John Wiley & Sons Meta-attributes and Artificial Networking Cover Applying machine learning to the interpretation of seismic data Seismic data gathered on the surfac.. Product #: 978-1-119-48200-0 Regular price: $142.06 $142.06 In Stock

Meta-attributes and Artificial Networking

A New Tool for Seismic Interpretation

Sain, Kalachand / Kumar, Priyadarshi Chinmoy

Special Publications


1. Edition July 2022
288 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-48200-0
John Wiley & Sons

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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

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.


About the Authors


List of Symbols and Operators


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


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


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


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


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


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


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 Seismic Attributes

6.2.3 Example Location

6.2.4 NN operation

Evaluation of intelligent neural model

6.2.5 Validation

6.3 RGB Blending and Geo-body Extraction

6.4 Summary



7. Chimney interpretation using meta-attribute

7.1 Gas Chimneys: a clue for hydrocarbon exploration

7.2 Research Methodology

7.3 Chimney Validation

7.3.1 Geological Validation

7.3.2 Petrophysical Validation

7.3.3 Soft sediment deformation anomalies

7.4 Interpretation using Chimney Cube

7.5 Summary


8. Fault Interpretation Using Meta-attribute

8.1 Fault meta-attribute: a motivation

8.2 Research Methodology

8.3 Results and Interpretation

8.4 Efficiency of the optimized TFC

8.5 Summary


9. Fault and Fluid Migration Interpretation Using Meta-attribute

9.1 Introduction

9.2 Geophysical Data

9.3 Results and Interpretation

9.3.1 Thinned Fault Cube (TFC) and Fluid Cube (FlC)

9.3.2 Neural Design for the TFC and FlC

9.3.3 Interpretation using TFC and FlC

9.4 Summary


10. Magmatic Sill Interpretation Using Meta-attribute (Part 1: Taranaki Basin example)

10.1 Magmatic Sills: Interpretation techniques

10.2 Research Methods

10.2.1 Structural conditioning

10.2.2 Selection of attributes

10.2.3 Example Locations

10.2.4 Neural Network

10.2.5 Validation

10.3 Results and Interpretation

10.4 Discussion

10.4.1 Sill cube an efficient interpretation tool for magmatic sills

10.4.2 Limitations of the Sill Cube automated approach

10.5 Conclusions


11. Magmatic Sill Interpretation Using Meta-attribute (Part 2: Vøring Basin example)

11.1 Introduction: The Vøring Basin case

11.2 Description of the Data

11.3 Interpretation based on SC meta-attribute computation

11.4 Summary


12. Magmatic Sill and Fluid Plumbing Interpretation Using Meta-attribute (Canterbury Basin example)

12.1 Introduction: The Canterbury Basin case

12.2 Description of the Data

12.3 Results and Interpretation

12.3.1 Data Enhancement, Attribute Analysis and Neural Operation

12.3.2 Interpretation through Sill Cube (SC) and Fluid Cube (FlC) meta-attributes

12.3.3 Limitation of the automated approach

12.4 Summary


13. Volcanic System Interpretation Using Meta-attribute

13.1 Introduction

13.2 Research Workflow

13.3 Results and Interpretation

13.3.1 Seismic Data Enhancement

13.3.2 Neural Networks: Analysis and Optimization

13.3.3 Geologic interpretation using IC meta-attribute

13.3.4 Validation of the IC meta-attribute

13.4 Summary


14. Interpretation of Mass Transport Deposits Using Meta-attribute

14.1 Introduction

14.2 Data and Research Workflow

14.3 Results and Interpretation

14.4 Summary


Appendix A

A.1 Mathematical formulation of some common series and transformation

A.1.1 Fourier Series

A.1.2 Fourier and Inverse Fourier Transforms

A.1.3 Hilbert Transform

A.1.4 Convolution

A.2 Dip-Steering

Appendix B

B.1 Answers to seismic cross-section interpretation (Tasks 1-6)

B.2 Answers to numerical tasks (Tasks 7-10)

Kalachand Sain, Wadia Institute of Himalayan Geology, India

Priyadarshi Chinmoy Kumar, Wadia Institute of Himalayan Geology, India

K. Sain, CSIR-National Geophysical Research Group, Hyderabad, India; P. C. Kumar, CSIR-National Geophysical Research Group, Hyderabad, India