|Title||Inverse spatial principle component analysis for geophysical survey data interpolation|
|Author||Li, Q; Dehler, S|
|Source||Journal of Applied Geophysics vol. 115, 2015 p. 79-91|
|Alt Series||Earth Sciences Sector, Contribution Series 20140281|
|Media||paper; on-line; digital|
|Subjects||mapping techniques; interpolation; inverse spatial principal component analysis; filtering methods; singular spectral analysis; airborne geophysics|
|Illustrations||graphs; plots; magnetic anomaly maps; geological sketch maps|
|Program||Geoscience for New Energy Supply (GNES) - Program Corrdination, Geoscience for New Energy Supply (GNES)|
|Abstract||The starting point for data processing, visualization, and overlay with other data sources in geological applications often involves building a regular grid by interpolation of geophysicalmeasurements.
Typically, the sampling interval along survey lines ismuch higher than the spacing between survey lines because the geophysical recording|
systemis able to operate with a high sampling rate, while the costs and slower speeds associated with
operational platforms limit line spacing. However, currently available interpolating methods often smooth data observed with higher sampling rate along a survey line to accommodate the lower spacing across lines, and much of the higher resolution
information is not captured in the interpolation process. In this approach, amethod termed as the inverse spatial principal component analysis (isPCA) is developed to address this problem. In the isPCAmethod, a whole profile observation as well as
its line position is handled as an entity and a survey collection of line entities is analyzed for interpolation. To test its performance, the developed isPCA method is used to process a simulated airborne magnetic survey from an existing magnetic
grid offshore the Atlantic coast of Canada. The interpolation results using the isPCA method and othermethods are comparedwith the original survey grid. It is demonstrated that the isPCA method outperforms the Inverse Distance Weighting (IDW),
Kriging (Geostatistical), and MINimum Curvature (MINC) interpolation methods in retaining detailed anomaly structures and restoring original values. In a second test, a high resolution magnetic survey offshore Cape Breton, Nova Scotia, Canada, was
processed and the results are compared with other geological information. This example demonstrates the effective performance of the isPCA method in basin structure identification.
|Summary||(Plain Language Summary, not published)|
A new interpolation method termed inverse spatial principal component analysis (isPCA) is developed in this paper for geophysical data interpolation and
airborne geophysical prospecting in particular. It is demonstrated that isPCA has better performance than standard methods in preserving anomalies and creating less distortion of the true values. isPCA has also been successfully used in processing a
high resolution magnetic survey on the east coast of Canada. isPCA has strong potential usages in other areas, such as satellite and multimedia interpolation.