Martian deep interior revealed by coda-wave autocorrelation of the marsquake S1222a [Code and Dataset]

Code for "Martian deep interior revealed by coda-wave autocorrelation of the marsquake S1222a" This repository contains the Python codes used in the study: "Martian deep interior revealed by coda-wave autocorrelation of the marsquake S1222a" The codes reproduce the main figures presented in the manuscript. A total of nine Python scripts are provided, corresponding to Figures 1–9 of the paper, respectively. Each script performs a specific part of the data processing, analysis, or visualization workflow: Script 1: Generates the Martian map and illustrates the relevant seismic locations and study regions used in the manuscript. Script 2: Produces the frequency spectra of the vertical-component waveform of the marsquake S1222a. Script 3: Performs parameter-sensitivity tests for coda-wave autocorrelation (ACF), evaluating the robustness of the identified autocorrelation features with respect to processing parameters. Script 4: Performs time-window sensitivity tests for the ACF analysis by examining the effects of different coda-wave window lengths. Script 5: Conducts incident-azimuth scanning of the coda waves to investigate the azimuthal dependence of coda waves. Script 6: Performs background-noise tests to assess whether the identified ACF arrivals can be attributed to environmental or instrumental noise. Script 7: Tests the influence of instrumental glitches on the ACF results and evaluates the robustness of the identified signals against potential data artifacts. Script 8: Performs travel-time matching between observed ACF arrivals and predicted seismic reflection phases. Script 9: Converts seismic travel times into depths to constrain the locations of major Martian interior discontinuities. Together, these scripts provide the complete computational workflow used to generate the main results and figures presented in the manuscript. The codes implement the data processing, analysis, and visualization procedures used in this study, including coda-wave autocorrelation analysis, seismic signal processing, travel-time analysis, and figure generation. The scripts are written in Python and require several scientific computing packages. The required libraries may vary depending on the local computing environment, but commonly used packages include: matplotlib, numpy, scipy, obspy, pandasand other standard Python scientific libraries. Users may need to install additional dependencies according to their specific Python configuration. This research is supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie 2025 Postdoctoral Fellowship Grant Number 101270264 (SeisEchoPlanets). If you use these codes in your research, please cite this repository and the associated publication. Thank you. For questions or further information, please contact: Jing ShiEmail: [e-mail]