This is a fairly new project, and pyCoreRelator did not exist before last December. We are just at the phase of manuscript(s) prep. More analyses were done for the remaining cores that don't have direct age constraints. So lots to compile!

Please stay tuned! Yet discussions are welcome anytime

not at the moment, but the current plan is to open source a polished version for general uses upon publication.

We do not attempt to say previous suggested correlation is entirely impossible. Instead we provide new method that everyone can reproduce exactly the same suite of optimal correlation solutions compatible with known age dates for each core pair, w/ metrics to show how "good/bad" these solutions are.

In short, only a small # of core pairs we’ve investigated can yielded correlation quality statistically better than coincidence. Most cores proximal to fans can't confidently yield synchronous bed-to-bed correlation based on available constraints.

This ongoing research is a collaborative effort, guided and supported by @zzsylvester.bsky.social and Jacob Covault from the @clasticslab.bsky.social at the Bureau of Economic Geology @txgeosciences.bsky.social, Joan Gomberg of the USGS, and Nora Nieminski of the AK DGGS.

𝒑𝒚𝑪𝒐𝒓𝒆𝑹𝒆𝒍𝒂𝒕𝒐𝒓 is designed to search out ALL plausible correlation solutions between core pairs, honoring known stratigraphic constraints (e.g., age, marker bed). It uses the Dynamic Time Warping algorithm to auto-align data and calculate signal similarity metrics.

All modules & Landlab utilities come with example Jupyter Notebooks, runnable on Google Colab for free. We hope pyTopoComplexity enhances your research & teaching. Welcome any feedback!
github.com/GeoLarryLai/...

Our ESurf paper features a case study integrating field age controls with pyTopoComplexity to assess erosivity parameters, hillslope diffusivity (D) & channel erodibility (K), in a non-equalibrium landform and discuss their scale dependency. 🧵2/3
doi.org/10.5194/esur...