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Applying machine learning to the interpretation of seismic data
Seismic data gathered on the surface can be used to generate numerous seismic attributes that enable better understanding of subsurface geological structures and stratigraphic features. With an ever-increasing volume of seismic data available, machine learning augments faster data processing and interpretation of complex subsurface geology.
Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data.
Volume highlights include:
* Historic evolution of seismic attributes
* Overview of meta-attributes and how to design them
* Workflows for the computation of meta-attributes from seismic data
* Case studies demonstrating the application of meta-attributes
* Sets of exercises with solutions provided
* Sample data sets available for hands-on exercises
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List of contents
Preface
About the Authors
Abbreviations
List of Symbols and Operators
PART I: SEISMIC ATTRIBUTES
1. An Overview of Seismic Attributes
1.1 Introduction
1.2 Historical evolution of seismic attributes
1.3 Characteristics of Seismic Attributes
1.4 A glance at seismic characteristics
1.4.1 Amplitude
1.4.2 Phase
1.4.3 Frequency
1.4.4 Bandwidth
1.4.5 Amplitude Change
1.4.6 Slope Dip and Azimuth
1.4.7 Curvature
1.4.8 Seismic Discontinuity
1.5 Summary
References
2. Complex Trace, Structural and Stratigraphic Attributes
2.1 Introduction
2.2 Complex Trace Attributes: Mathematical Formulations and Derivations
2.3 Other Derived Complex Trace Attributes
2.3.1 Instantaneous Frequency
2.3.2 Sweetness
2.3.3 Relative Amplitude Change and Instantaneous Bandwidth
2.3.4 RMS Frequency
2.3.5 Q-factor
2.4 Structural and Stratigraphic Attributes
2.4.1 Dip and Azimuth Attributes
Slope and Dip Exaggeration
Dip-steering
2.4.2 Coherence Attribute
2.4.3 Similarity Attribute
2.4.4 Curvature Attribute
2.4.5 Advanced structural attributes
Ridge Enhancement Filter (REF) attribute
Thin Fault Likelihood (TFL) attribute
Pseudo Relief attribute
2.4.6 Amplitude Variance
2.4.7 Reflection Spacing
2.4.8 Reflection Divergence
2.4.9 Reflection Parallelism
2.4.10 Spectral Decomposition
2.4.11 Velocity, Reflectivity and Attenuation attributes
2.5 A glance on interpretation pitfalls
2.6 Summary
References
3. Be an Interpreter: Brainstorming Session
3.1 Task 1
3.2 Task 2
3.3 Task 3
3.4 Task 4
3.5 Task 5
3.6 Task 6
3.7 Task 7
3.8 Task 8
3.9 Task 9
3.10 Task 10
PART II: META-ATTRIBUTES
4. An Overview of Meta-attributes
4.1 Introduction
4.2 Meta-attributes
4.3 Types of Meta-attributes
4.3.1 Hydrocarbon Probability meta-attribute
4.3.2 Chimney Cube meta-attribute
4.3.3 Fault Cube meta-attribute
4.3.4 Intrusion Cube meta-attribute
4.3.5 Sill Cube meta-attribute
4.3.6 Mass Transport Deposit Cube meta-attribute
4.3.7 Lithology meta-attribute
4.4 Summary
References
5. An Overview of Artificial Neural Networks
5.1 Introduction
5.2 Historical Evolution
5.3 Biological Neuron Vs Mathematical Neuron
5.3.1 Biological Neuron
5.3.2 Mathematical Neuron
5.4 Activation or Transfer Function
5.5 Types of Learning
5.6 Multi-layer Perceptron (MLP) and the Backpropagation Algorithm
5.7 Different Types of ANNs
5.7.1 Radial Basis Function (RBF) Network
5.7.2 Probabilistic Neural Network (PNN)
5.7.3 Generalized Regression Neural Network (GRNN)
5.7.4 Modular Neural Network (MNN)
5.7.5 Self Organizing Maps (SOM)
5.8 Summary
References
6. How to Design Meta-attributes
6.1 Introduction
6.2 Meta-attribute design
6.2.1 Seismic Data conditioning
Mean Filter (or Running-Average filter)
Median Filter
Alpha-Trimmed Mean Filter
6.2.2 Selection and Extraction of
About the author
Kalachand Sain, Wadia Institute of Himalayan Geology, India
Priyadarshi Chinmoy Kumar, Wadia Institute of Himalayan Geology, India
