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Institute of Wave & Information, Xi'an Jiaotong University, Xi'an, P.R. China. International Symposium on Geophysical Imaging with Localized Waves Sanya, Hainan, 24-28 July, 2011. Abrupt Feature Extraction via the Combination of Sparse Representations.

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Abrupt Feature Extraction via the Combination of Sparse Representations

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Abrupt feature extraction via the combination of sparse representations

Institute of Wave & Information, Xi'an Jiaotong University, Xi'an, P.R. China

International Symposium on Geophysical Imaging with Localized Waves

Sanya, Hainan, 24-28 July, 2011

Abrupt Feature Extraction via the Combination of Sparse Representations

Wei Wang, Wenchao Chen, Jinghuai Gao, Jin Xu

Institute of Wave & Information,

Xi’an Jiaotong University


Abrupt feature extraction via the combination of sparse representations

Wenchao Chen

Jinghuai Gao

Jin Xu

Co-Authors


Abrupt feature extraction via the combination of sparse representations

Outline

  • Introduction

  • Morphological Component Analysis

  • Abrupt Feature Extraction Methodology

  • Synthetic and field data examples

  • Conclusions


Abrupt feature extraction via the combination of sparse representations

Introduction

  • Problems for stratigraphic interpretation

    • Complexity of channels in 3D seismic data always makes detailed interpretation challenging;

    • Definition of sand bars and beaches also becomes complicated as they are only partially visible when seismic amplitude is examined.

  • How to improve imaging these features

    • Current interpretation workflows adopt advanced color and opacity based co-rendering techniques to merge multiple stratigraphic attributes.


Abrupt feature extraction via the combination of sparse representations

  • Time-consuming to interpret many seismic attributes;

  • Multi-attribute analysis techniques, e.g., multi-attribute

    3D visualization, PCA, cluster analysis, sometimes

    lack explicit physical meaning.

  • Current multi-attributes trap

    • Instantaneous attributes, e.g., the peak frequency depicts horizontal distribution of channels and the envelope highlight bright spots and dim spots;

    • Coherence attributes are taken to delimit the edges of stratigraphic units;

    • Spectral decomposition generates narrow-band instantaneous amplitude volumes and allows better imaging channel details.


Abrupt feature extraction via the combination of sparse representations

  • Motivation

    • Raw seismic time samples contain all information represented by various seismic attributes;

    • The amplitude and phase spectra and the spatial architecture within an analysis window form specific waveform patterns.

  • Our approach

    • Adopt the morphological component analysis (MCA) theory (Starck, et al., 2005), to extract waveforms of meaningful stratigraphic targets from seismic data;

    • Different waveform dictionaries are chosen to represent specific waveform patterns by exploring their sparsity.


Abrupt feature extraction via the combination of sparse representations

where each signal component can be described as

with an overcomplete dictionary and a sparse representation .

Morphological Component Analysis

  • Assumptions of the MCA

    • Firstly, the signal is assumed as a linear combination of different morphological features which are sparsely represented by n different dictionaries:


Abrupt feature extraction via the combination of sparse representations

  • Assumptions of the MCA

    • Secondly, the MCA assumes that for any given component skthe sparsest decomposition over the proper dictionary yields a highly sparse description, while its decomposition over the other dictionaries,

      Φj≠k, is highly non sparse, that is

  • Sparsity-promoted signal separation

    • In the MCA framework, the decomposition coefficients of corresponding signal components are the solutions of


Abrupt feature extraction via the combination of sparse representations

NP-Hard Optimization Problem

  • Sparsity-promoted signal separation

    • By substituting the norm by an norm, and relaxing the equality constraint, the MCA algorithm finally seeks a solution by solving the convex minimization problem (Starck, et al., 2005),


Abrupt feature extraction via the combination of sparse representations

  • Signal decomposition example

Wavelet dictionary

DCT dictionary

“Bumps+Cosine” Separation


Abrupt feature extraction via the combination of sparse representations

: abrupt features; : coherent events;

n: a zero-mean Gaussian noise matrix with a standard deviation .

Abrupt Feature Extraction Methodology

  • Signal model assumption

    • According to morphologic appearance in vertical sections, volumetric flattened seismic data are composed of coherent events and abrupt features;

    • We thus model these two kinds of seismic features as linear structures and punctate structures respectively. A vertical section s along the inline direction is formulated as


Abrupt feature extraction via the combination of sparse representations

& : representing dictionaries (s1 & s2);

& : controlling how much the sparseness priors are emphasized on the model.

  • Nonlinear optimization model

The success of MCA relies on the incoherence

between sub-dictionaries and each sub-dictionary

should lead to sparse representations of the

corresponding signal component.


Abrupt feature extraction via the combination of sparse representations

  • Choice of representations

    • The 2D-UWT presents only three directional elements independent of scales, and there are no highly anisotropic elements. We expect the 2D-UWT to be non-optimal for detection of highly anisotropic features and adopt it to detect punctate features.

Fig.1 2D-UWT atoms along the horizontal, vertical, and diagonal direction, with three different scale indexes.


Abrupt feature extraction via the combination of sparse representations

  • Choice of representations

    • The curvelet transform is a redundant dictionary and the curvelet elements are anisotropic and obey the parabolic scaling, which makes it the best choice for the detection of anisotropic structures such as coherent wavefronts.

Fig.2 Discrete curvelets indexed by different scale, orientation, and location.


Abrupt feature extraction via the combination of sparse representations

1) Initialize N, ,

2) Iterative procedure, n=1,2, …,N,

a) Update x1, assume x2 fixed,

b) Update x2, assumex1fixed,

3)

  • Implementation

To deal with 3D seismic data, our algorithm is performed line-by-line, each vertical slice separated into abrupt features and coherent events via the iterative-shrinkage algorithm (Bruce et al., 1998; Daubechies et al., 2005):


Abrupt feature extraction via the combination of sparse representations

Synthetic data examples

(a) The earth model with a thin channel with the size of 100-m wide and 10-m thick embedded. Both the horizontal and vertical sampled at 2.5-m.

(b) The migrated seismic section is generated by the PSDM algorithm using a Ricker wavelet with dominant frequency 60 Hz.

The reflection waveforms of the channel appear as a bright spot and overlap the coherent reflection events.


Abrupt feature extraction via the combination of sparse representations

Separated synthetic sections by the proposed method: (a) The migrated seismic section of the earth model; (b) The extracted abrupt features part and (c) the coherent events part.


Abrupt feature extraction via the combination of sparse representations

CMP No. CMP No.

Relative Geological Time (s)

(a) An inline section from volumetric flattened 3D seismic data

(b) The separated coherent events part using the proposed method

Field data examples


Abrupt feature extraction via the combination of sparse representations

CMP No. CMP No.

Relative Geological Time (s)

(a) An inline section from volumetric flattened 3D seismic data

(c) The extracted abrupt features part using the proposed method


Abrupt feature extraction via the combination of sparse representations

CMP No. CMP No. CMP No.

Relative Geological Time (s)

Top of Channel

Bottom of Channel

(c) Zoomed part of the abrupt features component

(a) Zoomed part of the inline section

(b) Zoomed part of the coherent events component


Abrupt feature extraction via the combination of sparse representations

(a) An horizontal slice from volumetric flattened 3D seismic data

(b) The extracted abrupt features part using the proposed method


Abrupt feature extraction via the combination of sparse representations

(a) An horizontal slice from volumetric flattened 3D seismic data

(b) The extracted abrupt features part using the proposed method


Abrupt feature extraction via the combination of sparse representations

Conclusions

  • The MCA technique is utilized to extract sedimentary features from 3D volumetric flattened seismic data;

  • Since sedimentary features modeled as punctate structures, the 2D-UWT is chosen to represent the sedimentary features while the curvelet transform, is chosen to sparsify coherent events representation;

  • Both synthetic & field data examples show the efficiency of our method for interpreting sedimentary features. The extracted abrupt feature waveforms can be used for subsequent quantitive analysis and reservoir modeling.


Abrupt feature extraction via the combination of sparse representations

Acknowledgements

  • This work has been partially supported by the NSFC (No. 40730424 & No. 40674064), the NHTRDPC (No. 2006AA09A102-11), and the INSTSP (No. 2008ZX05023-005-005 & No. 2008ZX05025-001-009).

  • We would like to thank Exploration and Development Research Institute of Daqing Oilfield Company Ltd. for their supporting us seismic data.


Abrupt feature extraction via the combination of sparse representations

Thank You !


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