seismic.traveltime package¶
Submodules¶
seismic.traveltime.cluster_grid module¶
- Description:
Define a 3D grid discretization of the Earth
- CreationDate:
2/09/2018
- Developer:
-
class
seismic.traveltime.cluster_grid.Grid2(ndis=2, param_file=None)[source]¶ Bases:
objectA non-uniform Grid Model of Earth for eventsource–>station rays clustering and sorting.
- Reference to the inversion Fortran code model parameter file param1x1 in this directory:
Global: 5x5 degree and a list of 1+16 depths
Region: 1x1 degree and a list of 1+29 depths
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find_block_number(lat, lon, z)[source]¶ find the index-number of an events/station in a non-uniform grid each spatial point (lat, lon, z) is mapped to a uniq block_number.
- Parameters
lat – latitude (-90,90)
lon – longitude (0,360)
z – depth meter
- Returns
a tuple of 4 block number AND and the block’s centre coordinate(int_block_n, xc,yc, zcm)
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generate_grid3D()[source]¶ Loop over all the reference depths, both regional and global depth list.
- Returns
pandas dataframe
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generate_latlong_grid(depthmeters=0.0)[source]¶ create a csv file containing: (block_number, lat,long, depthm=0, xc,yc,zc)
- Returns
Pandas data frame
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get_depth_index(z, dep_meters)[source]¶ given a point with depth z meters, and a np-array of refined depth in meters, find the index which z fall into.
- Parameters
z – an event depth in meters
dep_meters – an array of numbers corresponding to a refined depth discretization.
- Returns
depth index and the cell block centre depth in metres.
seismic.traveltime.events_stations_rays_visualization2 module¶
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seismic.traveltime.events_stations_rays_visualization2.plot1(mypdf)[source]¶ - Parameters
mypdf – which dataframe to view in the following??
- Returns
-
seismic.traveltime.events_stations_rays_visualization2.plot_geopandas(mypdf)[source]¶ Geopandas and Maps :param mypdf: :return:
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seismic.traveltime.events_stations_rays_visualization2.plot_gmt(mypdf)[source]¶ GMT-python plotting map figure saved into PNG file. But not displaying on screen
Require to conda install gmt-python module in anaconda python gmt version-6 will be installed. :param mypdf: :return:
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seismic.traveltime.events_stations_rays_visualization2.plot_rays(mypdf)[source]¶ - Parameters
mypdf –
- Returns
seismic.traveltime.gather_events module¶
Parse multiple events xml files to gather all seismic events-arrivals (Refactored From the original seismic.cluster.cluster.py)
output CSV files containing the following columns:
['source_block', 'station_block', 'residual', 'event_number',
SOURCE_LONGITUDE, SOURCE_LATITUDE, 'source_depth', STATION_LONGITUDE, STATION_LATITUDE,
'observed_tt', 'calculated_locations2degrees', xml_distance, STATION_CODE, 'SNR', 'P_or_S']
How to Run:
export ELLIPCORR=/g/data1a/ha3/fxz547/Githubz/passive-seismic/ellip-corr/
cd passive-seismic/tempworks
# python ../seismic/traveltime/gather_events.py -v DEBUG gather /g/data/ha3/fxz547/Githubz/passive-seismic/testdata/
# python ../seismic/traveltime/gather_events.py -v DEBUG gather /g/data/ha3/fxz547/Githubz/passive-seismic/some_events_xml/
# OR
#<fzhang@ubuntu16qos>/Softlab/Githubz/passive-seismic/tempworks (master)
# $ python2 ../seismic/traveltime/gather_events.py -v DEBUG gather ./events_xmls_test
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class
seismic.traveltime.gather_events.ArrivalWriter(rank, wave_type, output_file)[source]¶ Bases:
objectConvenience class for writing arrival data
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seismic.traveltime.gather_events.getSNR(arrival)[source]¶ From the arrival get the SNR value. This algorithm depend on how the snr value is coded in the xml file :param arrival: :return: a float SNR value
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seismic.traveltime.gather_events.get_paths_from_csv(csvfile)[source]¶ Parse a text/csv file to extract a list of paths, where events xml files are stored, to be gathered.
- Parameters
csvfile – csv file
- Returns
list_of_paths
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seismic.traveltime.gather_events.process_event(event, stations, grid, wave_type, counter)[source]¶ - Parameters
event – obspy.core.event.Event class instance
stations – dict stations dict
grid – can be None; Grid class instance
wave_type – str Wave type to generate inversion inputs. See gather function.
counter – int event counter in this process
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seismic.traveltime.gather_events.process_many_events(event_xmls, grid, stations, wave_type, output_file, seed=1)[source]¶
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seismic.traveltime.gather_events.recursive_glob(dirname, ext='*.xml')[source]¶ Under the dirname recursively find all files with extension ext. Return a list of the full-path to the files of interest.
See: https://stackoverflow.com/a/2186565/3321542
- Parameters
dirname – a single dir OR a list of dirs.
ext – eg, “.xml”
- Returns
a list of path2files
seismic.traveltime.mpiops module¶
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seismic.traveltime.mpiops.array_split(arr, process=None)[source]¶ Convenience function for splitting array elements across MPI processes
- Parameters
arr (ndarray) – Numpy array
process (int) – Process for which array members are required. If None, MPI.comm.rank is used instead. (optional)
:return List corresponding to array members in a process. :rtype: list
seismic.traveltime.parametrisation module¶
Class for parsing Alexei’s tomographic parametrisation file as an object.
Created on Wed Apr 03 13:52:28 2019
@author: Marcus W. Haynes
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class
seismic.traveltime.parametrisation.grid(in_file='param')[source]¶ Bases:
object-
extract_1D(lon, lat, values, ref=None, method='linear')[source]¶ Extracts arbitrary 1D vertical seismic velocity profiles from an inversion model.
- Input::
lon - float or list of desired longitude locations.
lat - float or list of desired latitude locations.
values - array of inversion values corresponding to the grid class.
ref [optional] - the reference model to convert velocity perturbations to absolute values, if desired. Needs to be a 2D array with depth in first column and velocity in second column.
method [optional] - method used to interpolate values to location
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seismic.traveltime.plotviews module¶
seismic.traveltime.pslog module¶
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seismic.traveltime.pslog.warn_with_traceback(message, category, filename, lineno, line=None)[source]¶ copied from: http://stackoverflow.com/questions/22373927/get-traceback-of-warnings
seismic.traveltime.sort_rays module¶
- Description:
Read in event-arrival seismic rays and sort them according to a discretization model of Earth.
The input file header assumed to be:
col_names=[‘source_block’, ‘station_block’, ‘residual’, ‘event_number’,’source_longitude’,’source_latitude’,’source_depth’, ‘station_longitude’,’station_latitude’, ‘observed_tt’, ‘locations2degrees’, ‘station_code’,’SNR’, ‘P_or_S’]
The output CSV file will be feed into an inversion program which will derive travel-time tomography images.
- Developer:
-
seismic.traveltime.sort_rays.apply_filters(csv_data, phase)[source]¶ Apply filters to the rows (rays) according to phase P or S. Remove un-reliable data. :param csv_data: input pandas dataframe :param phase: P or S :return: pandas dataframe
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seismic.traveltime.sort_rays.compute_ellipticity_corr(arrival_phase, ev_latitude, ev_longitude, ev_depth_km, sta_latitude, sta_longitude, degrees_to_source)[source]¶ Utility function to compute ellipticity correction.
- Parameters
arrival_phase – P or S
ev_latitude – event lat
ev_longitude – event long
ev_depth_km – event depth in km
sta_latitude – station lat
sta_longitude – station long
degrees_to_source – degree to source
- Returns
ellipticity correction float value
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seismic.traveltime.sort_rays.filter_S_by_P(inpdf, pevents_csv)[source]¶ Filter the S picks by P events :param inpdf: input pdf :param pevents_csv: Pevents csv file with 3 columns: [‘#eventID’, ‘net’,’sta’] :return: a new csv pdf after the filtering
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seismic.traveltime.sort_rays.is_ray_in(ray_id, P_RAYS_ID_LIST)[source]¶ check if an ray_id is in the P_RAYS_ID_LIST :param ray_id: “net_sta_evtid” :param P_RAYS_ID_LIST: a sequence of Ray_id of P waves. :return: 1 if in; 0 if not in
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seismic.traveltime.sort_rays.sort(input_file, sorted_file, residual_cutoff)[source]¶ Sort and filter the arrivals based on the source and station block number. There are two stages of filtering:
Filter based on the arrival time residual value: defult value for the cutoff is 5 for P-Wave, 10 for S-Wave
Filter based on median of observed travel time.
If there are multiple source and station block combinations, we keep the row corresponding to the median observed travel time (observed_tt).
- Parameters
input_file – output file from the gather stage (eg, outfile_P.csv)
sorted_file – str, optional optional sorted output file path. Default: sorted.csv.
residual_cutoff – float residual seconds, arrivals are rejected if the residual is larger than the cutoff
- Returns
A pandas df
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seismic.traveltime.sort_rays.sort2(input_file, sorted_file, residual_cutoff)[source]¶ Sort and filter the arrivals based on the source and station block number. If there are multiple source and station block combinations, the row corresponding to the highest SNR value is kept. (This function is for experiment only, not used.)
- Parameters
input_file – output file from the gather stage (eg, outfile_P.csv)
sorted_file – str, optionaloptional sorted output file path. Default: sorted.csv.
residual_cutoff – float arrivals are rejected if the residual is larger than the cutoff.
- Returns
pandas_df
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seismic.traveltime.sort_rays.sort_csv_in_grid(inputcsv, outputcsv, phase, mygrid, column_name_map)[source]¶ Read in a csv file, re-grid each row according to a given Grid model. Write into output csv file with re-calculated block_numbers re-named columns
- Parameters
inputcsv – path to an input csv file
outputcsv – path to output file
phase – P or S
mygrid – instance of Earth Grid model
column_name_map – column map dictionary as in csv_columns.json file
- Returns
outfile
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seismic.traveltime.sort_rays.translate_csv(in_csvfile, out_csvfile)[source]¶ Read in a csv file, re-grid each row according to a new Grid model. Write into another csv file with re-calculated block_numbers and six new columns of Grid cell centers.
- Parameters
in_csvfile – path to an input csv file
out_csvfile – path to an output csv file
- Returns
out_csvfile