Post on 13-Mar-2020
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
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
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
Step 2: Training sample picking
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Training samples: ~20,000 from 3 vertical sections~ 10 minutes picking
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
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
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/