The current lack of a robust, standardized technique for geophysical mapping of karst systems can be attributed to both the complexity of the environment and prior technological limitations. Abrupt lateral variations in physical properties that are inherent to karst systems generate significant geophysical noise, challenging conventional seismic signal processing and interpretation. Modern application of neural networks to multi-attribute seismic interpretation now provide a semiautomated method for identifying and leveraging the nonlinear relationships exhibited among seismic attributes. The ambiguity generally associated with designing neural networks for seismic object detection can be reduced via statistical analysis of the extracted attribute data. A data-driven approach to selecting the appropriate set of input seismic attributes, as well as the locations and minimum number of training examples, provides a more objective and computationally efficient method for identifying karst systems using reflection seismology. This statistically optimized neural network technique is thoroughly demonstrated using three-dimensional seismic reflection data collected from the southeastern portion of the Florida carbonate platform. Several dimensionality reduction methods are applied and the resulting karst probability models are evaluated relative to one another based on both quantitative and qualitative criteria. Comparing the preferred model, using quadratic discriminant analysis, to previously available seismic object detection workflows demonstrates the karst-specific nature of the tool. Results suggest that the karst multi-attribute workflow presented is capable of approximating the structural boundaries of karst systems with more accuracy and efficiency than a human counterpart or previously presented seismic interpretation schemes. This objective technique, using solely three-dimensional seismic reflection data, likely represents the most practical approach to mapping karst systems for subsequent hydrogeological modeling.
- Digital Object Identifier: 10.1190/int-2017-0197.1
- Source: USGS Publications Warehouse (indexId: 70198145)