Principal Component Analysis of Seismic Data

Not all volume-based curvature datasets are created equal.

Principal Component Analysis

Geo-Texture’s PCA data conditioning makes all the difference.

Edge-preserving Principal Component processing is a multi-trace analysis of seismic data which avoids the common problem of other such techniques by preserving lateral breaks, rather than smoothing them out. The process results in reduced random (and sometimes coherent) noise, better reflector continuity and amplitudes, and more sharply defined event terminations and lateral breaks. Input is a conventional stacked (and migrated) 3-D seismic volume. Principal Component Analysis is the basis for the eigenvalue coherency technique, which is the premier coherency algorithm.

Curvature without PCA

Curvature after PCA Data Conditioning

After PCA Data Conditioning

Our Principal Component Analysis data conditioning removes noise and sharpens breaks, providing superior imaging.

Principal Component processing is too compute-intensive for workstation applications.