Package: noisepy-seis
About NoisePyNoisePy is a Python package designed for fast and easy computation of ambient noise cross-correlation functions. It provides additional functionality for noise monitoring and surface wave dispersion analysis.Disclaimer: this code should not be used "as-is" and not run like a blackbox. The user is expected to change local paths and parameters. Submit an issue to github with information such as the scripts+error messages to debug.Detailed documentation can be found athttps://noisepy.github.io/NoisePy/Major updates comingNoisePy is going through a major refactoring to make this package easier to develop and deploy. Submit an issue, fork the repository and create pull requests tocontribute.InstallationThe nature of NoisePy being composed of python scripts allows flexible package installation, which is essentially to build dependent libraries the scripts and related functions live upon. We recommend usingcondaorpipto install.Note the order of the command lines below mattersWith Conda and pip:condacreate-nnoisepypython=3.10pip
condaactivatenoisepy
pipinstallnoisepy-seisWith Conda and pip and MPI support:condacreate-nnoisepypython=3.10pip
condaactivatenoisepy
condainstall-cconda-forgeopenmpi
pipinstallnoisepy-seis[mpi]With virtual environment:python-mvenvnoisepysourcenoisepy/bin/activate
pipinstallnoisepy-seisWith virtual environment and MPI support:An MPI installation is required. E.g. for macOS usingbrew:brewinstallopen-mpipython-mvenvnoisepysourcenoisepy/bin/activate
pipinstallnoisepy-seis[mpi]FunctionalityHere is a list of features of the package:download continous noise data based:on webservices using obspy's core functions ofget_stationandget_waveformson AWS S3 bucket calls, with a test on the SCEDC AWS Open Dataset.save seismic data inASDFformat, which convinently assembles meta, wavefrom and auxililary data into one single file (Tutorialson reading/writing ASDF files)offers scripts to precondition data sets before cross correlations. This involves working with gappy data from various formats (SAC/miniSEED) and storing it on local in ASDF.performs fast and easy cross-correlation with functionality to run in parallel throughMPIApplications module:Ambient noise monitoring: measure dv/v using a wide variety of techniques in time, fourier, and wavelet domain (Yuan et al., 2021)Surface wave dispersion: construct dispersion images using conventional techniques.UsageTo run the code on a single core, open the terminal and activate the noisepy environment before run following commands. To run on institutional clusters, see installation notes for individual packages on the module list of the cluster.Deploy using DockerWe use I/O on disk, so users need root access to the file system. To install rootless docker, see instructionshere.dockerpullghcr.io/noisepy/noisepy:latest
dockerrun-v~/tmp:/tmpghcr.io/noisepy/noisepy:latestcross_correlate--path/tmpTutorialsA short tutorial on how to use NoisePy-seis can be is available as aweb pageorJupyter notebookand can berun directly in Colab.This tutorial presents one simple example of how NoisePy might work! We strongly encourage you to download the NoisePy package and play it on your own! If you have any  comments and/or suggestions during running the codes, please do not hesitate to contact us through email or open an issue in this github page!Chengxin Jiang (chengxinjiang@gmail.com)
Marine Denolle (mdenolle@uw.edu).TaxonomyTaxonomy of the NoisePy variables.stationrefers to the site that has the seismic instruments that records ground shaking.channelrefers to the direction of ground motion investigated for 3 component seismometers. For DAS project, it may refers to the single channel sensors.istais the index name for looping over stationscc_lencorrelation length, basic window length in secondsstepis the window that get skipped when sliding windows in secondssmooth_Nnumber of points for smoothing the  time or frequency domain discrete arrays.maxlagmaximum length in seconds saved in files in each side of the correlation (save on storage)substack,substack_lenboolean, window length over which to substack the correlation (to save storage or do monitoring), it has to be a multiple ofcc_len.time_chunk, nchunkrefers to the time unit that defined a single job. for instace,cc_lenis the correlation length (e.g., 1 hour, 30 min), the overall duration of the experiment is the total length (1 month, 1 year, ...). The time chunk could be 1 day: the code would loop through each cc_len window in a for loop. But each day will be sent as a thread.AcknowledgementsContributingThanks to our contributors so far!Use this reference when publishing on your work with noisepyMain code:Zenodo DOI:noisepy/NoisePyJiang, C. and Denolle, M.NoisePy: a new high-performance python tool for seismic ambient noise seismology.Seismological Research Letter91, no. 3 (2020): 1853–1866.https://doi.org/10.1785/0220190364Algorithms used:(data pre-processing) Seats, K. J., Jesse F. L., and German A. P.Improved ambient noise correlation functions using Welch′ s method.Geophysical Journal International188, no. 2 (2012): 513-523.https://doi.org/10.1111/j.1365-246X.2011.05263.x(dv/v in wavelet domain) Yuan, C., Bryan, J. T., and Denolle, M.Numerical comparison of time-, frequency- and wavelet-domain methods for coda wave interferometry.Geophysical Journal International226, no. 2 (2021): 828-846.https://doi.org/10.1093/gji/ggab140(optimal stacking) Yang X, Bryan J, Okubo K, Jiang C, Clements T, Denolle MA.Optimal stacking of noise cross-correlation functions/Geophysical Journal International. 2023 Mar;232(3):1600-18.https://doi.org/10.1093/gji/ggac410This research received software engineering support from the University of Washington’s Scientific Software Engineering Center (SSEC) supported by Schmidt Futures, as part of the Virtual Institute for Scientific Software (VISS). We would like to acknowledgeCarlos Garcia Jurado SuarezandNicholas Richfor their collaboration and contributions to the software.