NCEDC Tutorial

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
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/3.10.16/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.sampling_rate = 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_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)

# 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

config.networks = ["NC"]
config.stations = ["KCT", "KRP", "KHMB"]
config.channels = ["HH?"]

catalog = XMLStationChannelCatalog(S3_STATION_XML, "{network}.{name}.xml", storage_options=S3_STORAGE_OPTIONS) # Station catalog
raw_store = NCEDCS3DataStore(S3_DATA, catalog, 
                             channel_filter(config.networks, config.stations, config.channels), 
                             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)

2025-01-14 01:09:08,333 140240918584192 INFO utils.log_raw(): TIMING:  0.180 secs for Listing 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.001/

2025-01-14 01:09:08,351 140240918584192 INFO utils.log_raw(): TIMING:  0.018 secs for Init: 1 timespans and 9 channels

2025-01-14 01:09:08,396 140239337162432 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KHMB.xml

2025-01-14 01:09:08,492 140239318288064 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KRP.xml

2025-01-14 01:09:08,509 140239299413696 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KCT.xml

2025-01-14 01:09:09,576 140240918584192 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]

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)

2025-01-14 01:09:09,610 140240918584192 INFO correlate.cross_correlate(): Starting Cross-Correlation with 4 cores

2025-01-14 01:09:09,611 140240918584192 INFO s3store.get_channels(): Getting 9 channels for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2025-01-14 01:09:09,615 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.004 secs for get 9 channels

2025-01-14 01:09:09,616 140240918584192 INFO correlate.cc_timespan(): Checking for stations already done: 6 pairs

2025-01-14 01:09:09,617 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.002 secs for check for 3 stations already done (warm up cache)

2025-01-14 01:09:09,618 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.001 secs for check for stations already done

2025-01-14 01:09:09,619 140240918584192 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

2025-01-14 01:09:10,270 140240918584192 INFO correlate._filter_channel_data(): Picked 100.0 as the closest sampling_rate to 20.0. 

2025-01-14 01:09:10,271 140240918584192 INFO correlate._filter_channel_data(): Filtered to 9/9 channels with sampling rate == 100.0

2025-01-14 01:09:10,272 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.653 secs for Read channel data: 9 channels

2025-01-14 01:09:13,867 140239299413696 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

2025-01-14 01:09:14,064 140239116961472 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

2025-01-14 01:09:14,066 140238934509248 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

2025-01-14 01:09:14,074 140239337162432 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

2025-01-14 01:09:14,110 140239135835840 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

2025-01-14 01:09:14,115 140239318288064 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

2025-01-14 01:09:14,219 140238915634880 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

2025-01-14 01:09:14,232 140239098087104 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

2025-01-14 01:09:31,076 140239299413696 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

2025-01-14 01:09:36,552 140240918584192 INFO utils.log_raw(): TIMING CC Main: 26.281 secs for Preprocess: 9 channels

2025-01-14 01:09:36,554 140240918584192 INFO correlate.check_memory(): Require  0.11gb memory for cross correlations

2025-01-14 01:09:37,418 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.864 secs for Compute FFTs: 9 channels

2025-01-14 01:09:37,419 140240918584192 INFO correlate.cc_timespan(): Starting CC with 6 station pairs

2025-01-14 01:09:38,998 140240918584192 INFO utils.log_raw(): TIMING CC Main:  1.578 secs for Correlate and write to store

2025-01-14 01:09:39,132 140240918584192 INFO utils.log_raw(): TIMING CC Main: 29.522 secs for Process the chunk of 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000

2025-01-14 01:09:39,377 140240918584192 INFO utils.log_raw(): TIMING:  0.238 secs for Listing 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.002/

2025-01-14 01:09:39,393 140240918584192 INFO utils.log_raw(): TIMING:  0.017 secs for Init: 2 timespans and 18 channels

2025-01-14 01:09:39,395 140240918584192 INFO s3store.get_channels(): Getting 9 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-01-14 01:09:39,399 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.260 secs for get 9 channels

2025-01-14 01:09:39,400 140240918584192 INFO correlate.cc_timespan(): Checking for stations already done: 6 pairs

2025-01-14 01:09:39,401 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.001 secs for check for 3 stations already done (warm up cache)

2025-01-14 01:09:39,402 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.002 secs for check for stations already done

2025-01-14 01:09:39,403 140240918584192 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

2025-01-14 01:09:40,183 140240918584192 INFO correlate._filter_channel_data(): Picked 100.0 as the closest sampling_rate to 20.0. 

2025-01-14 01:09:40,184 140240918584192 INFO correlate._filter_channel_data(): Filtered to 9/9 channels with sampling rate == 100.0

2025-01-14 01:09:40,185 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.782 secs for Read channel data: 9 channels

2025-01-14 01:09:43,781 140238934509248 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

2025-01-14 01:09:43,979 140238915634880 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

2025-01-14 01:09:43,996 140239318288064 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

2025-01-14 01:09:44,047 140239098087104 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

2025-01-14 01:09:44,056 140239135835840 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

2025-01-14 01:09:44,090 140239116961472 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

2025-01-14 01:09:44,121 140238699628224 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

2025-01-14 01:09:44,176 140239299413696 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

2025-01-14 01:10:00,963 140238934509248 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

2025-01-14 01:10:06,451 140240918584192 INFO utils.log_raw(): TIMING CC Main: 26.267 secs for Preprocess: 9 channels

2025-01-14 01:10:06,453 140240918584192 INFO correlate.check_memory(): Require  0.11gb memory for cross correlations

2025-01-14 01:10:07,366 140240918584192 INFO utils.log_raw(): TIMING CC Main:  0.913 secs for Compute FFTs: 9 channels

2025-01-14 01:10:07,369 140240918584192 INFO correlate.cc_timespan(): Starting CC with 6 station pairs

2025-01-14 01:10:09,007 140240918584192 INFO utils.log_raw(): TIMING CC Main:  1.638 secs for Correlate and write to store

2025-01-14 01:10:09,145 140240918584192 INFO utils.log_raw(): TIMING CC Main: 30.006 secs for Process the chunk of 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000

2025-01-14 01:10:09,149 140240918584192 INFO utils.log_raw(): TIMING CC Main: 59.540 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.networks = ["*"]

stack_cross_correlations(cc_store, stack_store, config)

2025-01-14 01:10:09,281 140240918584192 INFO stack.initializer(): Station pairs: 6

2025-01-14 01:10:12,883 140177297140608 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KCT/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-01-14 01:10:12,902 139849130572672 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-01-14 01:10:12,903 139750017264512 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KHMB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-01-14 01:10:12,922 140547567909760 INFO stack.stack_store_pair(): Stacking NC.KHMB_NC.KHMB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-01-14 01:10:12,959 140177297140608 INFO utils.log_raw(): TIMING:  0.071 secs for loading CCF data
2025-01-14 01:10:12,975 140177297140608 INFO utils.log_raw(): TIMING:  0.016 secs for stack/rotate all station pairs (NC.KCT, NC.KCT)
2025-01-14 01:10:12,981 140547567909760 INFO utils.log_raw(): TIMING:  0.054 secs for loading CCF data
2025-01-14 01:10:12,987 139849130572672 INFO utils.log_raw(): TIMING:  0.080 secs for loading CCF data
2025-01-14 01:10:12,988 139750017264512 INFO utils.log_raw(): TIMING:  0.079 secs for loading CCF data
2025-01-14 01:10:12,996 140177297140608 INFO utils.log_raw(): TIMING:  0.020 secs for writing stack pair (NC.KCT, NC.KCT)
2025-01-14 01:10:12,997 140177297140608 INFO stack.stack_store_pair(): Stacking NC.KHMB_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-01-14 01:10:13,000 140547567909760 INFO utils.log_raw(): TIMING:  0.019 secs for stack/rotate all station pairs (NC.KHMB, NC.KHMB)
2025-01-14 01:10:13,009 139750017264512 INFO utils.log_raw(): TIMING:  0.021 secs for stack/rotate all station pairs (NC.KCT, NC.KHMB)
2025-01-14 01:10:13,009 139849130572672 INFO utils.log_raw(): TIMING:  0.022 secs for stack/rotate all station pairs (NC.KCT, NC.KRP)
2025-01-14 01:10:13,021 140547567909760 INFO utils.log_raw(): TIMING:  0.021 secs for writing stack pair (NC.KHMB, NC.KHMB)
2025-01-14 01:10:13,022 140547567909760 INFO stack.stack_store_pair(): Stacking NC.KRP_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-01-14 01:10:13,065 139750017264512 INFO utils.log_raw(): TIMING:  0.055 secs for writing stack pair (NC.KCT, NC.KHMB)
2025-01-14 01:10:13,069 139849130572672 INFO utils.log_raw(): TIMING:  0.060 secs for writing stack pair (NC.KCT, NC.KRP)
2025-01-14 01:10:13,079 140177297140608 INFO utils.log_raw(): TIMING:  0.079 secs for loading CCF data
2025-01-14 01:10:13,080 140547567909760 INFO utils.log_raw(): TIMING:  0.054 secs for loading CCF data

2025-01-14 01:10:13,090 140547567909760 INFO utils.log_raw(): TIMING:  0.010 secs for stack/rotate all station pairs (NC.KRP, NC.KRP)
2025-01-14 01:10:13,091 140177297140608 INFO utils.log_raw(): TIMING:  0.012 secs for stack/rotate all station pairs (NC.KHMB, NC.KRP)
2025-01-14 01:10:13,101 140547567909760 INFO utils.log_raw(): TIMING:  0.011 secs for writing stack pair (NC.KRP, NC.KRP)
2025-01-14 01:10:13,122 140177297140608 INFO utils.log_raw(): TIMING:  0.031 secs for writing stack pair (NC.KHMB, NC.KRP)

2025-01-14 01:10:13,695 140240918584192 INFO utils.log_raw(): TIMING:  4.415 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.KHMB, NC.KRP),
 (NC.KRP, NC.KRP),
 (NC.KCT, NC.KCT),
 (NC.KHMB, NC.KHMB),
 (NC.KCT, NC.KRP),
 (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

2025-01-14 01:10:13,878 140240918584192 INFO utils.log_raw(): TIMING:  0.126 secs for loading 6 stacks

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

2025-01-14 01:10:13,904 140240918584192 INFO plotting_modules.plot_all_moveout(): Plottting: Allstack_linear, 6 station pairs

200 8001
_images/d2dbcf4637e02a1995401b4eac18e5b585fde8de02351869b2f0fa1209462555.png