Wisconsin Alumni Research Foundation

Clifford H Thurber

Christopher Jeffrey Rawles

Kurt L Feigl

Systems and methods for automatically detecting and identifying seismic wave features in seismic data are provided. In general, the systems and methods utilize a nonparametric time series classification method to detect seismic wave features that may otherwise be difficult to automatically identify in seismic data. Instead of building a model by estimating parameters from the seismic data, the data itself is used to define a trained model. The systems and methods described here provide the ability to detect and identify seismic wave features with reasonably fast and extremely accurate results without needing to compute parameters.

Systems and methods for non-parametric autopicking of seismic wave features from seismic data

A method and apparatus have been developed to model wrapped phase data directly. An average statistical cost measures the misfit between the observed phase value modeled phase value. A set of acceptable values for the parameters in a parametric expression characterizing the wrapped phase is found by searching for an average statistical cost below a threshold. In the context of interferometry, the parameter values can then be used to determine the impact of a geophysical event on a geographic area. In the context of a global navigation satellite system (GNSS), such as the Global Positioning System (GPS), the values can be used to determine the position of a GNSS receiver.

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Madison, WI, United States of America

Clifford H Thurber