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Stratigraphic correlation underpins all understanding of Earth’s history, yet few geoscientists have access to, or expertise in, numerical codes that can generate reproducible, optimal (in a least-squares framework) alignments between two stratigraphic time-series data sets. Here we introduce Align, a user-friendly computer app that makes accessible a published dynamic time warping (DTW) algorithm that, in a minute or less, catalogs a library of alignments between two time-series data sets by systematically exploring assumptions about the temporal overlap and relative sedimentation rates between the two stratigraphic sections. The Align app, written in the free, open-source R programming language, utilizes a graphical user interface (e.g., drop-down menus for data upload and sliding bars for parameter exploration) such that no coding is required. In addition to generating alignment libraries, a user can employ Align to visualize, explore, and cull each alignment library according to thresholds on Pearson’s correlation coefficient and/or temporal overlap. Here we demonstrate Align with time-series records of carbonate stable carbon isotope composition, though Align can, in principle, align any two quantitative stratigraphic time-series data sets.
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Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping
This paper investigates an ordered partial matching alignment problem, in which the goal is to align two sequences in the presence of potentially non-matching regions. We propose a novel parameter-free dynamic programming alignment method called hidden state time warping that allows an alignment path to switch between two different planes: a “visible” plane corresponding to matching sections and a “hidden” plane corresponding to non-matching sections. By defining two distinct planes, we can allow different types of time warping in each plane (e.g., imposing a maximum warping factor in matching regions while allowing completely unconstrained movements in non-matching regions). The resulting algorithm can determine the optimal continuous alignment path via dynamic programming, and the visible plane induces a (possibly) discontinuous alignment path containing matching regions. We show that this approach outperforms existing parameter-free methods on two different partial matching alignment problems involving speech and music.
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- Award ID(s):
- 1948531
- PAR ID:
- 10338591
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 12
- Issue:
- 8
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 3783
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app12083783
