. Lucia], 2000. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16293.pdf.

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Sharp, Andy. "The ecology and conservation biology of the yellow-footed rock-wallaby /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16815.pdf.

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Books on the topic "Rock image"

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Doss, Erika Lee. Elvis culture: Fans, faith, & image. Lawrence, Kan: University Press of Kansas, 1999.

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Understanding Elvis: Southern roots vs. star image. New York: Garland, 1998.

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1944-, Conkey Margaret Wright, California Academy of Sciences, Paul L. and Phyllis Wattis Foundation Endowment Symposium (2nd : 1995 : California Academy of Sciences), and Oregon Archaeological Retreat (1st : 1993), eds. Beyond art: Pleistocene image and symbol. San Francisco, Calif: California Academy of Sciences, 1997.

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François-Xavier, Fauvelle-Aymar, and Bon François 1970-, eds. Vols de vaches à Christol Cave: Histoire critique d'une image rupestre d'Afrique du Sud. Paris: Presses de l'université Paris-Sorbonne, 2009.

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Memoirs of a geezer: The autobiography of Jah Wobble : music, mayhem, life. London: Serpent's Tail, 2009.

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Reilly, Edward. The Monkees: A manufactured image : the ultimate reference guide to Monkee memories & memorabilia. Ann Arbor, Mich: Popular Culture Ink., 1987.

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Ray, Schweighardt, ed. Drugs, divorce, and a slipping image: The unauthorized story of the Beatles' "Get back" sessions. Princeton Junction, N.J: The 910, 1994.

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The Crescent on the Temple: The Dome of the Rock as Image of the Ancient Jewish Sanctuary. Boston: Brill, 2012.

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Menz, Joachim. Automatisierte fotografische Haufwerkanalyse. Leipzig: Deutscher Verlag für Grundstoffindustrie, 1987.

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Maggie, McManus, and Chadwick William 1942-, eds. The Monkees: A manufactured image : the ultimate reference guide to Monkee memories & memorabilia. Ann Arbor, Mich: Pierian Press, 1987.

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Book chapters on the topic "Rock image"

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Lepistö, Leena, Iivari Kunttu, and Ari Visa. "Color-Based Classification of Natural Rock Images Using Classifier Combinations." In Image Analysis, 901–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_91.

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Habrat, Magdalena, and Mariusz Młynarczuk. "Granulation-Based Reverse Image Retrieval for Microscopic Rock Images." In Lecture Notes in Computer Science, 74–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50420-5_6.

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Wang, Weixing, and Eva Hakami. "A Segmentation Algorithm for Rock Fracture Detection." In Pattern Recognition and Image Analysis, 580–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_64.

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Paclík, P., S. Verzakov, and R. P. W. Duin. "Improving the Maximum-Likelihood Co-occurrence Classifier: A Study on Classification of Inhomogeneous Rock Images." In Image Analysis, 998–1008. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_101.

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Jiqun, Zhang, Hu Chungjin, Liu Xin, He Dongmei, and Li Hua. "An Algorithm for Rock Pore Image Segmentation." In Lecture Notes in Electrical Engineering, 243–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46578-3_28.

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Urbach, Erik R., Marina Pervukhina, and Leanne Bischof. "Segmentation of Cracks in Shale Rock." In Mathematical Morphology and Its Applications to Image and Signal Processing, 451–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21569-8_39.

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Sun, Jiping, and Bo Su. "Coal−Rock Interface Detection Using Digital Image Analysis Technique." In Electrical, Information Engineering and Mechatronics 2011, 1215–23. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_144.

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Bai, Yunfeng, and Vladimir Berezovsky. "Digital Rock Image Enhancement via a Deep Learning Approach." In Advances in Computer, Communication and Computational Sciences, 533–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4409-5_48.

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Zhang, Huan, Cai Meng, and Zhaoxi Li. "Rock-Ring Accuracy Improvement in Infrared Satellite Image with Subpixel Edge Detection." In Image and Graphics Technologies and Applications, 180–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1702-6_18.

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Pan, S. W., X. Guo, and M. M. Zhang. "Rock Image Segmentation Based on Wavelet Transform and Watershed Algorithm." In Springer Series in Geomechanics and Geoengineering, 316–25. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7560-5_28.

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Conference papers on the topic "Rock image"

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Kumar Shahi, Anup, Anil Kumar, Kumar Hemant Singh, and Ranjith Pathegama Gamage. "Improved Upscaling Methods for Carbonate Rock Image Data." In 56th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2022. http://dx.doi.org/10.56952/arma-2022-2204.

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ABSTRACT Volumetric image data of rocks often need sophisticated image processing steps in any rock physics and petrophysics workflow. While the segmentation is highly dependent on the quality of data, a trade-off between resolution and field of view is inevitable. This work attempts to resolve this using multiple-point statistics that have long been used for generating synthetic images, though mostly applied to sandstone rocks where the heterogeneity is significantly less than that of carbonates. These algorithms work by sequentially populating a grid to emulate the observed image. However, finding the optimum kernel parameters is crucial to capturing the spatial characteristics of the data. Also, when dealing with multiple images, finding a single set of kernel parameters might not be a trivial task. Further these methods work by computing a covariance kernel that scales as the third power with the number of training examples, thus not scaling well with the more data. Therefore, we seek to design a single image-based upscaling method that would help alleviate these difficulties. We test the proposed methodology on carbonate rock sample data which are known for their complexities at various scales. In this study images of 4 samples are considered. An upsample-deblur is developed that consistently works better than the conventional bicubic interpolation based upsampling technique. For this, a low-resolution 2D image sample is extracted from an X-ray microtomography dataset which was then subjected to a Random Forest based upsampling algorithm. It is found that the data from low scale could be improved to form a single super-resolution image. The algorithm produces an image that is always better than the bicubic algorithm. We anticipate this strategy would help design advanced algorithms where the amount of training examples is less. 1. INTRODUCTION Digital Rock Physics workflow has gained significant attention from researchers due to its promising accuracy to characterize rocks and predict desired properties through numerical simulation (Andrä et al., 2013a, 2013b). Digital rock physics is a numerical workflow to compute and simulate various rock properties such as permeability, electrical conductivity, and elastic moduli based on high-resolution representations of the complex pore geometry obtained from imaging (Andrä et al., 2013a, 2013b; Arns et al., 2019; Devarapalli et al., 2017; Mehmani et al., 2020; Wildenschild & Sheppard, 2013).
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Qin, Xing, and Yanlong Zhao. "Rock Image Segmentation Based on Improved Back Propagation Neural Network." In 3rd International Discrete Fracture Network Engineering Conference. ARMA, 2022. http://dx.doi.org/10.56952/arma-dfne-22-0068.

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Abstract Image segmentation is an important basis for extracting the structure characteristics of the rock. In order to solve the problem that the traditional image segmentation method does not segment the rock image accurately, the genetic algorithm is used to optimize the traditional back propagation (abbreviated as BP) neural network image segmentation method. The features of the rock image domain are extracted, and the training samples are further corrected. Using the improved back propagation neural network rock image segmentation method, the rock image is segmented for three aspects: connected domain, local domain and edge domain. The calculation results compared with ImageJ software and traditional BP neural network show that under the condition of small sample size, the improved BP neural network not only can autonomously learn the whole connected structure, local domain structure and edge structure in the rock image, but improve the accuracy and speed of the BP neural network for rock image segmentation.
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Weixing Wang. "Rock Fracture Network Image Analysis." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713915.

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Anderson, Timothy. "Reconstruction and Synthesis of Source Rock Images at the Pore Scale." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208632-stu.

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Abstract Image-based characterization of rock fabric is critical for understanding recovery mechanisms in shale formations due to the significant multiscale nature of shale source rocks. Nanoscale imaging is particularly important for characterizing pore-scale structure of shales. Nanoimaging techniques, however, have a tradeoff between high-resolution/high-contrast sample-destructive imaging modalities and low-contrast/low-resolution sample-preserving modalities. Furthermore, acquisition of nanoscale images is often time-consuming, expensive, and requires signficant levels of expertise, resulting in small image datasets that do not allow for accurate quantification of petrophysical or morphological properties. In this work, we introduce methods for overcoming these challenges in image-based characterization of the fabric of shale source rocks using deep learning models. We present a multimodal/multiscale imaging and characterization workflow for enhancing non-destructive microscopy images of shale. We develop training methods for predicting 3D image volumes from 2D training data and simulate flow through the predicted shale volumes. We then present a novel method for synthesizing porous media images using generative flow models. We apply this method to several datasets, including grayscale and multimodal 3D image volume generation from 2D training images. Results from this work show that the proposed image reconstruction and generation approaches produce realistic pore-scale 3D volumes of shale source rocks even when only 2D image data is available. The models proposed here enable new capabilities for non-destructive imaging of source rocks and we hope will improve our ability to characterize pore-scale properties and phenomena in shales using image data.
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Gonzalez, Andres, Zoya Heidari, and Olivier Lopez. "Data-Driven Algorithms for Image-Based Rock Classification and Formation Evaluation in Formations With Rapid Spatial Variation in Rock Fabric." In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0018.

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Supervised learning algorithms can be employed for automation of time-intensive tasks, such as image-based rock classification. However, labeled data is not always available. Alternatively, unsupervised learning algorithms, which do not require labeled data, can be employed. Using either of these methods depends on the evaluated formations and the available training/input data sets. Therefore, further investigation is needed to compare the performance of both approaches. The objectives of this paper include, (a) to train two supervised learning models for image-based rock classification employing image-based features from computerized tomography (CT) scan images and core photos (b) to conduct image-based rock classification using the trained model, (c) to compare the results obtained using supervised learning models against an unsupervised learning-based workflow for rock classification, and (d) to derive class-based petrophysical models for improved estimation of petrophysical properties. First, we removed non-formation visual elements from the core image data. Then, we computed image-based features such as grey scale/color and textural features from core image data and conducted feature selection. Then, we employed the extracted features for model training. Finally, we used the trained model to conduct rock classification and compared the obtained rock classes against the results obtained from an unsupervised image-based rock classification workflow. This workflow uses image-based rock fabric features coupled with a physics-based cost function for rock classes optimization. We applied the workflow to one well intersecting three formations with rapid spatial variation in rock fabric. We used 60% of the data to train a random forest and a support vector machines classifier using a 5-fold crossvalidation approach. The remaining 40% of the data was used to test the accuracy of the supervised models. We stablished a base case of unsupervised learning rock classification and four different cases of supervised learning rock classification. The highest accuracy obtained for supervised rock classification was 97.4 %. The accuracy obtained in the unsupervised learning rock classification approach was 82.7% when compared against expert-derived lithofacies. Class-based permeability estimates decreased the mean relative error by 34% and 35% when compared with formation-based permeability estimates, for the supervised and unsupervised approaches, respectively. The highest accuracies for the supervised and unsupervised model were obtained when integrating features from CT-scan images and core photos, highlighting the importance of feature selection for machine learning workflows. Comparison of the two approaches for rock classification showed higher accuracy in supervised learning rock classification. However, the unsupervised approach provided reasonable accuracy as well as a more general and faster approach for rock classification and enhanced formation evaluation.
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Shebl, Hesham Talaat, Mohamed Ali Al Tamimi, Douglas Alexander Boyd, and Hani Abdulla Nehaid. "Automation of Carbonate Rock Thin Section Description Using Cognitive Image Recognition." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208149-ms.

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Abstract Simulation Engineers and Geomodelers rely on reservoir rock geological descriptions to help identify baffles, barriers and pathways to fluid flow critical to accurate reservoir performance predictions. Part of the reservoir modelling process involves Petrographers laboriously describing rock thin sections to interpret the depositional environment and diagenetic processes controlling rock quality, which along with pressure differences, controls fluid movement and influences ultimate oil recovery. Supervised Machine Learning and a rock fabric labelled data set was used to train a neural net to recognize Modified Durham classification reservoir rock thin section images and their individual components (fossils and pore types) plus predict rock quality. The image recognition program's accuracy was tested on an unseen thin section image database.
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Carrión-Ruiz, Berta, Silvia Blanco-Pons, and Jose Luis Lerma. "DIGITAL IMAGE ANALYSIS OF THE VISIBLE REGION THROUGH SIMULATION OF ROCK ART PAINTINGS." In ARQUEOLÓGICA 2.0 - 8th International Congress on Archaeology, Computer Graphics, Cultural Heritage and Innovation. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/arqueologica8.2016.3560.

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Non-destructive rock art recording techniques are getting special attention in the last years, opening new research lines in order to improve the level of documentation and understanding of our rich legacy. This paper applies the principal component analysis (PCA) technique in images that include wavelengths between 400-700 nm (visible range). Our approach is focused on determining the difference provided by the image processing of the visible region through four spectral images versus an image that encompasses the entire visible spectrum. The images were taken by means of optical filters that take specific wavelengths and exclude parts of the spectrum. Simulation of rock art is prepared in laboratory. For this purpose, three different pigments were made simulating the material composition of rock art paintings. The advantages of studying the visible spectrum in separate images are analysed. In addition, PCA is applied to each of the images to reduce redundant data. Finally, PCA is applied to the image that contains the entire visible spectrum and is compared with previous results. Through the results of the four visible spectral images one can begin to draw conclusions about constituent painting materials without using decorrelation techniques.
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Zhang, Zhen, Yiteng Li, Marwah AlSinan, Xupeng He, Hyung Kwak, and Hussein Hoteit. "Multiscale Carbonate Rock Reconstruction Using a Hybrid WGAN-GP and Super-Resolution." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210461-ms.

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Abstract The X-ray micro-Computed Tomography (μ-CT) is the primary tool for digital rock imaging, which provides the foundation for numerically studying petrophysical properties of reservoir rocks at the pore scale. However, the finite resolution of μ-CT imaging cannot capture the micro-porosity at the sub-micrometer scale in carbonate rocks. The tradeoff between the resolution and field of view (FOV) is a persisting challenge in the industry. The machine-learning-based single-image super-resolution techniques has rapidly developed in the past few years. It is becoming a promising approach to "super-resolve" low-resolution carbonate rock images. In this study, we present a fast super-resolution generative adversarial network to enhance the image resolution of carbonate rocks. A pre-trained VGG network is implemented to extract important high-level features, from which the perceptual similarity is evaluated between the generated and ground-truth images. The novelty of this study is two-fold. First, the generator is significantly simplified with a fast super-resolution convolutional neural network. On the other hand, the spatial and channel squeeze-and excitation block is applied to recalibrate nonlinear feature mapping so that the quality of super-resolved images is promising even with much fewer residual blocks. To quantify the quality of the super-resolution images, we compare difference maps between the generated and ground-truth images. Numerical results indicate that the proposed network shows excellent potential in enhancing the resolution of heterogeneous carbonate rocks. In particular, the pixel errors are minor, and the super-resolution images exhibit clear and sharp edges and dissolved mineral texture. This study provides a novel machine-learning-based method using a simple generative adversarial network with squeeze and excitation blocks to super-resolve μ-CT images of carbonate rocks.
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Ong, Yong Zheng, Nan You, Yunyue Elita Li, and Haizhao Yang. "Digital rock image inpanting using GANs." In SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/segam2020-3427515.1.

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Wang, W., Jia Zongpu, and Liwan Chen. "Rock fracture image acquisition and analysis." In 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, edited by Li Yang, Yaolong Chen, Ernst-Bernhard Kley, and Rongbin Li. SPIE, 2007. http://dx.doi.org/10.1117/12.783561.

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Reports on the topic "Rock image"

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Hofmann, Peter, Robert Marschallinger, Michael Unterwurzacher, and Fritz Zobl. Designation of marble provenance: State-of-the-art rock fabric characterization in thin sections by object based image analysis. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0284.

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Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.

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Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock and aggregate stockpiles.
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Gantzer, Clark J., Shmuel Assouline, and Stephen H. Anderson. Synchrotron CMT-measured soil physical properties influenced by soil compaction. United States Department of Agriculture, February 2006. http://dx.doi.org/10.32747/2006.7587242.bard.

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Methods to quantify soil conditions of pore connectivity, tortuosity, and pore size as altered by compaction were done. Air-dry soil cores were scanned at the GeoSoilEnviroCARS sector at the Advanced Photon Source for x-ray computed microtomography of the Argonne facility. Data was collected on the APS bending magnet Sector 13. Soil sample cores 5- by 5-mm were studied. Skeletonization algorithms in the 3DMA-Rock software of Lindquist et al. were used to extract pore structure. We have numerically investigated the spatial distribution for 6 geometrical characteristics of the pore structure of repacked Hamra soil from three-dimensional synchrotron computed microtomography (CMT) computed tomographic images. We analyzed images representing cores volumes 58.3 mm³ having average porosities of 0.44, 0.35, and 0.33. Cores were packed with < 2mm and < 0.5mm sieved soil. The core samples were imaged at 9.61-mm resolution. Spatial distributions for pore path length and coordination number, pore throat size and nodal pore volume obtained. The spatial distributions were computed using a three-dimensional medial axis analysis of the void space in the image. We used a newly developed aggressive throat computation to find throat and pore partitioning for needed for higher porosity media such as soil. Results show that the coordination number distribution measured from the medial axis were reasonably fit by an exponential relation P(C)=10⁻C/C0. Data for the characteristic area, were also reasonably well fit by the relation P(A)=10⁻ᴬ/ᴬ0. Results indicates that compression preferentially affects the largest pores, reducing them in size. When compaction reduced porosity from 44% to 33%, the average pore volume reduced by 30%, and the average pore-throat area reduced by 26%. Compaction increased the shortest paths interface tortuosity by about 2%. Soil structure alterations induced by compaction using quantitative morphology show that the resolution is sufficient to discriminate soil cores. This study shows that analysis of CMT can provide information to assist in assessment of soil management to ameliorate soil compaction.
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Ansari, S. M., E. M. Schetselaar, and J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.

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Unconstrained magnetotelluric inversion commonly produces insufficient inherent resolution to image ore-system fluid pathways that were structurally thinned during post-emplacement tectonic activity. To improve the resolution in these complex environments, we synthesized the 3-D magnetotelluric (MT) response for geologically realistic models using a finite-element-based forward-modelling tool with unstructured meshes and applied it to the Lalor volcanogenic massive-sulfide deposit in the Snow Lake mining camp, Manitoba. This new tool is based on mapping interpolated or simulated resistivity values from wireline logs onto unstructured tetrahedral meshes to reflect, with the help of 3-D models obtained from lithostratigraphic and lithofacies drillhole logs, the complexity of the host-rock geological structure. The resulting stochastic model provides a more realistic representation of the heterogeneous spatial distribution of the electric resistivity values around the massive, stringer, and disseminated sulfide ore zones. Both models were combined into one seamless tetrahedral mesh of the resistivity field. To capture the complex resistivity distribution in the geophysical forward model, a finite-element code was developed. Comparative analyses of the forward models with MT data acquired at the Earth's surface show a reasonable agreement that explains the regional variations associated with the host rock geological structure and detects the local anomalies associated with the MT response of the ore zones.
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Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques and larger-scale transport properties. This study uses pre-existing medical CT-scan datasets of reference material, namely glass beads and conventional reservoir rocks (Berea sandstone, Boise sandstone, Indiana limestone) to extract the 3D geometry of connected pores using an open-source software (Spam). Pore networks from rock samples were generated from dry and then saturated samples. Binarized datasets were produced for these materials (generated by a thresholding technique) to obtain pore size distribution and tortuosity, as well as preferential paths for fluid flow. Average porosities were also calculated for comparison with those obtained by conventional commercial laboratory techniques. The results obtained show that this approach works well for medium and coarse-grained materials that do not contain a large percentage of fine particles. However, this approach does not allow representative networks to be obtained for fine-grained rocks, due to the fact that small pores (or pore throats) cannot be taken into account in the datasets obtained from the medical CT-Scan. A next step, using datasets produced from a micro- CT scan, is planned in order to be able to generate representative networks in this type of material as well.
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Thapa, Bhaskar Bahadur. Analysis of in-situ rock joint strength using digital borehole scanner images. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10107663.

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Wu, Jin Chu, Alvin F. Martin, and Raghu Kacker. Hypothesis test of fingerprint-image matching algorithms in operational ROC analysis. Gaithersburg, MD: National Institute of Standards and Technology, 2009. http://dx.doi.org/10.6028/nist.ir.7586.

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Chriscoe, Mackenzie, Rowan Lockwood, Justin Tweet, and Vincent Santucci. Colonial National Historical Park: Paleontological resource inventory (public version). National Park Service, February 2022. http://dx.doi.org/10.36967/nrr-2291851.

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Colonial National Historical Park (COLO) in eastern Virginia was established for its historical significance, but significant paleontological resources are also found within its boundaries. The bluffs around Yorktown are composed of sedimentary rocks and deposits of the Yorktown Formation, a marine unit deposited approximately 4.9 to 2.8 million years ago. When the Yorktown Formation was being deposited, the shallow seas were populated by many species of invertebrates, vertebrates, and micro-organisms which have left body fossils and trace fossils behind. Corals, bryozoans, bivalves, gastropods, scaphopods, worms, crabs, ostracodes, echinoids, sharks, bony fishes, whales, and others were abundant. People have long known about the fossils of the Yorktown area. Beginning in the British colonial era, fossiliferous deposits were used to make lime and construct roads, while more consolidated intervals furnished building stone. Large shells were used as plates and dippers. Collection of specimens for study began in the late 17th century, before they were even recognized as fossils. The oldest image of a fossil from North America is of a typical Yorktown Formation shell now known as Chesapecten jeffersonius, probably collected from the Yorktown area and very likely from within what is now COLO. Fossil shells were observed by participants of the 1781 siege of Yorktown, and the landmark known as “Cornwallis Cave” is carved into rock made of shell fragments. Scientific description of Yorktown Formation fossils began in the early 19th century. At least 25 fossil species have been named from specimens known to have been discovered within COLO boundaries, and at least another 96 have been named from specimens potentially discovered within COLO, but with insufficient locality information to be certain. At least a dozen external repositories and probably many more have fossils collected from lands now within COLO, but again limited locality information makes it difficult to be sure. This paleontological resource inventory is the first of its kind for Colonial National Historical Park (COLO). Although COLO fossils have been studied as part of the Northeast Coastal Barrier Network (NCBN; Tweet et al. 2014) and, to a lesser extent, as part of a thematic inventory of caves (Santucci et al. 2001), the park had not received a comprehensive paleontological inventory before this report. This inventory allows for a deeper understanding of the park’s paleontological resources and compiles information from historical papers as well as recently completed field work. In summer 2020, researchers went into the field and collected eight bulk samples from three different localities within COLO. These samples will be added to COLO’s museum collections, making their overall collection more robust. In the future, these samples may be used for educational purposes, both for the general public and for employees of the park.
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Durling, P. W. Seismic reflection interpretation of the Carboniferous Cumberland Basin, Northern Nova Scotia. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331223.

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An interpretation of approximately 1700 km of seismic data was completed in 1996. The seismic analysis, together with well information and geological map data, were used to map thirteen seismic horizons in the Cumberland Basin. Ten of the horizons were mapped only in limited areas, whereas three horizons could be mapped regionally. These are: BW (base of the Windsor Group), BP (base of the Boss Point Formation), and PG (base of the Pictou Group). The BW horizon is the deepest regional horizon mapped. The horizon generally dips southerly toward the Cobequid Highlands. It is affected by faults adjacent to the Scotsburn Anticline and the Hastings Uplift; the horizon was not recognized over part of the uplift. On the seismic reflection data, the horizon varies between 500 ms and 3200 ms two-way travel time (approximately 800-7600 metres) and rocks corresponding to this horizon do not outcrop in the basin. The BP and PG horizons can be traced to outcrop on the flanks of the major anticlines. Time structure maps of these horizons mimic the distribution of synclines mapped from outcrop geology. The BP horizon is affected by more faults and is more tightly folded than the PG horizon south of a major fault (Beckwith Fault). North of the Beckwith Fault, both horizons are essentially flat and not deformed. Several geological relationships were documented during this study. A thick (up to 1600 m) clastic unit was recognized in the central portion of the southern margin of the Cumberland Basin. It is interpreted as Windsor Group equivalent. Seismic reflections from within the Falls and Millsville conglomerates were recognized and suggest that these rocks correlate with the Windsor Group. Seismic profiles that cross the southern margin of the Cumberland Basin image parts of the asement complex to the south of the basin (Cobequid Highlands) and show reflection patterns consistent with mountain fronts. The seismic data image the folded and faulted Cobequid Highlands basement complex, which is interpreted as a thrusted structural wedge.
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Pe-Piper, G., D. J W Piper, J. Nagle, and P. Opra. Petrography of bedrock and ice-rafted granules: Flemish Cap, offshore Newfoundland and Labrador. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331224.

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This Open File report provides petrographic information from a scanning electron microscope study of granules and small pebbles in four selected trawl samples from Flemish Cap. The mineral composition of the granules was determined by energy dispersive spectroscopy (EDS) and textures are shown in backscattered electron images (BSE). It complements Open File 8359 on the heavy mineral assemblage on Flemish Cap. Granules on the central shoals appear to be derived from outcropping Avalonian basement; those to the east and west are predominantly ice-rafted in origin. These data improve our understanding of the source of the voluminous sands on Flemish Cap and the characteristics of the Avalonian basement rocks on southern Flemish Cap.
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