Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
  • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
  • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
  • G01V1/32—Transforming one recording into another or one representation into another
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/20—Image preprocessing
  • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns

Definitions

• the present invention relates to a method for the hierarchical determination of coherent events in a seismic image.
• amplitude data or seismic attributes in the pre-stack and post-stack domains, the stack being a central operation of the Seismic processing that compresses seismic data (reducing the number of data) and acts as a powerful noise filter. It also applies to medical imaging data, sonar, non-destructive testing of materials, etc.
• Mathematical Morphology developed in the same way as Geostatistics by Professor Georges Matheron, is based on set-theoretic and topological notions. Its principle is to study the morphological characteristics (shape, size, orientation, ...) of the objects in an image. Mathematical Morphology provides the language and nonlinear tools appropriate for the recognition and processing of shapes in an image regardless of its dimension (1D, 2D, 3D ... nD).
• Mathematical Morphology provides tools for hierarchical segmentation of images. Indeed, it allows the segmentation of images into several regions according to one or more criteria such as, for example, the amplitude, the contrast, the amplitude gradient ... The boundaries between these regions defining segments that are not necessarily rectilinear which generally represent energetic and continuous forms of the image.
• a coherent event will be called a "coherent event" in a 3D image according to criteria of continuity and energy.
• a coherent event will correspond to a line that is not necessarily straight.
• Another possible application relates to the determination of coherent events on different seismic cubes of the same geographical area, these cubes being generated with different processing parameters.
• the invention proposes to provide a tool for assisting the determination of coherent events in a seismic image by associating with each pixel of the image a pair of EII indexes "Event Importance Index", ECI “Event Confidence Index” calculated by applying, on “overlapping windows", a hierarchical segmentation.
• overlapping window it is meant that instead of realizing a segmentation over the entire image as is commonly done, successive segmentations are performed on parts of the image defined by a "moving window” that moves on the image in the manner of a scanner.
• the computation of the pair of indices EII and ECI solves the problem of the real-time determination of the coherent events of a seismic image.
• the determination of the coherent events of a seismic image is done by means of the method according to the invention which comprises the following operating phases:
• a phase of choice of a segmentation criterion according to a variable represented in the form of a seismic image such as amplitude, contrast, gradient, or an area of the image to be qualified, at least one sliding window on said zone and its characteristics, a hierarchical segmentation phase, for positions of the overlapping sliding window, comprising a segmentation of said zone into n regions and for each pixel identified at least once by a segmentation boundary: saving the number of times the pixel is identified by a segmentation boundary for each sliding window to which it belongs and the calculation of the ratio of this number by the maximum theoretical number of times that this same pixel could be located, this ratio corresponding to the index ECI; o the allocation for each segmentation boundary of a hierarchical level based on a calculation according to a specific criterion such as the surface of the boundary, so as to obtain the index EII;
• the variable can be a seismic attribute such as amplitude, reflectivity, impedance or any other attribute calculated on a seismic cube.
• This thresholding may depend in particular on the business application pursued.
• the characteristics of the sliding window may include its dimensions, the recovery rate of a position i of the window with respect to a position ir, the recovery ratio being defined by 1 / r, where r is the modulus of the displacement vector in a main directions of the multiple seismic image of a grid step (definition an image is defined on a grid with as parameter the grid step in each of the main directions of the image (usually Euclidean axes)) in this direction.
• the boundaries of the segmentation for each position of the sliding window define the pixels identified by the segmentation process and for which the indices ElI and ECI can be computed, the low EII and high ECI pixels defining the most coherent events of the segmentation process. 'picture.
• the segmentation phase can be carried out:
• a previously filtered seismic image that is to say an image transformed by a filter, for example a filter making it possible to improve the continuity of the image and to eliminate high frequency random noise;
• the recovery of the positions of the sliding window can be defined by a recovery rate parameter expressed for example in pixels.
• This recovery rate parameter can be decreased to lighten the calculation times.
• This approach in accordance with the method according to the invention, may be implemented in the context of any operational context involving a seismic cube or seismic cubes of the same geographical area.
• FIG. 1 is a perspective representation of a seismic cube and a horizon to be qualified
• FIGS. 2a and 2b are vertical sections of FIG. 1 representing respectively the horizon to be qualified and the horizon to be qualified surrounded by a guide zone;
• Figure 4 is a representation of the displacement of the window of Figure 3;
• Figures 5a, 5b and 5c are representations of a sliding window segmented respectively into two, three and thirty regions;
• Figure 6 is a representation of the displacement of the window of Figure 3 showing the boundaries according to their hierarchy
• Figures 7a, 7b and 7c are representations of qualification attribute cards.
• FIGS. 8a and 8b are representations on vertical sections of anomalies, respectively deviations from a local extremum and a phase jump of the horizon, as evidenced by the qualification attribute maps of FIGS. 7a, 7b and 7c.
• the first example is illustrated in Figures 1 to 8b.
• This example concerns the quality control of a point of "horizons" (coherent events corresponding to geological interfaces) and in particular of a pointed horizon 1 on a seismic cube 2 ( Figures 1 and 2a).
• a horizon is very wide in space and represents a geological interface of sedimentation between geological layers, it generally results from an interpretation by a geophysicist or a geologist.
• the determination of the indices EII and ECI contributes to defining the parts of horizons deemed to be anomalous which should be examined in order to guarantee a geological coherence of the pointed horizons.
• variable or seismic attribute used is the amplitude of the cube.
• the first steps of the process include the choice:
• the criterion of hierarchical segmentation chosen is the amplitude corresponding to the values of the variable itself, that is to say that one takes a dynamic criterion related to the values of the variable.
• the zone of the seismic cube to be qualified is limited in space in a guide zone 4 of the seismic cube 2 comprising and surrounding the pointed horizon to be qualified 1 (FIG. 2b).
• This zone 4 serves as a mask for selecting the pixels of the seismic cube on which the following steps of the method according to the invention will be carried out.
• the width of the zone 4 is defined according to a vertical section ( Figure 2b) of the seismic cube by a vertical parameter of deviation in pixels on either side of the pointed horizon 1.
• the dimensions of the guide zone surrounding the horizon can be varied spatially.
• the dimensions of the sliding window must be fixed.
• a geostatistical analysis to define objective criteria for choosing the dimensions of the sliding window.
• These criteria may include lengths of spatial correlations for example determined by the identification of the ranges of a variogram.
• the variogram is a statistical function that can be used to analyze spatial correlations within a spatial dataset, the observed ranges on the variogram providing information about the average dimensions of the structures (events) in an image.
• the value of the recovery rate parameter of the different positions of the sliding window 3 may be decreased to lighten the calculation times.
• this value will be chosen so as not to degrade the quality of the results.
• said recovery ratio is chosen equal to one pixel and a fixed reference R is placed.
• the position 6 of the window is shifted one pixel to the right relative to the position 5 of the window and the position 7 of the window is shifted one pixel to the right with respect to the position 6 of the window.
• the pixels marked are represented by points P.
• segmentation regions varies from one sliding window to another. Indeed, for example, if we put a sliding window on France, the segmentation will show 22 regions, it will not be the same for a slippery window falling on Germany.
• the process comprises the following steps: an allocation phase to the non-marked pixels of a value of indices E1 and EIC corresponding to a non-digital characteristic value;
• a pixel is defined by three dimensions x, y and z.
• a qualification attribute map is a representation in two dimensions x and y.
• An attribute defined by a pair of x and y coordinates may correspond to an average value per vertical (line oriented along the vertical axis z of depth of the seismic cube) for all the pixels located in the guide zone surrounding the horizon (for example ECI or average EII by vertical) or else the selection of a pixel on the vertical, for example that of the strongest ECI or weaker EII, and a value associated with this pixel.
• Figure 7a shows a map of the horizon to qualify.
• a color code represents the Z depth of the horizon.
• Figure 7b shows a position qualification map) of pixels selected because belonging to the most consistent local event.
• Figure 7c is a map of the qualification attribute of the horizon pointer to be qualified, this attribute being named LPE coherence attribute.
• anomalies can also consist, in phase jumps of the horizon corresponding to pointing errors which represent the unauthorized passage from one horizon to another or from one event to another:
• the pointed horizon is represented by a black line 17 and the qualified marked pixels, that is to say the pixels of higher ECI and lower EII are represented by a white line 18.
• the frame 19 contains a phase jump of the pointed horizon.
• the characterization of these differences is never easy, the simple difference between the two seismic cubes proving themselves often insufficient, or even sterile. Indeed, even small geographical offsets of the coherent events related to each of the two seismic cubes significantly decrease the value of the information of the difference cube. Thus, the simple difference between the two seismic cubes does not make it possible to distinguish between differences related on the one hand to the geographical offsets of the coherent seismic events and on the other hand to variations in energy (in value) of these events.
• the determination of the indices EII and ECI on each of the two cubes of the same geographical area makes it possible to overcome spatial spatial shifts of coherent events of the same geological origin and thus to allow the implementation of complementary quantitative tools for analyzing the differences between seismic cubes of the same geographical area.
• the process then comprises the following steps:
• the criterion of pairing may for example be a criterion of spatial proximity: two events "sufficiently" close, that is to say belonging to a common neighborhood are considered to correspond to the same geological event.
• the results of segmentation of one of the seismic cubes can be used to constrain the segmentation of the other cube.
• the Water Division Line algorithm makes it possible to construct markers for the segmentation of a seismic cube, these markers being obtained from the segmentation results of the other cube.
• the pairing of coherent events on each of the two images can be achieved by searching, with a certain vertical tolerance, pixels defining coherent events of one of the two seismic images closest to the pixels defining coherent events of the other. seismic image.
• the selection of matched coherent events makes it possible to dispense with the effects of geographical shift of coherent events and consequently to better characterize the differences between seismic cubes by calculating, for example, the simple difference in values, the geographical shift of coherent events on each. two cubes or any other relevant attribute.
• This approach in accordance with the method according to the invention, may be implemented in the context of any operational context involving different seismic cubes but the same geographical area (4D, 4C, multi-3D, etc.).

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• 2005
  • 2005-04-15 FR FR0503793A patent/FR2884636B1/fr active Active
• 2006
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