Abstract

Modeling of seismic data takes two forms: those based on physical or geological (phenomenological) models and those that are data-driven (probabilistic) models. In the phenomenological approach, physical and geologic models are tied to seismic data either through geologic analogs or principles of structural deformation and sedimentary deposition. The results are then compared to the observed data, and the model is iterated as necessary to improve agreement. In contrast, probabilistic modeling looks at patterns in the data. The data could include raw seismic observations or seismic attributes. Probabilities can then be assigned to observations or potential observations; however, many common techniques such as neural networks and clustering do not explicitly take this step.

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