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, Integrating information from seismic data into sedimentary-processes modeling

P. L. Bouteiller, , 2016.

, Object retrieval in seismic data, Journées des Doctorants de l'Ecole Doctorale Géosciences

P. L. Bouteiller, J. Charléty, F. Delprat-jannaud, and C. Gorini, , 2016.

. Smai-sigma-conference, Signal, Image, Géométrie, Modélisation, Approximation) 2016. Marseille, France. Image processing for Mass Transport Deposits identification in seismic data

P. L. Bouteiller, , 2016.

, Image processing for Mass Transport Deposit retrieval in seismic data

P. L. Bouteiller, F. Delprat-jannaud, J. Charléty, C. Gorini, and D. Granjeon, , 2017.

, A new conceptual methodology for interpretation of mass transport processes from seismic data, SIAM Conference on Mathematical and Computational Issues in the Geosciences, 2017.

P. L. Bouteiller, S. Lafuerza, J. Charléty, A. T. Reis, D. Granjeon et al., Peer-reviewed article submitted February 28; revisions received June 25; revised submission sent, Journal of Marine and Petroleum Geology, 2018.

, Understanding mass transport processes in sedimentary basins from their signatures on seismic data: a knowledge-based approach

P. L. Bouteiller, , 2018.

, Procédé pour la détection d'objets géologiques dans une image

P. L. Bouteiller and J. Charléty, Patent application. Referenced as Le Bouteiller & Charléty, 2018.

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, Mixing unsupervised and knowledge-based analysis for heterogeneous object delineation in seismic data

P. L. Bouteiller, J. Charléty, F. Delprat-jannaud, D. Granjeon, C. Gorini-;-le et al., Oral presentation. EAGE Annual Conference and Exhibition, 2018.

, Semi-supervised multi-facies object retrieval in seismic data

P. L. Bouteiller and J. Charléty, Mathematical Geosciences. List of Figures 1.1 Main features of a passive continental margin. Tilted fault blocks, dikes, salt (or evaporites), reef and lagoon deposits, are signatures of the passive margin formation and past evolution. Shallow marine sediments, slump blocks, turbidites and deep marine sediments are continuing deposited sediments in the basin, Peer-reviewed article submitted October 1, p.12, 2015.

, Among them: slope failures, impact of debris flows on infrastructure, dissociation of hydrates, shallow-gas pockets, overpressure, fluid escape features (gas chimneys, mud volcanoes), diapirism, seismicity, and highly destructive tsunamis. From Vanneste et al. (2014)

, Schematic description of the three domains in an MTD. This scheme does not cover all kinds of MTDs

, Subaerial mass-transport in the Austrian Alps; extensional and compressional features are seen, as well as lateral shear. These features are also seen in submarine mass transports. From Posamentier & Martinsen (2011). (b) Map (A), seismic profile (B) and interpreted seismic profile (C): MTD in brown, Mass transport examples. (a), 2016.

. Sawyer, Mudflow features characteristic of Flow factor. A, C, E: lateral view of source area, p.19, 2012.

, Schematic cross-sections illustrating gravity-driven deformational processes, including those that form mass-transport deposits. Modified from Posamentier & Martinsen (2011)

. Mourgues, for sand-like material. ? is the slope angle of the free surface; ? * b is the overpressure ratio of the basal detachment surface, with value zero if no overpressure and 1 if overpressure equals hydrostratic pressure. The dashed line represents the solution for a compressive state of stress. The black bold line represents the solution for a gravity-driven extensional state of stress (the one studied in this PhD project): this line is the limit where the Flow factor (inverse of the Factor of Safety FS here) is F f = 1. The grey area shows domains of slope instability triggered by gravity only, From Mourgues et al, p.26, 2014.

. Elverhoi, In their article, clay portion ranges from 5 to 25%, but the authors precise that these critical values may change depending on the kind of clay and the scale of the experiment / field data, 2010.

M. , Modified from the Schlumberger Oilfield Glossary. (b) Display of seismic data (not depth-converted), positive amplitudes shaded for enhanced inter-trace continuity. Each trace results from the operation of stacking signals from all receivers. TWT: two-way traveltime. Modified from the US EPA web archive, Marine seismic acquisition: example for 2D data. (a) The acquisition ship drags a system of sources (air guns) and receivers (hydrophones on the streamer), p.33, 1977.

, Seismic facies A, interpreted as shelf-margin deltas/shoreface deposits; (B) seismic facies B, interpreted as slope clinoforms with tangential (oblique) geometry; (C) seismic facies C, interpreted as slope clinoforms with sigmoidal geometry; (D) seismic facies D, interpreted as turbidites; (E) seismic facies E, interpreted as mass-transport deposits; (F) seismic facies F, Seismic stratigraphic features on an example 2D seismic section, as interpreted by Berton & Vesely (2016): '(A), 2016.

. Reis, Amapá Lower Complex (ALC), the deepest mass transport complex of Amapá, is mapped in blue; Amapá Upper Complex (AUC), more recent, is mapped in orange, The Amapá and Pará-Maranhão Megaslides (ALC-AUC / PMM) were studied by, p.37, 1981.

, The base of the carbonate platform dates back approximately to the early Eocene (60 to 56 Ma). The 7-8 Ma horizon corresponds to the end of the top of the carbonate platform, and base of siliciclastic sedimentation. The dark lines connecting the slope and basin regions within the Limoeiro Formation are examples of an extensional-compressional tectonic system, 13 Interpretation of a 2D seismic profile accross the Amazon basin: horizons corresponding to the main evolution stages of the basin, p.39, 2018.

. Silva, Synthetic schema of the main types of transport mechanisms in the Amazon River Mouth basin, according to the literature, p.42, 1981.

, Seismic section extracted from the 3D cube. Most recent, upslope sedimentary series are shown, together with several horizons. Progradation is the deposition of sediments gradually further in the basinward direction

. .. , 44 1.16 2D seismic profile compared with a 3D section extracted from the 3D cube. Most recent, downslope sedimentary series are shown, together with horizon G (2.4 Ma) from Gorini et al. (2014) (yellow line). The top-left image displays the context of the 3D section extract shown on the right; this greencontoured extract corresponds spatially to the green-contoured region of the 2D section, aggradation is the deposition of sediments that gradually builds upwards. No MTD is visible on this section

, The algorithm clearly yields a distinction between the geometry and texture parts of both natural and seismic images. However, the geometry component of the seismic image does not reveal the expected geological structure. Images obtained by applying algorithm and code from Le Guen (2014)

, Axes of the scatter plot correspond to GLCM contrast (abscissa) for a reference vector drawn in light blue, and GLCM energy (ordinate) for a reference vector drawn in orange

, (a) The level-set formulation of the delineation problem states that the contour line is the zero-level set of a function ?. ? is modified iteratively to fit a contour in the image while respecting a regularization constraint (e.g., on the total curvature or length of the contour). (b) Example of application: detection of freely swimming fish in a SONAR image; initial image, and zero-level-set contour after 4, 10 and 16 iterations, p.64, 2001.

. Al.-;-from and . Shafiq, 66 2.4 A typical seismic facies classification using the interpreter trained probabilistic neural net, where multiple seismic facies classes have been identified. The seismic classification scheme on the right consists of high amplitude (HA), moderate amplitude (MA), low amplitude (LA), continuous (C) and semi-continuous (SC) seismic facies. Training patches are contoured in green lines, Examples of geometrical segmentation methods for seismic image partitioning: (a) salt body delineation via level-set and manual constraint based on a specifically-designed seismic attribute, from Haukås et, 2002.

, Comparison between a k-means clustering map (left) and a SOM clustering map (right) of a Frio Channel gas play (South Texas), 2003.

. Roy, 76 2.9 Illustration of the method presented in this section. (a) One seismic section and the available prior probabilities on MTD occurrence. (b) The same section where every pixel is colored according to its GTM cluster label (a number between 1 and 49), and the posterior probabilities computed from our method. A 2D colormap is used for GTM labels to account for the 2D topographic "ranking" given by GTM. (c) Retrieval of probabilities for several sections, Generative Topographic Mapping: principle and characteristic of the Magnification Factors, vol.118, 1998.
URL : https://hal.archives-ouvertes.fr/in2p3-00724073

, 119 2.14 Illustration of the orientation filtering results for the 23 largest objects of the processed full-stack dataset. Vectors are normed and represent the orientation of the objects. Blue vectors correspond to MTDs, red vectors correspond to non-MTD objects, and green vectors correspond to unsure or unexpected objects. The light blue contour is a sketch of the cone used for filtering out most non-MTD objects. Note that the 3D view does not render the whole direction of vectors or cone, Posterior probabilities on one inline of the seismic cube used in our study, for the four datasets

, 125 2.17 An example of application of the Chan-Vese algorithm (Chan & Vese (2001)) on one 2D attribute image of the seismic volume: initial (left) and final (right) state of the contour (red line). Parameters to be tuned include the initial position of the contour (here chosen as split into multiple contours for accelerating the convergence, Crossplots comparing the volume, aspect ratio, and divergence to uniform (DTU in this report), of the largest connected components retrieved by our method. (a) full-stack dataset, (b) near-offset dataset, (c) mid-offset dataset, (d) far-offset dataset, p.126, 2007.

. Bergmann, represented by 7 textural attribute images. 20, resp. 25, class centers were picked manually on the reference image in cases (b), resp. (c), An example of application of the (supervised) iterative multiplicative filtering for data labeling by, 2017.

, Example of use of the International Stratigraphic Chart to retrieve Geologic Timescale Elements: the query was the word 'Cretaceous' to be 'within the label' of the elements searched. Results include all elements of the ontology having the word Cretaceous in their label. From Linked Data API (2017): link to the webpage, last accessed Sept, vol.10, 2018.

. .. , 2 Proposed architecture for an ontology for interpretation of major surfaces in a seismic block, p.136, 2009.

, Studied horizon parts shown on a seismic image; two different parts of the image give different information shown by the two graphs below the image, and resulting in interpretation of the horizons' relative ages (right). (b) Four descriptors depicted for all considered parts of horizons: two different quantitative descriptor (left and right), two different categorical descriptors (middle); the fifth rectangle on the right is the ontology proposal, Example of horizon interpretation in the ontology proposed by Verney, vol.137, 2009.

, Cluster labels (numbers from 1 to 49) are indicated. High values of MF indicate a stretched region of the manifold, corresponding to 'natural' boundaries between groups of points in the data space, Magnification factors (MF) map of the GTM manifold for the full-stack dataset, p.139

, Example for the deformed facies: seismic amplitude image (left), selection of clusters (middle), all selected clusters grouped into one facies (right). (b) Interpreted facies groups used in our study, drawn on the 2D grid of 49 cluster centers. The deformed facies, for example, is defined by clusters 4, Interpreting facies groups from the GTM-defined clusters. (a), vol.5, p.140

, Proposed workflow joining our two contributions into a global methodology for mass transport process interpretation from seismic data, p.201

, Result of the object postprocessing on one seismic section as presented on Figure 2.16. (b) Result of the object post-processing, where the morphological closing uses a larger structuring element -including all pixels. (c) Result of the object postprocessing, where the morphological closing uses a larger structuring element -selecting only pixels with positive probability. Black arrows indicate pixels added compared to (a), Objects recovered as one or more pieces. (a)

, Integrating facies to create facies maps and lateral proportion curves. Five seismic sections are shown with the corresponding MTD sections, colored according to cluster numbers. The red-level facies map shows the vertical proportion of one facies (as defined in section 3.2 and Figure 3.5) in each column of the MTD (integration over direction dir. 3). The curve shows the lateral proportion of the facies, in each dip-oriented section of the MTD (integration over directions dir. 2 and dir. 3)

, 13, see also Figure 3.5) on one seismic section. Horizon H1 is a hypothetical unconformity between seismic unit (I) (ancient slope) and unit, vol.5

. .. Mtds, 209 4.5 Determination of a stratigraphic pattern to use for calculating 'vertical' facies proportion curves along the geological time. From the study of a seismic cube (left) and its corresponding clustered cube (middle), we hypothetize the pattern to use (right). Here, for a first approach

, Approximate positions of MTDs in time are given. The 'MTD-like' facies kind is the grouping of clusters having maximum posterior probabilities in the cluster probability assignment presented in chapter 2, p.210

. Holcomb, 218 5.2 One inline of the seismic dataset with suggested relationship between the downslope region, An application of the Ilastik software to the extraction of neuronal cell bodies and nuclei from electron microscopy image stacks, 2016.

, Points on a regular grid in the low-dimensional latent space (left) are mapped, using a parameterized, non-linear mapping y(x; W), to corresponding centers of Gaussians (right), 1998.

, On the right, the same section is shown, with a mask corresponding to the ground-truth associated to this image (see also Figure 2.9a, p. 81)

, Mapping of the Cartesian coordinate system x i of the L-dimensional latent space onto the curvilinear coordinate system ? i in the L-dimensional manifold embedded in the data space. For L = 2, dA is an infinitesimal area in the latent space, and dA is the corresponding region on the manifold, 1997.

]. .. , Asymmetric error function for one pixel t: the error is smaller for false positives (i.e. where X t < Y t ) than for false negatives (i.e. where X t > Y t ). X: original prior image; Y : modeled image, p.229

, One reference image (a) and seven synthetic images ((b) to (h)), used for testing several metrics

C. , Table Supplementary 2 of the article of section 3.3

, 235 List of Tables B.1 Several metric results for the seven synthetic images of Figure B.2: metric values between one image ((b) to (h) of Figure B.2) and the Reference image (Figure B.2a). 1?F uzzyS: Fuzzy Sensitivity (1 -index value), EM : Error Metric

?. , Fuzzy Mutual Information (1 -index value). 1 ? HaarP SI: Haar wavelet-based perceptual similarity index (1 -index value). M M : Mass Metric

, Mass Metric (M M ) results for the PCA dimension reduction method, for Training Images (TI) and Validation Images (VI), with ? = 1, p.232

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