Composite Datastore Tutorial

Composite Datastore Tutorial#

Noisepy is a python software package to process ambient seismic noise cross correlations. This tutorial aims to introduce the use of noisepy for a toy problem on a composite data store. It can be ran locally or on the cloud.

The data is stored on AWS S3:

SCEDC Data Set: https://scedc.caltech.edu/data/getstarted-pds.html
NCEDC Data Set: https://ncedc.org/db/cloud/getstarted-pds.html

First, we install the noisepy-seis package

# Uncomment and run this line if the environment doesn't have noisepy already installed:
# ! pip install noisepy-seis 

Warning: NoisePy uses obspy as a core Python module to manipulate seismic data. If you use Google Colab, restart the runtime now for proper installation of obspy on Colab.

Import necessary modules#

Then we import the basic modules

%load_ext autoreload
%autoreload 2
from noisepy.seis import cross_correlate, stack_cross_correlations, __version__    # noisepy core functions
from noisepy.seis.io.asdfstore import ASDFCCStore, ASDFStackStore                  # Object to store ASDF data within noisepy
from noisepy.seis.io.compositerawstore import CompositeRawStore
from noisepy.seis.io.s3store import SCEDCS3DataStore, NCEDCS3DataStore
from noisepy.seis.io.channel_filter_store import channel_filter
from noisepy.seis.io.datatypes import Channel, CCMethod, ConfigParameters, FreqNorm, RmResp, StackMethod, TimeNorm        # Main configuration object
from noisepy.seis.io.channelcatalog import XMLStationChannelCatalog                # Required stationXML handling object
import os
import obspy
import shutil
from datetime import datetime, timezone
from datetimerange import DateTimeRange

from noisepy.seis.io.plotting_modules import plot_all_moveout

print(f"Using NoisePy version {__version__}")

path = "./composite_data" 

os.makedirs(path, exist_ok=True)
cc_data_path = os.path.join(path, "CCF")
stack_data_path = os.path.join(path, "STACK")
S3_STORAGE_OPTIONS = {"s3": {"anon": True}}

/opt/hostedtoolcache/Python/3.10.17/x64/lib/python3.10/site-packages/noisepy/seis/io/utils.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  from tqdm.autonotebook import tqdm

Using NoisePy version 0.1.dev1

We will work with a single day worth of data on SCEDC and NCEDC. The continuous data is organized with a single day and channel per miniseed. For this example, you can choose any year since 2002. We will just cross correlate a single day.

# SCEDC S3 bucket common URL characters for that day.
SCEDC_DATA = "s3://scedc-pds/continuous_waveforms/"
NCEDC_DATA = "s3://ncedc-pds/continuous_waveforms/NC/"

SCEDC_STATION_XML = "s3://scedc-pds/FDSNstationXML/CI/"  
NCEDC_STATION_XML = "s3://ncedc-pds/FDSNstationXML/NC/"

# timeframe for analysis
start = datetime(2012, 1, 1, tzinfo=timezone.utc)
end = datetime(2012, 1, 3, tzinfo=timezone.utc)
timerange = DateTimeRange(start, end)
print(timerange)

2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000

Ambient Noise Project Configuration#

We prepare the configuration of the workflow by declaring and storing parameters into the ConfigParameters() object and/or editing the config.yml file.

# Initialize ambient noise workflow configuration
config = ConfigParameters() # default config parameters which can be customized

Customize the job parameters below:

config.start_date = start
config.end_date = end
config.acorr_only = False # only perform auto-correlation or not
config.xcorr_only = True # only perform cross-correlation or not

config.inc_hours = 24 # INC_HOURS is used in hours (integer) as the 
        #chunk of time that the paralelliztion will work.
        # data will be loaded in memory, so reduce memory with smaller 
        # inc_hours if there are over 400+ stations.
        # At regional scale for SCEDC, we can afford 20Hz data and inc_hour 
        # being a day of data.

# pre-processing parameters
config.sampling_rate = 20  # (int) Sampling rate in Hz of desired processing (it can be different than the data sampling rate)
config.single_freq = False
config.cc_len = 3600  # (float) basic unit of data length for fft (sec)
config.step = 1800.0  # (float) overlapping between each cc_len (sec)

config.ncomp = 3  # 1 or 3 component data (needed to decide whether do rotation)

config.stationxml = False  # station.XML file used to remove instrument response for SAC/miniseed data
      # If True, the stationXML file is assumed to be provided.
config.rm_resp = RmResp.INV  # select 'no' to not remove response and use 'inv' if you use the stationXML,'spectrum',

############## NOISE PRE-PROCESSING ##################
config.freqmin, config.freqmax = 0.05, 2.0  # broad band filtering of the data before cross correlation
config.max_over_std  = 10  # threshold to remove window of bad signals: set it to 10*9 if prefer not to remove them

################### SPECTRAL NORMALIZATION ############
config.freq_norm = FreqNorm.RMA  # choose between "rma" for a soft whitening or "no" for no whitening. Pure whitening is not implemented correctly at this point.
config.smoothspect_N = 10  # moving window length to smooth spectrum amplitude (points)
    # here, choose smoothspect_N for the case of a strict whitening (e.g., phase_only)

#################### TEMPORAL NORMALIZATION ##########
config.time_norm = TimeNorm.ONE_BIT # 'no' for no normalization, or 'rma', 'one_bit' for normalization in time domain,
config.smooth_N = 10  # moving window length for time domain normalization if selected (points)

############ cross correlation ##############
config.cc_method = CCMethod.XCORR # 'xcorr' for pure cross correlation OR 'deconv' for deconvolution;
    # FOR "COHERENCY" PLEASE set freq_norm to "rma", time_norm to "no" and cc_method to "xcorr"

# OUTPUTS:
config.substack = True  # True = smaller stacks within the time chunk. False: it will stack over inc_hours
config.substack_windows = 1  # how long to stack over (for monitoring purpose)
    # if substack=True, substack_windows=2, then you pre-stack every 2 correlation windows.
    # for instance: substack=True, substack_windows=1 means that you keep ALL of the correlations
    # if substack=False, the cross correlation will be stacked over the inc_hour window

### For monitoring applications ####
## we recommend substacking ever 2-4 cross correlations and storing the substacks
# e.g. 
# config.substack = True 
# config.substack_windows = 4

config.maxlag = 200  # lags of cross-correlation to save (sec)

# the network list that are used here
config.networks = ['CI', 'NC']

# For this tutorial make sure the previous run is empty
#os.system(f"rm -rf {cc_data_path}")
if os.path.exists(cc_data_path):
    shutil.rmtree(cc_data_path)

Step 1: Cross-correlation#

In this instance, we read directly the data from the scedc bucket into the cross correlation code. We are not attempting to recreate a data store. Therefore we go straight to step 1 of the cross correlations.

We first declare the data and cross correlation stores

scedc_catalog = XMLStationChannelCatalog(SCEDC_STATION_XML, 
                                         storage_options=S3_STORAGE_OPTIONS)
ncedc_catalog = XMLStationChannelCatalog(NCEDC_STATION_XML, "{network}.{name}.xml", 
                                         storage_options=S3_STORAGE_OPTIONS)

scedc_store = SCEDCS3DataStore(SCEDC_DATA, scedc_catalog,  
                               channel_filter(["CI"], ["VES", "SVD", "BBR"], ["BH?", "HH?"]), 
                               timerange, storage_options=S3_STORAGE_OPTIONS)

ncedc_store = NCEDCS3DataStore(NCEDC_DATA, ncedc_catalog, 
                               channel_filter(["NC"], ["BBGB", "AFD", "GDXB"], ["BH?", "HH?"]), 
                               timerange, storage_options=S3_STORAGE_OPTIONS)

raw_store = CompositeRawStore({"CI": scedc_store, 
                               "NC": ncedc_store}) # Composite store for reading data from both SCEDC and NCEDC
cc_store = ASDFCCStore(cc_data_path)               # Store for writing CC data

get the time range of the data in the data store inventory

span = raw_store.get_timespans()
print(span)

[2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000, 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000]

Get the channels available during a given time spane

channel_list=raw_store.get_channels(span[1])
print(channel_list)

2025-04-17 02:00:13,036 140367161154432 INFO utils.log_raw(): TIMING:  0.769 secs for Listing 1731 files from s3://scedc-pds/continuous_waveforms/2012/2012_002/

2025-04-17 02:00:13,072 140367161154432 INFO utils.log_raw(): TIMING:  0.036 secs for Init: 1 timespans and 18 channels

2025-04-17 02:00:13,246 140365663307456 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://scedc-pds/FDSNstationXML/CI/CI_BBR.xml

2025-04-17 02:00:13,451 140365568935616 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://scedc-pds/FDSNstationXML/CI/CI_VES.xml

2025-04-17 02:00:13,469 140365550061248 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://scedc-pds/FDSNstationXML/CI/CI_SVD.xml

2025-04-17 02:00:17,349 140367161154432 INFO s3store.get_channels(): Getting 18 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-04-17 02:00:17,607 140367161154432 INFO utils.log_raw(): TIMING:  0.230 secs for Listing 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.002/

2025-04-17 02:00:17,623 140367161154432 INFO utils.log_raw(): TIMING:  0.017 secs for Init: 1 timespans and 9 channels

2025-04-17 02:00:17,688 140365568935616 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.BBGB.xml

2025-04-17 02:00:17,723 140365550061248 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.GDXB.xml

2025-04-17 02:00:17,761 140365355026112 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.AFD.xml

2025-04-17 02:00:18,128 140367161154432 INFO s3store.get_channels(): Getting 9 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

[CI.BBR.BHE, CI.BBR.BHN, CI.BBR.BHZ, CI.BBR.HHE, CI.BBR.HHN, CI.BBR.HHZ, CI.SVD.BHE, CI.SVD.BHN, CI.SVD.BHZ, CI.SVD.HHE, CI.SVD.HHN, CI.SVD.HHZ, CI.VES.BHE, CI.VES.BHN, CI.VES.BHZ, CI.VES.HHE, CI.VES.HHN, CI.VES.HHZ, NC.AFD.HHE, NC.AFD.HHN, NC.AFD.HHZ, NC.BBGB.HHE, NC.BBGB.HHN, NC.BBGB.HHZ, NC.GDXB.HHE, NC.GDXB.HHN, NC.GDXB.HHZ]

Perform the cross correlation#

The data will be pulled from SCEDC & NCEDC, cross correlated, and stored locally if this notebook is ran locally. If you are re-calculating, we recommend to clear the old cc_store.

# we also define a channel pair filter that limits cross-correlation to stations pairs closer than 600 km
def pair_filter(src: Channel, rec: Channel) -> bool:
    latS = src.station.lat
    lonS = src.station.lon
    latR = rec.station.lat
    lonR = rec.station.lon
    dist, _, _ = obspy.geodetics.base.gps2dist_azimuth(latS, lonS, latR, lonR)
    dist /= 1e3 # to km
    if dist <= 600:
        return True
    else:
        return False

cross_correlate(raw_store, config, cc_store, pair_filter=pair_filter)

2025-04-17 02:00:18,179 140367161154432 INFO correlate.cross_correlate(): Starting Cross-Correlation with 4 cores

2025-04-17 02:00:18,767 140367161154432 INFO utils.log_raw(): TIMING:  0.588 secs for Listing 1731 files from s3://scedc-pds/continuous_waveforms/2012/2012_001/

2025-04-17 02:00:18,802 140367161154432 INFO utils.log_raw(): TIMING:  0.035 secs for Init: 2 timespans and 36 channels

2025-04-17 02:00:18,804 140367161154432 INFO s3store.get_channels(): Getting 18 channels for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2025-04-17 02:00:18,981 140367161154432 INFO utils.log_raw(): TIMING:  0.170 secs for Listing 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.001/

2025-04-17 02:00:18,998 140367161154432 INFO utils.log_raw(): TIMING:  0.017 secs for Init: 2 timespans and 18 channels

2025-04-17 02:00:18,999 140367161154432 INFO s3store.get_channels(): Getting 9 channels for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2025-04-17 02:00:19,002 140367161154432 INFO utils.log_raw(): TIMING CC Main:  0.822 secs for get 27 channels

2025-04-17 02:00:19,011 140367161154432 INFO correlate.cc_timespan(): Checking for stations already done: 17 pairs

2025-04-17 02:00:19,012 140367161154432 INFO utils.log_raw(): TIMING CC Main:  0.010 secs for check for 6 stations already done (warm up cache)

2025-04-17 02:00:19,017 140367161154432 INFO utils.log_raw(): TIMING CC Main:  0.004 secs for check for stations already done

2025-04-17 02:00:19,017 140367161154432 INFO correlate.cc_timespan(): Still need to process: 6/6 stations, 27/27 channels, 17/17 pairs for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2025-04-17 02:00:21,909 140367161154432 INFO correlate._filter_channel_data(): Filtered to 18/27 channels with sampling rate >= 20.0

2025-04-17 02:00:21,913 140367161154432 INFO utils.log_raw(): TIMING CC Main:  2.896 secs for Read channel data: 18 channels

2025-04-17 02:00:23,891 140365147408064 INFO noise_module.preprocess_raw(): removing response for CI.SVD..BHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:24,012 140364927207104 INFO noise_module.preprocess_raw(): removing response for CI.SVD..BHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:24,101 140365355026112 INFO noise_module.preprocess_raw(): removing response for CI.SVD..BHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:25,251 140364964955840 INFO noise_module.preprocess_raw(): removing response for NC.BBGB..HHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:25,491 140365166282432 INFO noise_module.preprocess_raw(): removing response for NC.BBGB..HHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:25,501 140365336151744 INFO noise_module.preprocess_raw(): removing response for NC.GDXB..HHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:25,596 140364946081472 INFO noise_module.preprocess_raw(): removing response for NC.BBGB..HHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:25,601 140365128533696 INFO noise_module.preprocess_raw(): removing response for NC.GDXB..HHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

 WARNING (norm_resp): computed and reported sensitivities differ by more than 5 percent. 
	 Execution continuing.
 WARNING (norm_resp): computed and reported sensitivities differ by more than 5 percent. 
	 Execution continuing.

2025-04-17 02:00:31,357 140365147408064 INFO noise_module.preprocess_raw(): removing response for NC.GDXB..HHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

 WARNING (norm_resp): computed and reported sensitivities differ by more than 5 percent. 
	 Execution continuing.

2025-04-17 02:00:31,833 140365355026112 INFO noise_module.preprocess_raw(): removing response for NC.AFD..HHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:31,944 140364927207104 INFO noise_module.preprocess_raw(): removing response for NC.AFD..HHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:36,595 140364964955840 INFO noise_module.preprocess_raw(): removing response for CI.BBR..BHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:36,817 140364946081472 INFO noise_module.preprocess_raw(): removing response for CI.BBR..BHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:36,829 140365166282432 INFO noise_module.preprocess_raw(): removing response for CI.BBR..BHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:37,194 140365336151744 INFO noise_module.preprocess_raw(): removing response for CI.VES..BHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:38,090 140365128533696 INFO noise_module.preprocess_raw(): removing response for NC.AFD..HHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:41,736 140365147408064 INFO noise_module.preprocess_raw(): removing response for CI.VES..BHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:41,986 140364964955840 INFO noise_module.preprocess_raw(): removing response for CI.VES..BHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:44,391 140367161154432 INFO utils.log_raw(): TIMING CC Main: 22.478 secs for Preprocess: 18 channels

2025-04-17 02:00:44,392 140367161154432 INFO correlate.check_memory(): Require  0.22gb memory for cross correlations

2025-04-17 02:00:45,946 140367161154432 INFO utils.log_raw(): TIMING CC Main:  1.553 secs for Compute FFTs: 18 channels

2025-04-17 02:00:45,951 140367161154432 INFO correlate.cc_timespan(): Starting CC with 17 station pairs

2025-04-17 02:00:50,885 140367161154432 INFO utils.log_raw(): TIMING CC Main:  4.934 secs for Correlate and write to store

2025-04-17 02:00:51,047 140367161154432 INFO utils.log_raw(): TIMING CC Main: 32.868 secs for Process the chunk of 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2025-04-17 02:00:51,055 140367161154432 INFO s3store.get_channels(): Getting 18 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-04-17 02:00:51,063 140367161154432 INFO s3store.get_channels(): Getting 9 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-04-17 02:00:51,065 140367161154432 INFO utils.log_raw(): TIMING CC Main:  0.011 secs for get 27 channels

2025-04-17 02:00:51,075 140367161154432 INFO correlate.cc_timespan(): Checking for stations already done: 17 pairs

2025-04-17 02:00:51,076 140367161154432 INFO utils.log_raw(): TIMING CC Main:  0.010 secs for check for 6 stations already done (warm up cache)

2025-04-17 02:00:51,078 140367161154432 INFO utils.log_raw(): TIMING CC Main:  0.002 secs for check for stations already done

2025-04-17 02:00:51,079 140367161154432 INFO correlate.cc_timespan(): Still need to process: 6/6 stations, 27/27 channels, 17/17 pairs for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-04-17 02:00:54,050 140367161154432 INFO correlate._filter_channel_data(): Filtered to 18/27 channels with sampling rate >= 20.0

2025-04-17 02:00:54,053 140367161154432 INFO utils.log_raw(): TIMING CC Main:  2.974 secs for Read channel data: 18 channels

2025-04-17 02:00:56,082 140364964955840 INFO noise_module.preprocess_raw(): removing response for CI.SVD..BHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:56,114 140364927207104 INFO noise_module.preprocess_raw(): removing response for CI.SVD..BHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:56,116 140365128533696 INFO noise_module.preprocess_raw(): removing response for CI.SVD..BHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:57,388 140365166282432 INFO noise_module.preprocess_raw(): removing response for NC.BBGB..HHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:57,467 140365147408064 INFO noise_module.preprocess_raw(): removing response for NC.GDXB..HHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

 WARNING (norm_resp): computed and reported sensitivities differ by more than 5 percent. 
	 Execution continuing.
2025-04-17 02:00:57,629 140365336151744 INFO noise_module.preprocess_raw(): removing response for NC.BBGB..HHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:57,638 140364946081472 INFO noise_module.preprocess_raw(): removing response for NC.BBGB..HHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:00:57,679 140365355026112 INFO noise_module.preprocess_raw(): removing response for NC.GDXB..HHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

 WARNING (norm_resp): computed and reported sensitivities differ by more than 5 percent. 
	 Execution continuing.

2025-04-17 02:01:04,555 140365128533696 INFO noise_module.preprocess_raw(): removing response for NC.AFD..HHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:04,625 140364927207104 INFO noise_module.preprocess_raw(): removing response for NC.GDXB..HHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:04,822 140364964955840 INFO noise_module.preprocess_raw(): removing response for NC.AFD..HHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

 WARNING (norm_resp): computed and reported sensitivities differ by more than 5 percent. 
	 Execution continuing.

2025-04-17 02:01:08,494 140365147408064 INFO noise_module.preprocess_raw(): removing response for CI.BBR..BHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:08,671 140365355026112 INFO noise_module.preprocess_raw(): removing response for CI.BBR..BHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:08,673 140365336151744 INFO noise_module.preprocess_raw(): removing response for CI.BBR..BHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:08,880 140364946081472 INFO noise_module.preprocess_raw(): removing response for CI.VES..BHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:08,944 140365166282432 INFO noise_module.preprocess_raw(): removing response for NC.AFD..HHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:13,982 140365147408064 INFO noise_module.preprocess_raw(): removing response for CI.VES..BHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:14,217 140365355026112 INFO noise_module.preprocess_raw(): removing response for CI.VES..BHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2025-04-17 02:01:16,219 140367161154432 INFO utils.log_raw(): TIMING CC Main: 22.166 secs for Preprocess: 18 channels

2025-04-17 02:01:16,220 140367161154432 INFO correlate.check_memory(): Require  0.22gb memory for cross correlations

2025-04-17 02:01:17,730 140367161154432 INFO utils.log_raw(): TIMING CC Main:  1.509 secs for Compute FFTs: 18 channels

2025-04-17 02:01:17,734 140367161154432 INFO correlate.cc_timespan(): Starting CC with 17 station pairs

2025-04-17 02:01:22,621 140367161154432 INFO utils.log_raw(): TIMING CC Main:  4.887 secs for Correlate and write to store

2025-04-17 02:01:22,779 140367161154432 INFO utils.log_raw(): TIMING CC Main: 31.725 secs for Process the chunk of 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-04-17 02:01:22,788 140367161154432 INFO utils.log_raw(): TIMING CC Main: 64.609 secs for Step 1 in total with 4 cores

The cross correlations are saved as a single file for each channel pair and each increment of inc_hours. We now will stack all the cross correlations over all time chunk and look at all station pairs results.

Step 2: Stack the cross correlation#

We now create the stack stores. Because this tutorial runs locally, we will use an ASDF stack store to output the data. ASDF is a data container in HDF5 that is used in full waveform modeling and inversion. H5 behaves well locally.

Each station pair will have 1 H5 file with all components of the cross correlations. While this produces many H5 files, it has come down to the noisepy team’s favorite option:

the thread-safe installation of HDF5 is not trivial
the choice of grouping station pairs within a single file is not flexible to a broad audience of users.

# open a new cc store in read-only mode since we will be doing parallel access for stacking
cc_store = ASDFCCStore(cc_data_path, mode="r")
stack_store = ASDFStackStore(stack_data_path)

Configure the stacking#

There are various methods for optimal stacking. We refern to Yang et al (2022) for a discussion and comparison of the methods:

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/ggac410

Users have the choice to implement linear, phase weighted stacks pws (Schimmel et al, 1997), robust stacking (Yang et al, 2022), acf autocovariance filter (Nakata et al, 2019), nroot stacking. Users may choose the stacking method of their choice by entering the strings in config.stack_method.

If chosen all, the current code only ouputs linear, pws, robust since nroot is less common and acf is computationally expensive.

config.stack_method = StackMethod.LINEAR

method_list = [method for method in dir(StackMethod) if not method.startswith("__")]
print(method_list)

['ALL', 'AUTO_COVARIANCE', 'LINEAR', 'NROOT', 'PWS', 'ROBUST', 'SELECTIVE']

cc_store.get_station_pairs()

[(NC.BBGB, CI.SVD),
 (NC.AFD, CI.VES),
 (NC.BBGB, NC.AFD),
 (CI.VES, CI.VES),
 (CI.SVD, CI.SVD),
 (NC.BBGB, NC.GDXB),
 (NC.BBGB, CI.VES),
 (CI.SVD, CI.VES),
 (CI.BBR, CI.BBR),
 (NC.GDXB, NC.AFD),
 (NC.GDXB, CI.VES),
 (NC.GDXB, NC.GDXB),
 (NC.BBGB, CI.BBR),
 (NC.BBGB, NC.BBGB),
 (CI.BBR, CI.VES),
 (NC.AFD, NC.AFD),
 (CI.SVD, CI.BBR)]

stack_cross_correlations(cc_store, stack_store, config)

2025-04-17 02:01:22,918 140367161154432 INFO stack.initializer(): Station pairs: 17

2025-04-17 02:01:26,451 140158754032512 INFO stack.stack_store_pair(): Stacking CI.BBR_CI.BBR/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,515 140158754032512 INFO utils.log_raw(): TIMING:  0.059 secs for loading CCF data
2025-04-17 02:01:26,517 140358358809472 INFO stack.stack_store_pair(): Stacking CI.SVD_CI.BBR/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,528 140158754032512 INFO utils.log_raw(): TIMING:  0.013 secs for stack/rotate all station pairs (CI.BBR, CI.BBR)
2025-04-17 02:01:26,550 140158754032512 INFO utils.log_raw(): TIMING:  0.022 secs for writing stack pair (CI.BBR, CI.BBR)
2025-04-17 02:01:26,551 140158754032512 INFO stack.stack_store_pair(): Stacking CI.SVD_CI.VES/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,561 140172052765568 INFO stack.stack_store_pair(): Stacking CI.BBR_CI.VES/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,614 140672825367424 INFO stack.stack_store_pair(): Stacking CI.SVD_CI.SVD/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,617 140358358809472 INFO utils.log_raw(): TIMING:  0.093 secs for loading CCF data
2025-04-17 02:01:26,633 140358358809472 INFO utils.log_raw(): TIMING:  0.016 secs for stack/rotate all station pairs (CI.SVD, CI.BBR)
2025-04-17 02:01:26,642 140158754032512 INFO utils.log_raw(): TIMING:  0.088 secs for loading CCF data

2025-04-17 02:01:26,657 140158754032512 INFO utils.log_raw(): TIMING:  0.015 secs for stack/rotate all station pairs (CI.SVD, CI.VES)
2025-04-17 02:01:26,661 140172052765568 INFO utils.log_raw(): TIMING:  0.094 secs for loading CCF data
2025-04-17 02:01:26,678 140172052765568 INFO utils.log_raw(): TIMING:  0.017 secs for stack/rotate all station pairs (CI.BBR, CI.VES)
2025-04-17 02:01:26,684 140672825367424 INFO utils.log_raw(): TIMING:  0.064 secs for loading CCF data
2025-04-17 02:01:26,698 140672825367424 INFO utils.log_raw(): TIMING:  0.014 secs for stack/rotate all station pairs (CI.SVD, CI.SVD)
2025-04-17 02:01:26,704 140358358809472 INFO utils.log_raw(): TIMING:  0.070 secs for writing stack pair (CI.SVD, CI.BBR)
2025-04-17 02:01:26,707 140358358809472 INFO stack.stack_store_pair(): Stacking CI.VES_CI.VES/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,720 140672825367424 INFO utils.log_raw(): TIMING:  0.022 secs for writing stack pair (CI.SVD, CI.SVD)
2025-04-17 02:01:26,721 140672825367424 INFO stack.stack_store_pair(): Stacking NC.AFD_CI.VES/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,727 140158754032512 INFO utils.log_raw(): TIMING:  0.069 secs for writing stack pair (CI.SVD, CI.VES)
2025-04-17 02:01:26,727 140158754032512 INFO stack.stack_store_pair(): Stacking NC.AFD_NC.AFD/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,762 140172052765568 INFO utils.log_raw(): TIMING:  0.082 secs for writing stack pair (CI.BBR, CI.VES)
2025-04-17 02:01:26,767 140172052765568 INFO stack.stack_store_pair(): Stacking NC.BBGB_CI.BBR/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,767 140358358809472 INFO utils.log_raw(): TIMING:  0.058 secs for loading CCF data
2025-04-17 02:01:26,779 140358358809472 INFO utils.log_raw(): TIMING:  0.011 secs for stack/rotate all station pairs (CI.VES, CI.VES)
2025-04-17 02:01:26,779 140158754032512 INFO utils.log_raw(): TIMING:  0.049 secs for loading CCF data
2025-04-17 02:01:26,791 140158754032512 INFO utils.log_raw(): TIMING:  0.011 secs for stack/rotate all station pairs (NC.AFD, NC.AFD)
2025-04-17 02:01:26,801 140358358809472 INFO utils.log_raw(): TIMING:  0.022 secs for writing stack pair (CI.VES, CI.VES)
2025-04-17 02:01:26,802 140358358809472 INFO stack.stack_store_pair(): Stacking NC.BBGB_CI.SVD/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,811 140672825367424 INFO utils.log_raw(): TIMING:  0.087 secs for loading CCF data
2025-04-17 02:01:26,814 140158754032512 INFO utils.log_raw(): TIMING:  0.023 secs for writing stack pair (NC.AFD, NC.AFD)
2025-04-17 02:01:26,815 140158754032512 INFO stack.stack_store_pair(): Stacking NC.BBGB_CI.VES/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,827 140672825367424 INFO utils.log_raw(): TIMING:  0.015 secs for stack/rotate all station pairs (NC.AFD, CI.VES)
2025-04-17 02:01:26,858 140172052765568 INFO utils.log_raw(): TIMING:  0.086 secs for loading CCF data

2025-04-17 02:01:26,875 140172052765568 INFO utils.log_raw(): TIMING:  0.018 secs for stack/rotate all station pairs (NC.BBGB, CI.BBR)
2025-04-17 02:01:26,887 140358358809472 INFO utils.log_raw(): TIMING:  0.081 secs for loading CCF data
2025-04-17 02:01:26,891 140158754032512 INFO utils.log_raw(): TIMING:  0.073 secs for loading CCF data
2025-04-17 02:01:26,895 140672825367424 INFO utils.log_raw(): TIMING:  0.068 secs for writing stack pair (NC.AFD, CI.VES)
2025-04-17 02:01:26,896 140672825367424 INFO stack.stack_store_pair(): Stacking NC.BBGB_NC.AFD/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,906 140358358809472 INFO utils.log_raw(): TIMING:  0.019 secs for stack/rotate all station pairs (NC.BBGB, CI.SVD)
2025-04-17 02:01:26,909 140158754032512 INFO utils.log_raw(): TIMING:  0.018 secs for stack/rotate all station pairs (NC.BBGB, CI.VES)
2025-04-17 02:01:26,949 140172052765568 INFO utils.log_raw(): TIMING:  0.073 secs for writing stack pair (NC.BBGB, CI.BBR)
2025-04-17 02:01:26,950 140172052765568 INFO stack.stack_store_pair(): Stacking NC.BBGB_NC.BBGB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,969 140672825367424 INFO utils.log_raw(): TIMING:  0.070 secs for loading CCF data
2025-04-17 02:01:26,978 140358358809472 INFO utils.log_raw(): TIMING:  0.072 secs for writing stack pair (NC.BBGB, CI.SVD)
2025-04-17 02:01:26,979 140358358809472 INFO stack.stack_store_pair(): Stacking NC.BBGB_NC.GDXB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,981 140158754032512 INFO utils.log_raw(): TIMING:  0.072 secs for writing stack pair (NC.BBGB, CI.VES)
2025-04-17 02:01:26,981 140158754032512 INFO stack.stack_store_pair(): Stacking NC.GDXB_CI.VES/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:26,988 140672825367424 INFO utils.log_raw(): TIMING:  0.018 secs for stack/rotate all station pairs (NC.BBGB, NC.AFD)
2025-04-17 02:01:27,012 140172052765568 INFO utils.log_raw(): TIMING:  0.056 secs for loading CCF data
2025-04-17 02:01:27,023 140172052765568 INFO utils.log_raw(): TIMING:  0.011 secs for stack/rotate all station pairs (NC.BBGB, NC.BBGB)
2025-04-17 02:01:27,045 140172052765568 INFO utils.log_raw(): TIMING:  0.022 secs for writing stack pair (NC.BBGB, NC.BBGB)
2025-04-17 02:01:27,045 140172052765568 INFO stack.stack_store_pair(): Stacking NC.GDXB_NC.AFD/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:27,057 140672825367424 INFO utils.log_raw(): TIMING:  0.069 secs for writing stack pair (NC.BBGB, NC.AFD)
2025-04-17 02:01:27,057 140672825367424 INFO stack.stack_store_pair(): Stacking NC.GDXB_NC.GDXB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:01:27,059 140358358809472 INFO utils.log_raw(): TIMING:  0.077 secs for loading CCF data
2025-04-17 02:01:27,070 140158754032512 INFO utils.log_raw(): TIMING:  0.082 secs for loading CCF data
2025-04-17 02:01:27,076 140358358809472 INFO utils.log_raw(): TIMING:  0.017 secs for stack/rotate all station pairs (NC.BBGB, NC.GDXB)

2025-04-17 02:01:27,091 140158754032512 INFO utils.log_raw(): TIMING:  0.021 secs for stack/rotate all station pairs (NC.GDXB, CI.VES)
2025-04-17 02:01:27,111 140672825367424 INFO utils.log_raw(): TIMING:  0.050 secs for loading CCF data
2025-04-17 02:01:27,122 140672825367424 INFO utils.log_raw(): TIMING:  0.012 secs for stack/rotate all station pairs (NC.GDXB, NC.GDXB)
2025-04-17 02:01:27,123 140172052765568 INFO utils.log_raw(): TIMING:  0.072 secs for loading CCF data
2025-04-17 02:01:27,139 140172052765568 INFO utils.log_raw(): TIMING:  0.016 secs for stack/rotate all station pairs (NC.GDXB, NC.AFD)
2025-04-17 02:01:27,146 140672825367424 INFO utils.log_raw(): TIMING:  0.024 secs for writing stack pair (NC.GDXB, NC.GDXB)
2025-04-17 02:01:27,149 140358358809472 INFO utils.log_raw(): TIMING:  0.073 secs for writing stack pair (NC.BBGB, NC.GDXB)
2025-04-17 02:01:27,167 140158754032512 INFO utils.log_raw(): TIMING:  0.075 secs for writing stack pair (NC.GDXB, CI.VES)
2025-04-17 02:01:27,194 140172052765568 INFO utils.log_raw(): TIMING:  0.054 secs for writing stack pair (NC.GDXB, NC.AFD)

2025-04-17 02:01:27,778 140367161154432 INFO utils.log_raw(): TIMING:  4.863 secs for step 2 in total

QC_1 of the cross correlations for Imaging#

We now explore the quality of the cross correlations. We plot the moveout of the cross correlations, filtered in some frequency band.

cc_store.get_station_pairs()

[(NC.BBGB, CI.SVD),
 (NC.AFD, CI.VES),
 (NC.BBGB, NC.AFD),
 (CI.VES, CI.VES),
 (CI.SVD, CI.SVD),
 (NC.BBGB, NC.GDXB),
 (NC.BBGB, CI.VES),
 (CI.SVD, CI.VES),
 (CI.BBR, CI.BBR),
 (NC.GDXB, NC.AFD),
 (NC.GDXB, CI.VES),
 (NC.GDXB, NC.GDXB),
 (NC.BBGB, CI.BBR),
 (NC.BBGB, NC.BBGB),
 (CI.BBR, CI.VES),
 (NC.AFD, NC.AFD),
 (CI.SVD, CI.BBR)]

pairs = stack_store.get_station_pairs()
print(f"Found {len(pairs)} station pairs")
sta_stacks = stack_store.read_bulk(timerange, pairs) # no timestamp used in ASDFStackStore

Found 17 station pairs

2025-04-17 02:01:28,279 140367161154432 INFO utils.log_raw(): TIMING:  0.445 secs for loading 17 stacks

plot_all_moveout(sta_stacks, 'Allstack_linear', 0.1, 0.2, 'ZZ', 1)

2025-04-17 02:01:28,305 140367161154432 INFO plotting_modules.plot_all_moveout(): Plottting: Allstack_linear, 17 station pairs

_images/45f7029f7ea5c37a86491ec543382919df27bafce231fa10fff868f6f7d48eec.png