NoisePy NCEDC Tutorial

NoisePy NCEDC 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 the NCEDC data. It can be ran locally or on the cloud.

The data is stored on AWS S3 as the 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.s3store import NCEDCS3DataStore # Object to query SCEDC data from on S3
from noisepy.seis.io.channel_filter_store import channel_filter
from noisepy.seis.io.datatypes import CCMethod, ConfigParameters, FreqNorm, RmResp, StackMethod, TimeNorm        # Main configuration object
from noisepy.seis.io.channelcatalog import XMLStationChannelCatalog        # Required stationXML handling object
import os
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 = "./ncedc_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/3.10.14/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 NCEDC. The continuous data is organized with a single day and channel per miniseed. For this example, you can choose any year since 1993. We will just cross correlate a single day.

# NCEDC S3 bucket common URL characters for that day.
S3_DATA = "s3://ncedc-pds/continuous_waveforms/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

The station information, including the instrumental response, is stored as stationXML in the following bucket

S3_STATION_XML = "s3://ncedc-pds/FDSNstationXML/NC/"            # S3 storage of stationXML

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 NCEDC, we can afford 20Hz data and inc_hour 
        # being a day of data.

 
# pre-processing parameters
config.samp_freq= 20  # (int) Sampling rate in Hz of desired processing (it can be different than the data sampling rate)
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_len = config.cc_len  # how long to stack over (for monitoring purpose): need to be multiples of cc_len
    # if substack=True, substack_len=2*cc_len, then you pre-stack every 2 correlation windows.
    # for instance: substack=True, substack_len=cc_len 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_len = 4* config.cc_len

config.maxlag= 200  # lags of cross-correlation to save (sec)
config.net_list=['NC']

# For this tutorial make sure the previous run is empty
os.system(f"rm -rf {cc_data_path}")
os.system(f"rm -rf {stack_data_path}")

0

Step 1: Cross-correlation#

In this instance, we read directly the data from the ncedc 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

stations = "KCT,KRP,KHMB".split(",") # filter to these stations
catalog = XMLStationChannelCatalog(S3_STATION_XML, "{network}.{name}.xml", storage_options=S3_STORAGE_OPTIONS) # Station catalog
raw_store = NCEDCS3DataStore(S3_DATA, catalog, 
                             channel_filter(config.net_list, stations, ["HHE", "HHN", "HHZ"]), 
                             timerange, storage_options=S3_STORAGE_OPTIONS) # Store for reading raw data from S3 bucket
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[0])
print(channel_list)

2024-06-24 21:36:37,176 140252279077760 INFO utils.log_raw(): TIMING: 0.1887 secs. for Loading 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.001/

2024-06-24 21:36:37,191 140252279077760 INFO utils.log_raw(): TIMING: 0.0144 secs. for Init: 1 timespans and 9 channels

2024-06-24 21:36:37,240 140250781656640 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KRP.xml

2024-06-24 21:36:37,284 140250764875328 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KCT.xml

2024-06-24 21:36:37,306 140250546435648 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KHMB.xml

2024-06-24 21:36:38,051 140252279077760 INFO s3store.get_channels(): Getting 9 channels for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000: [NC.KCT.HHE, NC.KCT.HHN, NC.KCT.HHZ, NC.KHMB.HHE, NC.KHMB.HHN, NC.KHMB.HHZ, NC.KRP.HHE, NC.KRP.HHN, NC.KRP.HHZ]

[NC.KCT.HHE, NC.KCT.HHN, NC.KCT.HHZ, NC.KHMB.HHE, NC.KHMB.HHN, NC.KHMB.HHZ, NC.KRP.HHE, NC.KRP.HHN, NC.KRP.HHZ]

Perform the cross correlation#

The data will be pulled from 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.

cross_correlate(raw_store, config, cc_store)

2024-06-24 21:36:38,084 140252279077760 INFO correlate.cross_correlate(): Starting Cross-Correlation with 4 cores

2024-06-24 21:36:38,085 140252279077760 INFO s3store.get_channels(): Getting 9 channels for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000: [NC.KCT.HHE, NC.KCT.HHN, NC.KCT.HHZ, NC.KHMB.HHE, NC.KHMB.HHN, NC.KHMB.HHZ, NC.KRP.HHE, NC.KRP.HHN, NC.KRP.HHZ]

2024-06-24 21:36:38,089 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.0048 secs. for get 9 channels

2024-06-24 21:36:38,090 140252279077760 INFO correlate.cc_timespan(): Checking for stations already done: 6 pairs

2024-06-24 21:36:38,091 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.0017 secs. for check for 3 stations already done (warm up cache)

2024-06-24 21:36:38,094 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.0028 secs. for check for stations already done

2024-06-24 21:36:38,095 140252279077760 INFO correlate.cc_timespan(): Still need to process: 3/3 stations, 9/9 channels, 6/6 pairs for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2024-06-24 21:36:38,724 140252279077760 INFO correlate._filter_channel_data(): Picked 100.0 as the closest sampling frequence to 20.0. 

2024-06-24 21:36:38,725 140252279077760 INFO correlate._filter_channel_data(): Filtered to 9/9 channels with sampling rate == 100.0

2024-06-24 21:36:38,726 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.6316 secs. for Read channel data: 9 channels

2024-06-24 21:36:42,474 140250210891328 INFO noise_module.preprocess_raw(): removing response for NC.KCT..HHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,481 140250177328704 INFO noise_module.preprocess_raw(): removing response for NC.KHMB..HHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,498 140250546435648 INFO noise_module.preprocess_raw(): removing response for NC.KRP..HHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,505 140250194110016 INFO noise_module.preprocess_raw(): removing response for NC.KRP..HHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,507 140250522314304 INFO noise_module.preprocess_raw(): removing response for NC.KCT..HHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,527 140250781656640 INFO noise_module.preprocess_raw(): removing response for NC.KHMB..HHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,540 140250505532992 INFO noise_module.preprocess_raw(): removing response for NC.KHMB..HHE | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:42,569 140250764875328 INFO noise_module.preprocess_raw(): removing response for NC.KCT..HHN | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:36:59,609 140250522314304 INFO noise_module.preprocess_raw(): removing response for NC.KRP..HHZ | 2012-01-01T00:00:00.000000Z - 2012-01-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:05,112 140252279077760 INFO utils.log_raw(): TIMING CC Main: 26.3856 secs. for Preprocess: 9 channels

2024-06-24 21:37:05,113 140252279077760 INFO correlate.check_memory(): Require  0.11gb memory for cross correlations

2024-06-24 21:37:05,968 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.8544 secs. for Compute FFTs: 9 channels

2024-06-24 21:37:05,969 140252279077760 INFO correlate.cc_timespan(): Starting CC with 6 station pairs

2024-06-24 21:37:07,435 140252279077760 INFO utils.log_raw(): TIMING CC Main: 1.4661 secs. for Correlate and write to store

2024-06-24 21:37:07,557 140252279077760 INFO utils.log_raw(): TIMING CC Main: 29.4727 secs. for Process the chunk of 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2024-06-24 21:37:07,853 140252279077760 INFO utils.log_raw(): TIMING: 0.2905 secs. for Loading 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.002/

2024-06-24 21:37:07,867 140252279077760 INFO utils.log_raw(): TIMING: 0.0141 secs. for Init: 2 timespans and 18 channels

2024-06-24 21:37:07,868 140252279077760 INFO s3store.get_channels(): Getting 9 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000: [NC.KCT.HHE, NC.KCT.HHN, NC.KCT.HHZ, NC.KHMB.HHE, NC.KHMB.HHN, NC.KHMB.HHZ, NC.KRP.HHE, NC.KRP.HHN, NC.KRP.HHZ]

2024-06-24 21:37:07,871 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.3090 secs. for get 9 channels

2024-06-24 21:37:07,872 140252279077760 INFO correlate.cc_timespan(): Checking for stations already done: 6 pairs

2024-06-24 21:37:07,873 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.0017 secs. for check for 3 stations already done (warm up cache)

2024-06-24 21:37:07,875 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.0013 secs. for check for stations already done

2024-06-24 21:37:07,875 140252279077760 INFO correlate.cc_timespan(): Still need to process: 3/3 stations, 9/9 channels, 6/6 pairs for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2024-06-24 21:37:08,490 140252279077760 INFO correlate._filter_channel_data(): Picked 100.0 as the closest sampling frequence to 20.0. 

2024-06-24 21:37:08,491 140252279077760 INFO correlate._filter_channel_data(): Filtered to 9/9 channels with sampling rate == 100.0

2024-06-24 21:37:08,491 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.6167 secs. for Read channel data: 9 channels

2024-06-24 21:37:12,020 140250546435648 INFO noise_module.preprocess_raw(): removing response for NC.KCT..HHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,139 140250194110016 INFO noise_module.preprocess_raw(): removing response for NC.KHMB..HHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,268 140250522314304 INFO noise_module.preprocess_raw(): removing response for NC.KCT..HHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,313 140250210891328 INFO noise_module.preprocess_raw(): removing response for NC.KCT..HHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,368 140250764875328 INFO noise_module.preprocess_raw(): removing response for NC.KRP..HHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,402 140250505532992 INFO noise_module.preprocess_raw(): removing response for NC.KHMB..HHN | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,426 140250781656640 INFO noise_module.preprocess_raw(): removing response for NC.KHMB..HHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:12,443 140250177328704 INFO noise_module.preprocess_raw(): removing response for NC.KRP..HHE | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:29,025 140250546435648 INFO noise_module.preprocess_raw(): removing response for NC.KRP..HHZ | 2012-01-02T00:00:00.000000Z - 2012-01-02T23:59:59.950000Z | 20.0 Hz, 1728000 samples using inv

2024-06-24 21:37:34,506 140252279077760 INFO utils.log_raw(): TIMING CC Main: 26.0150 secs. for Preprocess: 9 channels

2024-06-24 21:37:34,507 140252279077760 INFO correlate.check_memory(): Require  0.11gb memory for cross correlations

2024-06-24 21:37:35,324 140252279077760 INFO utils.log_raw(): TIMING CC Main: 0.8159 secs. for Compute FFTs: 9 channels

2024-06-24 21:37:35,325 140252279077760 INFO correlate.cc_timespan(): Starting CC with 6 station pairs

2024-06-24 21:37:36,834 140252279077760 INFO utils.log_raw(): TIMING CC Main: 1.5093 secs. for Correlate and write to store

2024-06-24 21:37:36,959 140252279077760 INFO utils.log_raw(): TIMING CC Main: 29.3969 secs. for Process the chunk of 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2024-06-24 21:37:36,963 140252279077760 INFO utils.log_raw(): TIMING CC Main: 58.8798 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:

  1. the thread-safe installation of HDF5 is not trivial

  2. 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()
config.stations=["*"]
config.net_list=["*"]

stack_cross_correlations(cc_store, stack_store, config)

2024-06-24 21:37:37,096 140252279077760 INFO stack.initializer(): Station pairs: 6

2024-06-24 21:37:40,575 140581567310720 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KHMB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2024-06-24 21:37:40,656 140581567310720 INFO utils.log_raw(): TIMING: 0.0754 secs. for loading CCF data
2024-06-24 21:37:40,672 140581567310720 INFO utils.log_raw(): TIMING: 0.0164 secs. for stack/rotate all station pairs (NC.KCT, NC.KHMB)
2024-06-24 21:37:40,681 139689887435648 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KCT/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2024-06-24 21:37:40,703 139733651536768 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2024-06-24 21:37:40,726 140581567310720 INFO utils.log_raw(): TIMING: 0.0529 secs. for writing stack pair (NC.KCT, NC.KHMB)
2024-06-24 21:37:40,727 140664826309504 INFO stack.stack_store_pair(): Stacking NC.KHMB_NC.KHMB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2024-06-24 21:37:40,729 140581567310720 INFO stack.stack_store_pair(): Stacking NC.KHMB_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2024-06-24 21:37:40,741 139689887435648 INFO utils.log_raw(): TIMING: 0.0554 secs. for loading CCF data
2024-06-24 21:37:40,757 139689887435648 INFO utils.log_raw(): TIMING: 0.0160 secs. for stack/rotate all station pairs (NC.KCT, NC.KCT)

2024-06-24 21:37:40,776 139689887435648 INFO utils.log_raw(): TIMING: 0.0190 secs. for writing stack pair (NC.KCT, NC.KCT)
2024-06-24 21:37:40,779 139689887435648 INFO stack.stack_store_pair(): Stacking NC.KRP_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2024-06-24 21:37:40,788 139733651536768 INFO utils.log_raw(): TIMING: 0.0803 secs. for loading CCF data
2024-06-24 21:37:40,792 140664826309504 INFO utils.log_raw(): TIMING: 0.0580 secs. for loading CCF data
2024-06-24 21:37:40,803 140581567310720 INFO utils.log_raw(): TIMING: 0.0673 secs. for loading CCF data
2024-06-24 21:37:40,807 139733651536768 INFO utils.log_raw(): TIMING: 0.0190 secs. for stack/rotate all station pairs (NC.KCT, NC.KRP)
2024-06-24 21:37:40,810 140664826309504 INFO utils.log_raw(): TIMING: 0.0186 secs. for stack/rotate all station pairs (NC.KHMB, NC.KHMB)
2024-06-24 21:37:40,820 140581567310720 INFO utils.log_raw(): TIMING: 0.0167 secs. for stack/rotate all station pairs (NC.KHMB, NC.KRP)
2024-06-24 21:37:40,832 140664826309504 INFO utils.log_raw(): TIMING: 0.0204 secs. for writing stack pair (NC.KHMB, NC.KHMB)
2024-06-24 21:37:40,836 139689887435648 INFO utils.log_raw(): TIMING: 0.0549 secs. for loading CCF data
2024-06-24 21:37:40,856 139689887435648 INFO utils.log_raw(): TIMING: 0.0194 secs. for stack/rotate all station pairs (NC.KRP, NC.KRP)
2024-06-24 21:37:40,859 139733651536768 INFO utils.log_raw(): TIMING: 0.0515 secs. for writing stack pair (NC.KCT, NC.KRP)
2024-06-24 21:37:40,869 140581567310720 INFO utils.log_raw(): TIMING: 0.0484 secs. for writing stack pair (NC.KHMB, NC.KRP)
2024-06-24 21:37:40,871 139689887435648 INFO utils.log_raw(): TIMING: 0.0151 secs. for writing stack pair (NC.KRP, NC.KRP)

2024-06-24 21:37:41,438 140252279077760 INFO utils.log_raw(): TIMING: 4.3446 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.KCT, NC.KRP),
 (NC.KRP, NC.KRP),
 (NC.KHMB, NC.KHMB),
 (NC.KHMB, NC.KRP),
 (NC.KCT, NC.KCT),
 (NC.KCT, NC.KHMB)]

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 6 station pairs

2024-06-24 21:37:41,611 140252279077760 INFO utils.log_raw(): TIMING: 0.1167 secs. for loading 6 stacks

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

2024-06-24 21:37:41,637 140252279077760 INFO plotting_modules.plot_all_moveout(): Plottting: Allstack_linear, 6 station pairs

200 8001
_images/a3a70cda2060f9b685b79b8057039a5310e525b38f6980ce19608df850d91c35.png
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