Wisconsin Alumni Research Foundation

Pivotal Software, Inc.

Christopher Jeffrey Rawles

Kaushik K Das

Rashmi Raghu

Clifford H Thurber

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

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing database system processes seismic wave data using database queries. The seismic wave data is obtained from sensors of a reflection seismology survey. The seismic wave data is distributed among nodes of the parallel processing database system. Computations of processing the seismic wave data are performed using relational database queries that execute on the nodes in parallel. These computations performed by the queries include generating a starting Earth model of an area surveyed, and performing iterations of solving a forward problem of generating seismic wave patterns from the starting Earth model, performing local optimization that modifies the starting Earth model, and providing the modified Earth model as input to the forward problem in a next iteration.

Growing community of inventors

Novato, CA, United States of America

Christopher Jeffrey Rawles