{"product_id":"metaattributes-and-artificial-networking-9781119482000","title":"Metaattributes and Artificial Networking","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eApplying machine learning to the interpretation of seismic data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeismic 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMeta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation\u003c\/i\u003e explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVolume highlights include:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eHistoric evolution of seismic attributes\u003c\/li\u003e \u003cli\u003eOverview of meta-attributes and how to design them\u003c\/li\u003e \u003cli\u003eWorkflows for the computation of meta-attributes from seismic data\u003c\/li\u003e \u003cli\u003eCase studies demonstrating the application of meta-attributes\u003c\/li\u003e \u003cli\u003eSets of exercises with solutions provided\u003c\/li\u003e \u003cli\u003eSample \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eAbout the Authors\u003c\/p\u003e \u003cp\u003eAbbreviations\u003c\/p\u003e \u003cp\u003eList of Symbols and Operators\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I: SEISMIC ATTRIBUTES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. An Overview of Seismic Attributes\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction\u003c\/p\u003e \u003cp\u003e1.2 Historical evolution of seismic attributes\u003c\/p\u003e \u003cp\u003e1.3 Characteristics of Seismic Attributes\u003c\/p\u003e \u003cp\u003e1.4 A glance at seismic characteristics\u003c\/p\u003e \u003cp\u003e1.4.1 Amplitude\u003c\/p\u003e \u003cp\u003e1.4.2 Phase\u003c\/p\u003e \u003cp\u003e1.4.3 Frequency\u003c\/p\u003e \u003cp\u003e1.4.4 Bandwidth\u003c\/p\u003e \u003cp\u003e1.4.5 Amplitude Change\u003c\/p\u003e \u003cp\u003e1.4.6 Slope Dip and Azimuth\u003c\/p\u003e \u003cp\u003e1.4.7 Curvature\u003c\/p\u003e \u003cp\u003e1.4.8 Seismic Discontinuity\u003c\/p\u003e \u003cp\u003e1.5 Summary\u003c\/p\u003e \u003cp\u003e References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Complex Trace, Structural and Stratigraphic Attributes\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction\u003c\/p\u003e \u003cp\u003e2.2 Complex Trace Attributes: Mathematical Formulations and Derivations\u003c\/p\u003e \u003cp\u003e2.3 Other Derived Complex Trace Attributes\u003c\/p\u003e \u003cp\u003e2.3.1 Instantaneous Frequency\u003c\/p\u003e \u003cp\u003e2.3.2 Sweetness\u003c\/p\u003e \u003cp\u003e2.3.3 Relative Amplitude Change and Instantaneous Bandwidth\u003c\/p\u003e \u003cp\u003e2.3.4 RMS Frequency\u003c\/p\u003e \u003cp\u003e2.3.5 Q-factor\u003c\/p\u003e \u003cp\u003e2.4 Structural and Stratigraphic Attributes\u003c\/p\u003e \u003cp\u003e2.4.1 Dip and Azimuth Attributes\u003c\/p\u003e \u003cp\u003eSlope and Dip Exaggeration\u003c\/p\u003e \u003cp\u003eDip-steering\u003c\/p\u003e \u003cp\u003e2.4.2 Coherence Attribute\u003c\/p\u003e \u003cp\u003e2.4.3 Similarity Attribute\u003c\/p\u003e \u003cp\u003e2.4.4 Curvature Attribute\u003c\/p\u003e \u003cp\u003e2.4.5 Advanced structural attributes\u003c\/p\u003e \u003cp\u003eRidge Enhancement Filter (REF) attribute\u003c\/p\u003e \u003cp\u003eThin Fault Likelihood (TFL) attribute\u003c\/p\u003e \u003cp\u003ePseudo Relief attribute\u003c\/p\u003e \u003cp\u003e2.4.6 Amplitude Variance\u003c\/p\u003e \u003cp\u003e2.4.7 Reflection Spacing\u003c\/p\u003e \u003cp\u003e2.4.8 Reflection Divergence\u003c\/p\u003e \u003cp\u003e2.4.9 Reflection Parallelism\u003c\/p\u003e \u003cp\u003e2.4.10 Spectral Decomposition\u003c\/p\u003e \u003cp\u003e2.4.11 Velocity, Reflectivity and Attenuation attributes\u003c\/p\u003e \u003cp\u003e2.5 A glance on interpretation pitfalls\u003c\/p\u003e \u003cp\u003e2.6 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Be an Interpreter: Brainstorming Session \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Task 1\u003c\/p\u003e \u003cp\u003e3.2 Task 2\u003c\/p\u003e \u003cp\u003e3.3 Task 3\u003c\/p\u003e \u003cp\u003e3.4 Task 4\u003c\/p\u003e \u003cp\u003e3.5 Task 5\u003c\/p\u003e \u003cp\u003e3.6 Task 6\u003c\/p\u003e \u003cp\u003e3.7 Task 7\u003c\/p\u003e \u003cp\u003e3.8 Task 8\u003c\/p\u003e \u003cp\u003e3.9 Task 9\u003c\/p\u003e \u003cp\u003e3.10 Task 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II: META-ATTRIBUTES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. An Overview of Meta-attributes\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction\u003c\/p\u003e \u003cp\u003e4.2 Meta-attributes\u003c\/p\u003e \u003cp\u003e4.3 Types of Meta-attributes\u003c\/p\u003e \u003cp\u003e4.3.1 Hydrocarbon Probability meta-attribute\u003c\/p\u003e \u003cp\u003e4.3.2 Chimney Cube meta-attribute\u003c\/p\u003e \u003cp\u003e4.3.3 Fault Cube meta-attribute\u003c\/p\u003e \u003cp\u003e4.3.4 Intrusion Cube meta-attribute\u003c\/p\u003e \u003cp\u003e4.3.5 Sill Cube meta-attribute\u003c\/p\u003e \u003cp\u003e4.3.6 Mass Transport Deposit Cube meta-attribute\u003c\/p\u003e \u003cp\u003e4.3.7 Lithology meta-attribute\u003c\/p\u003e \u003cp\u003e4.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. An Overview of Artificial Neural Networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction\u003c\/p\u003e \u003cp\u003e5.2 Historical Evolution\u003c\/p\u003e \u003cp\u003e5.3 Biological Neuron Vs Mathematical Neuron\u003c\/p\u003e \u003cp\u003e5.3.1 Biological Neuron\u003c\/p\u003e \u003cp\u003e5.3.2 Mathematical Neuron\u003c\/p\u003e \u003cp\u003e5.4 Activation or Transfer Function\u003c\/p\u003e \u003cp\u003e5.5 Types of Learning\u003c\/p\u003e \u003cp\u003e5.6 Multi-layer Perceptron (MLP) and the Backpropagation Algorithm\u003c\/p\u003e \u003cp\u003e5.7 Different Types of ANNs\u003c\/p\u003e \u003cp\u003e5.7.1 Radial Basis Function (RBF) Network\u003c\/p\u003e \u003cp\u003e5.7.2 Probabilistic Neural Network (PNN)\u003c\/p\u003e \u003cp\u003e5.7.3 Generalized Regression Neural Network (GRNN)\u003c\/p\u003e \u003cp\u003e5.7.4 Modular Neural Network (MNN)\u003c\/p\u003e \u003cp\u003e5.7.5 Self Organizing Maps (SOM)\u003c\/p\u003e \u003cp\u003e5.8 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. How to Design Meta-attributes\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction\u003c\/p\u003e \u003cp\u003e6.2 Meta-attribute design\u003c\/p\u003e \u003cp\u003e6.2.1 Seismic Data conditioning\u003c\/p\u003e \u003cp\u003eMean Filter (or Running-Average filter)\u003c\/p\u003e \u003cp\u003eMedian Filter\u003c\/p\u003e \u003cp\u003eAlpha-Trimmed Mean Filter\u003c\/p\u003e \u003cp\u003e6.2.2 Selection and Extraction of Seismic Attributes\u003c\/p\u003e \u003cp\u003e6.2.3 Example Location\u003c\/p\u003e \u003cp\u003e6.2.4 NN operation\u003c\/p\u003e \u003cp\u003eEvaluation of intelligent neural model\u003c\/p\u003e \u003cp\u003e6.2.5 Validation\u003c\/p\u003e \u003cp\u003e6.3 RGB Blending and Geo-body Extraction\u003c\/p\u003e \u003cp\u003e6.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III: CASE STUDIES OF META-ATTRIBUTES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Chimney interpretation using meta-attribute\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Gas Chimneys: a clue for hydrocarbon exploration\u003c\/p\u003e \u003cp\u003e7.2 Research Methodology\u003c\/p\u003e \u003cp\u003e7.3 Chimney Validation\u003c\/p\u003e \u003cp\u003e7.3.1 Geological Validation\u003c\/p\u003e \u003cp\u003e7.3.2 Petrophysical Validation\u003c\/p\u003e \u003cp\u003e7.3.3 Soft sediment deformation anomalies\u003c\/p\u003e \u003cp\u003e7.4 Interpretation using Chimney Cube\u003c\/p\u003e \u003cp\u003e7.5 Summary\u003c\/p\u003e \u003cp\u003e References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Fault Interpretation Using Meta-attribute\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Fault meta-attribute: a motivation\u003c\/p\u003e \u003cp\u003e8.2 Research Methodology\u003c\/p\u003e \u003cp\u003e8.3 Results and Interpretation\u003c\/p\u003e \u003cp\u003e8.4 Efficiency of the optimized TFC\u003c\/p\u003e \u003cp\u003e8.5 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Fault and Fluid Migration Interpretation Using Meta-attribute\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction\u003c\/p\u003e \u003cp\u003e9.2 Geophysical Data\u003c\/p\u003e \u003cp\u003e9.3 Results and Interpretation\u003c\/p\u003e \u003cp\u003e9.3.1 Thinned Fault Cube (TFC) and Fluid Cube (FlC)\u003c\/p\u003e \u003cp\u003e9.3.2 Neural Design for the TFC and FlC\u003c\/p\u003e \u003cp\u003e9.3.3 Interpretation using TFC and FlC\u003c\/p\u003e \u003cp\u003e9.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Magmatic Sill Interpretation Using Meta-attribute (Part 1: Taranaki Basin example)\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Magmatic Sills: Interpretation techniques\u003c\/p\u003e \u003cp\u003e10.2 Research Methods\u003c\/p\u003e \u003cp\u003e10.2.1 Structural conditioning\u003c\/p\u003e \u003cp\u003e10.2.2 Selection of attributes\u003c\/p\u003e \u003cp\u003e10.2.3 Example Locations\u003c\/p\u003e \u003cp\u003e10.2.4 Neural Network\u003c\/p\u003e \u003cp\u003e10.2.5 Validation\u003c\/p\u003e \u003cp\u003e10.3 Results and Interpretation\u003c\/p\u003e \u003cp\u003e10.4 Discussion\u003c\/p\u003e \u003cp\u003e10.4.1 Sill cube an efficient interpretation tool for magmatic sills\u003c\/p\u003e \u003cp\u003e10.4.2 Limitations of the Sill Cube automated approach\u003c\/p\u003e \u003cp\u003e10.5 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Magmatic Sill Interpretation Using Meta-attribute (Part 2: Vøring Basin example)\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction: The Vøring Basin case\u003c\/p\u003e \u003cp\u003e11.2 Description of the Data\u003c\/p\u003e \u003cp\u003e11.3 Interpretation based on SC meta-attribute computation\u003c\/p\u003e \u003cp\u003e11.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Magmatic Sill and Fluid Plumbing Interpretation Using Meta-attribute (Canterbury Basin example)\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction: The Canterbury Basin case\u003c\/p\u003e \u003cp\u003e12.2 Description of the Data\u003c\/p\u003e \u003cp\u003e12.3 Results and Interpretation\u003c\/p\u003e \u003cp\u003e12.3.1 Data Enhancement, Attribute Analysis and Neural Operation\u003c\/p\u003e \u003cp\u003e12.3.2 Interpretation through Sill Cube (SC) and Fluid Cube (FlC) meta-attributes\u003c\/p\u003e \u003cp\u003e12.3.3 Limitation of the automated approach\u003c\/p\u003e \u003cp\u003e12.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Volcanic System Interpretation Using Meta-attribute \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction\u003c\/p\u003e \u003cp\u003e13.2 Research Workflow\u003c\/p\u003e \u003cp\u003e13.3 Results and Interpretation\u003c\/p\u003e \u003cp\u003e13.3.1 Seismic Data Enhancement\u003c\/p\u003e \u003cp\u003e13.3.2 Neural Networks: Analysis and Optimization\u003c\/p\u003e \u003cp\u003e13.3.3 Geologic interpretation using IC meta-attribute\u003c\/p\u003e \u003cp\u003e13.3.4 Validation of the IC meta-attribute\u003c\/p\u003e \u003cp\u003e13.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Interpretation of Mass Transport Deposits Using Meta-attribute \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction\u003c\/p\u003e \u003cp\u003e14.2 Data and Research Workflow\u003c\/p\u003e \u003cp\u003e14.3 Results and Interpretation\u003c\/p\u003e \u003cp\u003e14.4 Summary\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Mathematical formulation of some common series and transformation\u003c\/p\u003e \u003cp\u003eA.1.1 Fourier Series\u003c\/p\u003e \u003cp\u003eA.1.2 Fourier and Inverse Fourier Transforms\u003c\/p\u003e \u003cp\u003eA.1.3 Hilbert Transform\u003c\/p\u003e \u003cp\u003eA.1.4 Convolution\u003c\/p\u003e \u003cp\u003eA.2 Dip-Steering\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Answers to seismic cross-section interpretation (Tasks 1-6)\u003c\/p\u003e \u003cp\u003eB.2 Answers to numerical tasks (Tasks 7-10)\u003c\/p\u003e \u003cp\u003eGlossary\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407063523671,"sku":"9781119482000","price":112.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119482000.jpg?v=1730498046","url":"https:\/\/bookcurl.com\/products\/metaattributes-and-artificial-networking-9781119482000","provider":"Book Curl","version":"1.0","type":"link"}