Seismic fault detection based on multi-attribute support vector

machine analysisHaibin Di, Muhammad Amir Shafiq, and Ghassan AlRegib

Center for Energy & Geo Processing (CeGP)Georgia Institute of Technology

{hdi7, amirshafiq, alregib}@gatech.eduSeptember 27, 2017

INT 5: Fault and Salt @ SEG 2017

Outline• Motivation• Workflow description• SVM and MLP analysis• CNN analysis• Conclusions

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Motivation• Fault interpretation is important for subsurface interpretation and

reservoir characterization from 3D seismic data• Lots of methods/algorithms available:

• Attributes: coherence, curvature, flexure, likelihood, and so on• Techniques: ant-tracking, Hough transform, time wrapping, motion vectors,

and so on

• Seismic data size is significantly increasing, which requires more interpretation efforts;

• Machine learning is efficient in big data analysis• How is its performance on seismic fault detection?

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Problem definition from machine learning

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Image ClassificationClasses

Seismic

Natural1. Person2. Vehicle3. Others

1. Fault2. Non-fault

Multi-attribute SVM/MLP analysis

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Workflow

Step 1: attribute selection

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Group Attribute Measurement Fault

Geometric attribute

Dip

Lateral variation of the geometry of seismic reflectors

Steeply dipping

CurvatureJuxtaposition of positive and

negative curvatures

FlexurePeak flexure with two subtle

sidelobesGeometric fault High values

Edge-detection attribute

Coherence

Lateral changes in seismic waveform and/or amplitude

using various operators

Low coherenceSobel edge High valuesSemblance Low semblanceCanny edge High values

Similarity Low similarityVariance High variance

Texture attribute

GLCM contrast Statistical analysis of local distribution of seismic

amplitude

High contrast

GLCM homogeneity Low homogeneity

Gradient of texture (GoT) Variation of seismic texture High GoTSaliency Attraction to interpreter eyes High saliency

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14 attributes

Step 2: Training sample picking

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Training samples: ~20,000 from 3 vertical sections~ 10 minutes picking

Step 3: Optimal ML model training

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Support vector machine (SVM)

Manual

SVM

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Multi-layer perceptron (MLP)

Manual MLP

SVM

MLP

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Confusion matrix of SVM

Confusion matrix of MLP

SVM

MLP

Step 4: Volumetric processing

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SVM

MLP

Result

13Time 1132 ms Inline 1791 Crossline 2600

Interpretation

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Fault volume imaging

Seeded fault picking

Automatic fault extraction

Multi-attribute SVM/MLP analysis

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Attribute selection is important (e.g., Barnes and Laughlin, 2002; Zhao et al., 2015)

a) ideal: perfect classification b) In most cases: overlapping between two features in the attribute domain

GLCM ContrastVa

rianc

e

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Contribution of 14 attributes - Attribute weight matrix

CNN analysis

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With original amplitude as input, seismic attribute selection is not necessary

The architecture of 1-layer convolutional neural network

CNN analysis

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Manual vs. CNN of the training section Four randomly-selected vertical section of the generated fault volume

Total precision: 0.88True positive rate: 0.99

PredictionNon-fault Fault

ActualNon-fault 262248 37744

Fault 94 18154

CNN analysis

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16 attributes

Conclusions

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• Supervised ML classification is applied to seismic data for fault detection

• Both SVM and MLP are used for multi-attribute based classification• MLP has better performance with enough training samples• Attribute selection is important and relies on interpreters’ experience

• CNN is capable of generating attributes automatically to complete fault classification, which requires less from an interpreter.

• More work is in need to• Promote sample-level to Image/volume-level detection and segmentation• Achieve cross-dataset interpretation• Build open and comprehensive training datasets with all features of interest

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…… in the early morning

More information (e.g., recent research, publications, tools, and codes) is available by:Visit our booth: #2109Visit our center: http://www.ghassanalregib.comVisit my webpage: https://sites.google.com/site/dihaibin/