Characterization of migrated seismic volumes using texture attributes: a comparative study
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1 School of Electrical & Computer Engineering Characterization of migrated seismic volumes using texture attributes: a comparative study Zhiling Long, Yazeed Alaudah, Muhammad Ali Qureshi, Motaz Al Farraj, Zhen Wang, Asjad Amin, Mohamed Deriche, and Ghassan AlRegib Center for Energy and Geo Processing (CeGP) at Georgia Tech and KFUPM
2 Motivation Migrated Seismic Image Natural Texture Image (Wood) Migrated seismic images are texture images o Abundant texture image attributes are available in image processing literature. o Can they be used to characterize subsurface structures for computer assisted understanding? o Can they be useful for any other seismic applications? 2
3 Outline Typical texture image attributes o Frequency domain attributes o Space domain attributes Seismic application: structure-based seismic image retrieval Conclusions 3
4 Steerable Pyramid Image source: 4
5 Curvelet Transform Translated to origin Periodically extend & wrap 5
6 Curvelet: Typical Spectra 6
7 Curvelet: Examples Image source: 7
8 Outline Typical texture image attributes o Frequency domain attributes o Space domain attributes Seismic application: structure-based seismic image retrieval Conclusions 8
9 Local Binary Pattern (LBP) Illustration Sampling Schemes (R, P) Spot Corner Vertical Edge Horizontal Edge Typical Microstructures Complete LBP (CLBP) CLBP-S: sign of difference (i.e., LBP) CLBP-M: magnitude of difference CLBP-C: center pixel 9
10 LBP Example Salt Dome CLBP-S CLBP-M CLBP-C 10
11 Local Radius Index (LRI) Examines distribution of distances between adjacent edges along various angles. o LRI-A: measures width of Adjacent smooth regions o LRI-D: measures Distances from pixels to nearest edge 11
12 LRI Example A-1 A-2 A-3 A-4 A-5 A-6 A-7 A-8 12
13 Outline Typical texture image attributes o Frequency domain attributes o Space domain attributes Seismic application: structure-based seismic image retrieval Conclusions 13
14 Structure-based Retrieval Query (Image #1) Database Retrieval Ranking #1 Query (Image #2) Database Retrieval Ranking #2 Performance Evaluation Query (Image #N) Database Retrieval Ranking #N Similarity between histograms of texture attributes Ranking #1 Query (Image #1) Score 1 Score n Metric A Score N Rationale An attribute good for contentbased image retrieval is good at characterizing the image content. Database (Images #2-N) Here, the image content is a certain subsurface structure. 14
15 Retrieval: Evaluation Metrics Rank Reciprocal Rank Retrieved Image Match? (Y / N) 1/5=0.20 5/5=1.00 A N 2/5=0.40 4/5=0.80 B Y 3/5=0.60 3/5=0.60 C N 4/5=0.80 2/5=0.40 D Y 5/5=1.00 1/5=0.20 E Y P@n: How many of the first n retrieved images are matching images? MAP: What fraction among the retrieved are matching images? In this example, MAP = (1/2+2/4+3/5)/3 = 0.53 Four measures: all ranged 0 1; higher is better. o Precision at n (P@n) o Mean average precision (MAP): deals with multiple matching images. o Retrieval accuracy (RA): o Area under ROC curve (AUC): typical metric for binary detection problem considering both rate of true detection and rate of false positive 15
16 Dataset 400 images extracted from Netherlands Offshore F3 Block, available from OpendTect.org 4 classes with 100 images per class A more comprehensive dataset (LANDMASS) has been recently developed at CeGP: Horizon Chaotic Horizon Fault Salt Dome 16
17 Retrieval Results Attribute MAP RA AUC Comp. Time (s) Steerable Pyramid Curvelet LBP LRI SeiSIM SeiSIM is a seismic similarity metric previously developed at CeGP, used as a benchmark. 17
18 Conclusions Texture image attributes are generally capable of characterizing subsurface structures in migrated data. o Curvelet is the overall best, especially useful for characterizing curvature details commonly found with faults, horizons, and salt dome boundaries. o LBP is a good alternative in the space domain, capable of capturing repeating textural patterns in a computationally efficient way. The attributes can be useful for various seismic applications such as o Automatic labeling o Salt dome detection o Seismic image enhancement 18
19 Questions & Answers Thank You! 19
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