Salt Dome Detection and Tracking Using Texture Analysis and Tensor-based Subspace Learning

Transcription

1 Salt Dome Detection and Tracking Using Texture Analysis and Tensor-based Subspace Learning Zhen Wang*, Dr. Tamir Hegazy*, Dr. Zhiling Long, and Prof. Ghassan AlRegib 02/18/ /42

2 Outline Introduction Salt-dome Detection Salt-dome Tracking Texture Tensor Classification Tracked Boundary Synthesis Experimental Results Conclusion and Future Work 2/42

3 Salt Domes Formed by the deposition of salt Closely related to reservoir regions

4 Motivation Manual interpretation is time consuming and labor intensive Drawbacks of salt-dome detection methods: Based on graph theory: large computational cost Based on the combinations of various seismic attributes (e.g., amplitude, dips, and frequency, ): tweak combinational weights 4/42

5 Dataset A subset of Netherlands offshore F3 block (OpendTect) Dimension: Time*Crossline*Inline = 137*301*351 Resolution: inline & crossline: 25m, time: 4ms 20m Inline # /42

6 Outline Introduction Salt-dome Detection Salt-dome Tracking Texture Tensor Classification Tracked Boundary Synthesis Experimental Results Conclusion and Future Work 6/42

7 Traditional Edge Detectors Caprock Boundaries Detected Side Boundaries Not Detected 7/42

8 Overview of Detection Method Seed Detection Seismic Sections Compute Gradient of Texture (GoT) Thresholding Region Growing Morphology Detected Boundaries 8/42

9 Gradient of Texture (GoT) Human interpretation involves sensitivity to texture change Texture Boundary Texture Region 1 Texture Region 2 GoT Is Being Computed at This Point Sliding Direction GoT captures change in texture GoT measures dissimilarity between neighborhood windows 1 st Neighborhood Window Gradient of Texture = Dissimilarity (W -, W + ) 2 nd Neighborhood Window x 9/42

10 Gradient of Texture (GoT) To detect texture boundaries of any orientation Compute x and y components of GoT: G x, G y Compute GoT as the magnitude of the (G x, G y ) vector Dissimilarity Measure Neighborhood Windows Texture Boundary Neighborhood windows used to compute x component W x-, W x+ Neighborhood windows used to compute y component W y-, W y+ 10/42

11 Dissimilarity Measure To capture human sensitivity to texture change, we use a dissimilarity metric of the variety of [HA14]. DFT magnitude spectrum of DFT magnitude spectrum was shown to be consistent with human perception of change Expectation Operator 2D DFT [HA14] T. Hegazy and G. AlRegib, COHERENSI: A new full-reference IQA using error spectrum chaos, Proc. of IEEE GlobalSIP, Atlanta, GA, Dec. 3-5, /42

12 Region Growing GoT attribute returns many boundaries Some are Salt boundaries Others are not related to salt Normalized GoT Attribute Detect a salt point (seed point) Grow seed until GoT threshold is hit Seed Point 12/42

13 Seed Selection Interactive Labor saving Single click vs. traversing long tortuous boundary Automated Smoothed multi-scale directionality attribute can be used to detect a salt point with high confidence Multi-scale directionality map Seed point selection lowest directionality after Gaussian smoothing Eigenvalues of moment of inertia tensor of gradient components scatter plot Gaussian filter 13

14 Detection Steps by Example 0. Seismic Section 1. Normalized GoT Attribute 2. Thresholded GoT 3. (3) After Region Growing 4. Dilated Region 5. Region Boundary 14/42

15 Outline Introduction Salt-dome Detection Salt-dome Tracking Texture Tensor Classification Tracked Boundary Synthesis Experimental Results Conclusion and Future Work 15/42

16 What is a tensor? Multidimensional arrays (N-th order) N 1, vectors N 2, matrices N 3, data volumes N-th-order tensor has N modes H. Lu, K. N. Plataniotis and A. N. Venetsanopoulos "A survey of multilinear subspace learning for tensor data", Pattern Recognit., vol. 44, no. 7, pp /42

17 Unfolding: Modes of Tensors The n-th mode of tensor (unfolding): 1-Mode: 2-Mode: 3-Mode: 8/42

18 Basic Operations of Tensors n-mode product of a tensor by matrix The scale product of two tensors Frobenius norm of : 18/42

19 Tensor Decomposition and Multi-linear PCA Singular value decomposition (SVD) A U Σ V T Similar to SVD, tensors can be decomposed as: V A U Σ Projection matrices along different modes 19/42

20 Tensor Decomposition and Multi-linear PCA Dimensional reduction MPCA: Projection matrices determine the mapping: : is composed of eigenvectors corresponding to the largest P n eigenvalues of 20/42

21 Textures along Salt-Dome Boundary Seismic Patches: GoT Contrast Patches: 21/42

22 Texture Tensor Classification Classification Criterion 22/42

23 Texture Tensor Classification Classification Criterion 23/42

24 Texture Tensor Classification Classification Criterion 24/42

25 Texture Tensor Classification Texture Tensor Classification 25/42

26 Classification Criterion Reconstruction Error: Current Pair of Texture Tensors Projection Matrices 26/42

27 Examples of Classified Tensors 27/42

28 Outline Introduction Salt-dome Detection Salt-dome Tracking Texture Tensor Classification Tracked Boundary Synthesis Experimental Results Conclusion and Future Work 28/42

29 Localization of Tracked Points Predicted Section Reference Section Candidate points correspond to texture patches:, e.g., Compare similarity to texture tensors built from the reference section 29/42

30 Reconstruction Error : weights for seismic sections and contrast maps Pair of Texture Tensors built from reference section Projection Matrices 30/42

31 Synthesis of Tracked Boundary Tracked points: Projected Boundary (Inline #399) Tracked Boundary (Inline #409) 31/42

32 Outline Introduction Salt-dome Detection Salt-dome Tracking Texture Tensor Classification Tracked Boundary Synthesis Experimental Results Conclusion and Future Work 32/42

33 Salt Dome Detection Inline #409 GLCM Contrast Map Inline #392 Gradient Map Ground Truth Detected Boundary 33/42

34 Local Regions of Detected Boundaries GoT map: GLCM Constrast Map: Gradient Map: Aqrawi, A. A., T. H. Boe, and S. Barros, 2011, Detecting salt domes using a dip guided 3D sobel seismic at-tribute: Presented at the 2011 SEG Annual Meeting,Society of Exploration Geophysicists

35 Fréchet Distance J K : 0,1 0,, : 0,1 0, A:[0, J] S, B :[0, K] S 35/42

36 SalSIM Index SalSIM Indices of Detected Boundaries Inline # Detection method with GoT maps Detection method with GLCM contrast maps Detection method with gradient maps AMD: averaged maximum distance 35/42

37 Salt Dome Tracking Inline #409 (a) Proposed tracking method (b) Tracking method based on vectorization (c) Tensor-based tracking method without GoT map (d) Tensor-based tracking method with GLCM contrast map Ground Truth Tracked Boundary 37/42

38 Local Regions of tracked Boundaries (a) Proposed tracking method (b) Tracking method based on vectorization (c) Tensor-based tracking method without GoT map (d) Tensor-based tracking method with GLCM contrast map 38/42

39 SalSIM Index SalSIM Indices of tracked Boundaries Inline # Proposed tracking method Tracking method based on vectorization Tensor-based tracking method without GoT maps Tensor-based tracking method only with GLCM contrast maps /42

40 SalSIM Index Tracked Boundaries based on Detected Results Inline # Proposed detection method Proposed tracking method based on detected boundary in Inline #400 40/42

41 Outline Introduction Salt-dome Detection Salt-dome Tracking Texture Tensor Classification Tracked Boundary Synthesis Experimental Results Conclusion and Future Work 41/42

42 Conclusion GoT maps can be used to detect salt-dome boundaries Tensor-based subspace learning can synthesize tracked boundaries Future work: 3D version of GoT attribute Build texture tensors through multiple seismic sections 42/42

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