Application of dynamic programming to the correlation of paleoclimate records

Signal matching is a powerful tool frequently used in paleoclimate research, but it is enormously time‐consuming when performed by hand. Previously proposed automatic correlation techniques require very good initial fits to find the correct alignment of two records. A new technique presented in this paper utilizes dynamic programming to find the globally optimal alignment of two records. Geological realism is instilled in the solution through the definition of penalty functions for undesirable behavior such as unlikely changes in accumulation rate. Examples with synthetic and real data demonstrate that the dynamic programming technique produces accurate, high‐resolution results with much less effort than hand tuning or preexisting automated correlation techniques.