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Patagonia ambient noise tomography ######################################################################## do0_get_ENAP_mseed.py get ENAP files from /DB/, clean ANMA bad timing days, resample to 1Hz, and write day files do0_make_alt_response_file.py read stationxml files for each network, pull stations that are used here, write a single xml file and a single dataless file with all station info combined do1_cat_and_convert_mseed.py list dates that we have data for, all networks, and cat into single-day, all-station miniseed files (pulling from ENAP and IRIS downloads). Call rdseed with combined dataless file to make seed for seed2cor. do2_generate_seedlist.py make a list of seedfiles to run through seed2cor do2_generate_station_list.py make a list of stations and their coordinates for seed2cor. *note* lat/lon order changed when version of seed2cor changed. do3_cor.sh RUN seed2cor with hardcorded number of threads do4_stack_cor.py read sac files from seed2cor, stack monthly cross-correlations into one stack per station pair. Do the same for overlapping month triplets for later std calc. do5_symmetrize_cor.py symmetrize stacked cross correlations (for SNR calc) do6_calc_snr.sh run the spectral_snr_s2c code to get SNR for full xcorr and 3-month xcorr do7_ftan_input_list.py Write list input file for aftan: pulls filenames and uses snr for period bounds, the rest is basically hard-coded do8_aftan.sh run aftan **do9_make_disp_curves.py [** -> run locally, not on server] read aftan output and make/pickle DispersionCurve python objects **do10_params_tomo.py test tomo parameters **do10_jackknife_stations.py run tomography with one station excluded in each pass **do10_tomo_npass.py actually run tomography with 3-pass, with one set of parameters, and pickle vmaps ak135_phvel.dat disp curve reference for aftan (hard-coded name/path) ################################################################# plot_aftan.py plot dispersion curves and amplitude maps from aftan outputs [note that station coord format may be messed up for mapping; easy to fix] plot_lcurves.py plot some of the L-curve stuff in a 3D scatter. Doesn't actually do much but can be a reminder of relevant commands for parsing that info. station_disp_curve.py read EQ and ANT maps, get dispersion curves at each station location and plot compare_ant_eq.py plot EQ and ANT tomo maps side by side, on the same velocity scale, to see just how far off they are :( ################################################################# seed/ miniseed files with all the input data 1P/ and YJ/ are all the IRIS stuff (unpacked but not organized) ENAP/ is the resampled miniseed files pulled from /DB/ dayfiles/ is the catted single-day, all-station files used for seed2cor (both miniseed and fullseed) datalesss/ is the dataless seed headers for ALL stations, all networks ################################################################# requests/ Data_request_*.py script to automate breq_fast requests to IRIS there are two scripts because one was used for the discrete 1P and YJ periods and the other dealt with the longer-term permanent network data For later: when more 1P data is available, you'll want _1_ decrypt_untar.sh use openssl key to decrypt the embargoed 1P data Note that openssl version matters in interesting ways just_untar.sh YJ data (and permanent stations on their own) are not embargoed, hence not encrypted northchile_sta.lst YJ stations, for breq_fast request script southchile_sta.lst 1P and permanent stations, for breq_fast request script permanent_sta.lst just the permanent stations, for breq_fast request script ################################################################# old/ bits and pieces, some pysismo stuff and some other stuff that's all no longer used
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