Stochastic dynamic time warping for automated stratigraphic correlation

Abstract

Implementations of the present disclosure provide techniques for stochastic stratigraphic correlation that produce quantitative estimation of uncertainty by generating multiple viable correlations. In contrast to deterministic models where the output of the model is fully determined by the parameter values and the initial conditions, stochastic models possess some inherent randomness. Using stochastic models, the same set of parameter values and initial conditions will lead to an ensemble of different outputs. The described techniques include introducing probabilistic sampling into dynamic time warping processes. In some implementations, the techniques allow for a user to interactively update correlation markers and their associated uncertainties. The techniques can adhere to these markers and produce multiple viable solutions.