eliasrh/DDSync: DDSync v0.1.0 — Initial public release

DDSync v0.1.0 Initial public release of DDSync, a graph-based synchronization / denoising tool for HypoDD/GrowClust-style dt.cc differential travel-time files. This is to accompandy submission of the corresponding manuscript (see below). Software live version: https://github.com/eliasrh/DDSync , please look at the live version main branch for the most up to date version. What's included MATLAB reference implementation (DDSync/) Julia implementation (DDSyncJulia/) designed for large catalogs (two-pass streaming; memory-light options) Method summary Per-(station, phase) weighted least-squares graph synchronization to estimate relative arrival-time potentials theta MAD-based pruning of internally inconsistent edges Optional IRLS (Huber) reweighting for heavy tails Optional thetaStd export (Hutchinson diag(inv(L)) with configurable probes; pseudo-std fallbacks available) Inputs / outputs Inputs: dt.cc + catalog.txt (event IDs must be contiguous 1..N)Outputs: dt_sync.cc, theta/theta_ _ .txt, thetastd/std_theta_ _ .txt, sync_metrics.txt Requirements MATLAB: tested on R2013b+ (no extra toolboxes required) Julia: tested on Julia 1.12.3; expected to work on Julia 1.9+ (no external packages; stdlib only) Quick start MATLAB: copy DDSync/ddsync_params_template.m → ddsync_params.m, edit paths, run run_ddsync Julia: copy DDSyncJulia/ddsync_config_template.toml → ddsync_config.toml, thenjulia DDSyncJulia/run_ddsync.jl ddsync_config.toml dt.cc catalog.txt Notes Hutchinson thetaStd estimation can be slow for high accuracy; see the warnings and config options in the READMEs. Authorship Software author: Elías Rafn Heimisson License Non-commercial research/educational use only. Commercial / for-profit use requires a separate license agreement. Contact: eliasrafn@hi.is Citation A Zenodo DOI will be minted automatically for this release tag. Please cite the Zenodo archive for v0.1.0. PLease reference: Elías Rafn Heimisson, Yifan Yu; DDSync: Graph‐Based Denoising of Differential Travel‐Time Observations with Applications to Pick Reconstruction and Path‐Difference Tomography. Seismological Research Letters 2026; doi: https://doi.org/10.1785/0220260086