The current lack of a robust standardized technique for geophysical mapping of karst systems can be attributed to 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. The application of neural networks (NNs) to multiattribute seismic interpretation can provide a semiautomated method for identifying and leveraging the nonlinear relationships exhibited among seismic attributes. The ambiguity generally associated with designing NNs 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 suggested number of training examples, provides a more objective and computationally efficient method for identifying karst systems using reflection seismology. This statistically optimized NN technique is demonstrated using 3D 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 quantitative and qualitative criteria. Comparing the preferred model, using quadratic discriminant analysis, with previously available seismic object detection workflows demonstrates the karst-specific nature of the tool. Results suggest that the karst multiattribute 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 3D seismic reflection data, is proposed as a practical approach to mapping karst systems for subsequent hydrogeologic modeling.

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