AresWave: estimation of marsquake source parameters by waveform fitting with stochastic optimization

Introduction: Marsquake source parameter estimation is limited by the single-station geometry of the interior exploration using seismic investigations, geodesy and heat transport (InSight) mission, which makes hypocentral depth and focal mechanism difficult to resolve from arrival times alone. Materials and methods: We present AresWave, a Python package that integrates seismic analysis code (SAC) data format preprocessing, moment tensor calculation, DSMpy synthetic waveform generation, Bayesian depth inference from S–P differential times, and particle swarm optimization (PSO) waveform fitting. Results: Applied to S0173a, S0185a, and S0235b, the Bayesian module yielded depth intervals that constrained the PSO search. The best-fitting PSO depths were 10.00 km, 30.37 km, and 19.53 km, respectively, and fully contained the Bayesian optima. The waveform comparisons reproduced P arrivals well and provided acceptable S-phase matches despite the instability of Martian S waves. The recovered mechanisms are broadly consistent with mapped fault systems and previous source parameter studies. Conclusions: AresWave provides a reproducible workflow for estimating geologically consistent marsquake source parameters under single-station conditions. By combining probabilistic depth constraints with waveform-sensitive stochastic optimization, the package offers a practical framework for current InSight analyses and future planetary seismology applications.