Examples of multi-attribute, neural network-based seismic object detection
P. De Groot, H. Ligtenberg, T. Oldenziel, D. Connolly, P. Meldahl, 2004. "Examples of multi-attribute, neural network-based seismic object detection", 3D Seismic Technology: Application to the Exploration of Sedimentary Basins, Richard J. Davies, Joseph A. Cartwright, Simon A. Stewart, Mark Lappin, John R. Underhill
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Certain seismic objects, like faults and gas chimneys, are often difficult to delineate using conventional attribute analysis. Many attributes contain useful information about the target object but each new attribute provides a new and different view of the data. The challenge is to find the optimal attribute for a specific interpretation. In this paper the optimal attribute is found with a pattern recognition approach based on multi-dimensional/multi-attributes and neural network modelling. Multi-dimensional attributes, as opposed to point attributes, can provide the spatial information on the seismic objects. The role of the neural network is to classify the input attributes into two or more output classes. Neural networks are trained on seismic attributes extracted at representative example locations that are manually picked by a seismic interpreter. This approach is a form of supervised learning in which the network learns to recognize certain seismic responses associated with the identified target objects. Application of the trained network yields an ‘object probability’ cube for the target object. Essentially, the neural network can target any seismic or geological feature requiring detailed analysis. In this paper the method is described and examples are shown of gas chimneys, faults, salt domes and 4D anomalies. Some interpretation aspects are discussed.
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3D Seismic Technology: Application to the Exploration of Sedimentary Basins
A ‘new age’ of subsurface geological mapping that is just as far ranging in scope as the frontier source geological mapping campaigns of the past two centuries in emerging. It is the direct result of the advent of 2D, and subsequently 3D, seismic data paralleled by advances in seismic acquisition and processing over the past three decades. Subsurface mapping is fuelled by the economic drive to explore and recover hydrocarbons but inevitably it will lead to major conceptual advances in Earth sciences, across a broader range of disciplines than those made during the 2D seismic revolution of the 1970s. Now that 3D seismic data coverage has increased and the technology is widely available we are poised to mine the full intellectual and economic benefits. This book illustrates how 3D seismic technology is being used to understand depositional systems and stratigraphy, structural and igneous geology, in developing and producing from hydrocarbon reservoirs and also what recent technological advances have been made. This technological journey is a fast-moving one where the remaining scientific potential still far exceeds the scope of the advances made thus far. This book explores the breadth of the opportunities that lie ahead as well as the inevitable accompanying challeges.