Multivariate Analysis of Seismic Field Data
This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present data sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM
- Sponsoring Organization:
- USDOE Office of Defense Programs (DP)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 8993
- Report Number(s):
- SAND99-1505
- Country of Publication:
- United States
- Language:
- English
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