North American Glacial Varve Project
Antevs (Automatic Numerical Time-series Evaluation of Varying Sequences) is a program for correlating glacial varve sequences, tree-rings, and similar time-series data. It was developed for the correlation of glacial varve sequences (Rayburn and Vollmer, 2013), but includes standard techniques for matching tree-ring sequences. The program uses Fourier analysis, cubic splines, and other curve fitting algorithms to remove trends and to normalize the series for comparison. The resulting series are correlated using cross-correlation techniques to identify potential matches. Editing capabilities allow series renumbering once an unknown series is correlated with a known chronology. Anteves can interface with the Velmex Unislide digital readout system, used for digitally recording tree-ring measurements.
Antevs is designed to be user-friendly with a simple graphical interface, and is used extensively in undergraduate student and other research projects in John Rayburn's varve and tree-ring laboratory at SUNY New Paltz. It can convert between various tree-ring and spreadsheet files, and includes an interface with the Velmex UniSlide digital readout used for tree-ring measurements.
Antevs is written by Frederick W. Vollmer. It is free software, but may not be redistributed or posted online without the author's permission. It may only be distributed as the released compressed file package with all included files. Any significant usage, such as a resulting presentation or publication, should include attribution to the program and author. This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. Please read the accompanying User Manual file for license and additional information.
Antevs is available for Macintosh, Windows, and Linux. Currently testing is done on OS X 10.6, 10.8, Windows XP, Windows 7, and Linux Ubuntu. Feedback, feature requests, and bug reports are appreciated to improve operation on various platforms.