Seismic Multi-attribute Classification for Salt Boundary Detection:

A Comparison13 June, 2017

Haibin Di* and Ghassan AlRegib Center for Energy and Geo Processing,

Georgia Institute of Technology, Atlanta, GA

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Outline Introduction Proposed workflow Result analysis Conclusion

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Structural Interpretation Salt bodies are important geologic structures for subsurface hydrocarbon exploration

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Salt dome

Salt

Large-scale Seismic Data Acquisition

Manual Interpretation Time consuming Labor intensive

Motive for automated interpretation

Migrated Seismic Volume Seismic Interpretation [1-2]

[1] http://csegrecorder.com/articles/view/advances-in-true-volume-interpretation-of-structure-and-stratigraphy-in-3d[2] http://www.dgi.com/earthvision/evnews/evnews.html

1 km2 seismic survey for one hour

Seismic surveys: PetaBytes of data

Generate 1TB migrated data

Years for manual interpretation

Up to 160 shots/1 km2

20,000 traces per shot, every 10s

500 samples per trace per second

Generate 600GB data

ComputationalSeismic Interpretation

Challenges

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Motivation Manual interpretation is labor-intensive and time-

consuming for large seismic datasets; Computational seismic interpretation (e.g., object

detection, facies analysis) is a relatively recent research focus

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ComputationalSeismic Interpretation

Objective To verify the application of various machine learning algorithms to

seismic feature detection: Provide a fair comparison between different ML Use salt boundary detection as an example

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Salt domeSalt dome

Motivation Various approaches have been developed from other

disciplines, includinga. Edge detectionb. Texture analysisc. Seismic Saliencyd. Machine learning-based classification

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Sobel filter Salt likelihood

Common ML approaches Logistic regression

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Decision tree

Logistic function to link features and labels

Flowchart-like structure: non-leaf for features, and leaves for labels

Common ML approaches Random forest

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Support vector machine

An ensemble of decision trees Hyperplane boundary in feature domain

Common ML approaches Artificial neural network

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K-mean cluster analysis

Clustering of observations into separate groups

Complicated process mimicking the brain activities

Proposed workflow

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Load seismic amplitude

Attribute 1

Train models: SVM, ANN, k-means et al.

Pick training samples

Select seismic attributes

Attribute 2

.

Attribute N

Volumetric processing

Proposed workflow

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Step A: Attribute selection (12)

GLCM standard deviation

RMS amplitude

GLCM dissimilarity

GLCM variance

GLCM contrast

GLCM entropy

GoT

GLCM ASM

GLCM homogeneity

Saliency

GLCM energy

Canny edge

Proposed workflow

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Attribute vs.

salt boundary

Attribute Measurement Salt boundary Non-boundary

RMS amplitude Reflection intensity High Low

GLCM angular second moment

Spatial arrangement of seismic amplitude in

various statistical approaches

Low High

GLCM contrast High Low

GLCM dissimilarity High Low

GLCM energy Low High

GLCM entropy High Low

GLCM homogeneity Low High

GLCM standard deviation

High Low

GLCM variance High Low

Gradient of textureVariation of seismic

textureHigh Low

SaliencyAttention from an

interpreterHigh Low

Canny edge detection

Lateral similarity of waveform/amplitude

Low High

Human visual system (HVS) is sensitive to

Structure Motion Surrounding information

Attention models mimic the behavior of human subjects looking at an image or video

A new attribute: Seismic SaliencyImage

Formation

Light and Exposure Control

Visual Processing

[1] https://www.studyblue.com/notes/note/n/visual-process-and-perception/deck/14629318[2] http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/

Human Visual System [1]

Saliency Detection [2]

Detection

Attention Models in Seismic Interpretation

Human visual system

Attention models based on

Interpretation

Detection and delineation of salt domes

Detection and delineation of faults

Structural

[1] https://agilescientific.com/blog/2013/8/6/your-next-employment-contract.html

Attention Models based on HVS

We recently proposed a FFT-based saliency detection method, which compared toother detection algorithms

Effectively captures temporal and spatial saliency Have better computational efficiency Require few parameters selection

Saliency Detection

Zhiling Long and Ghassan AlRegib, Saliency detection for videos using 3D FFT local spectra, Proc. SPIE, vol. 9394, pp. 93941G93941G6, 2015.

Ft

FsF

Geometric Decomposition

Temporal Energy Distribution

Spatial Energy Distribution

A Video Frame

Seismic Saliency

M. A. Shafiq, Z. Long, T. Alshawi, and G. AlRegib, Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons, IEEE International Conference on Image Processing (ICIP), Beijing, China, Sep. 17-20, 2017.

Saliency Detection

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Crossline Section Saliency Map

Saliency Detection

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Time Section Saliency Map

Proposed workflow

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Step B: Training sample picking (879)

1.5 s

1.6 s

1.7 s

XL 700 XL 800 XL 900 XL 1000

1.4 s

-4000 +4000

Manual picking on one single vertical section (crossline 415), including 197 pickings on the salt-dome boundary (denoted as cyan dots); 682 pickings on the surrounding non-boundary features (denoted as magenta dots).

Proposed method

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Step C: Testing

(a) Logistic regression (863)(b) Decision tree (875)(c) Random forest (876)(d) Support vector machine (864)(e) Artificial neural network (843)(f) K-mean clustering (857)The re-clustering of the 879pickings Good accuracy Minor mis-clustering where the

seismic signals are similar tosalt boundaries

Proposed method

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Step D: Volumetric processing

At each sample in a volume, the twelve attributes are retrieved, based on which the trained modelgives its prediction. Such processing repeats at ALL samples.

Trained model

Probability: 0

Probability: 1

Probability: 0

Result analysis

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Salt probability

Salt-boundary probability from(a) Logistic regression(b) Decision tree(c) Random forest(d) Support vector machine(e) Artificial neural network(f) K-mean clustering

Original seismic amplitude

Result analysis

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3D view

Good match is observed byoverlaying the probability volumeon the original seismic amplitudefrom(a) Logistic regression(b) Decision tree(c) Random forest(d) Support vector machine(e) Artificial neural network(f) K-mean clustering

Result analysis

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Salt surface extractionSeeded tracking on the salt probability volume

3 seeds used in this work as black dots Salt surface

Result analysis

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Salt surface

Salt surface is generated by seed-tracking on the probabilityvolumes from(a) Logistic regression(b) Decision tree(c) Random forest(d) Support vector machine(e) Artificial neural network(f) K-mean clustering

The clipping of salt surfaces to fourrandomly-selected vertical sectionsRed: Logistic regressionMagenta: Decision treeBlack: Random forestYellow: Support vector machineGreen: Artificial neural networkCyan: K-means clustering

Result analysis

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2D view

Conclusion Six commonly-used machine learning approaches are compared for

multi-attribute salt-boundary detection from 3D seismic data with the same configurations,

12 seismic attributes 879 training samples

Similar results are observed, indicating its less sensitivity to ML algorithms

Among the six ML algorithms, k-means is least effective Accuracy is further improved with more training samples and more

complicated ML algorithms (such as CNN)

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Thank You

For more information about the center, please visit:

http://cegp.ece.gatech.edu/

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http://cegp.ece.gatech.edu/

Backup

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Saliency Detection

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M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, The role of visual saliency in the automationof seismic interpretation, accepted in Geophysical Prospecting, March, 2017.

M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, SalSi: A New Seismic Attribute For Salt Dome Detection, IEEE ICASSP, Shanghai, China, Mar. 20-25, 2016.

Seismic Saliency

Seismic Volume Saliency Map

M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, The role of visual saliency in the automationof seismic interpretation, accepted in Geophysical Prospecting, March, 2017.

M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, SalSi: A New Seismic Attribute For Salt Dome Detection, IEEE ICASSP, Shanghai, China, Mar. 20-25, 2016.

Saliency in Interpretation

Saliency Detection Thresholding

Post Processing

V S B

Salt Dome Highlighting

SALIENCY DETECTION