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/3.10.17/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 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)
2025-04-17 02:01:47,650 139774919121792 INFO utils.log_raw(): TIMING: 0.909 secs for Listing 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.001/
2025-04-17 02:01:47,668 139774919121792 INFO utils.log_raw(): TIMING: 0.018 secs for Init: 1 timespans and 9 channels
2025-04-17 02:01:47,871 139773314336448 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KCT.xml
2025-04-17 02:01:48,098 139773421291200 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KRP.xml
2025-04-17 02:01:48,212 139773333210816 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://ncedc-pds/FDSNstationXML/NC/NC.KHMB.xml
2025-04-17 02:01:51,154 139774919121792 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)
2025-04-17 02:01:51,184 139774919121792 INFO correlate.cross_correlate(): Starting Cross-Correlation with 4 cores
2025-04-17 02:01:51,186 139774919121792 INFO s3store.get_channels(): Getting 9 channels for 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000
2025-04-17 02:01:51,189 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.004 secs for get 9 channels
2025-04-17 02:01:51,190 139774919121792 INFO correlate.cc_timespan(): Checking for stations already done: 6 pairs
2025-04-17 02:01:51,191 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.002 secs for check for 3 stations already done (warm up cache)
2025-04-17 02:01:51,196 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.005 secs for check for stations already done
2025-04-17 02:01:51,197 139774919121792 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
2025-04-17 02:02:00,277 139774919121792 INFO correlate._filter_channel_data(): Picked 100.0 as the closest sampling_rate to 20.0.
2025-04-17 02:02:00,278 139774919121792 INFO correlate._filter_channel_data(): Filtered to 9/9 channels with sampling rate == 100.0
2025-04-17 02:02:00,279 139774919121792 INFO utils.log_raw(): TIMING CC Main: 9.083 secs for Read channel data: 9 channels
2025-04-17 02:02:03,955 139773289170624 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
2025-04-17 02:02:04,025 139772911683264 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
2025-04-17 02:02:04,054 139773094135488 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
2025-04-17 02:02:04,078 139773113009856 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
2025-04-17 02:02:04,152 139772892808896 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
2025-04-17 02:02:04,162 139773131884224 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
2025-04-17 02:02:04,175 139773333210816 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
2025-04-17 02:02:04,279 139772930557632 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
2025-04-17 02:02:21,154 139773094135488 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
2025-04-17 02:02:26,645 139774919121792 INFO utils.log_raw(): TIMING CC Main: 26.366 secs for Preprocess: 9 channels
2025-04-17 02:02:26,647 139774919121792 INFO correlate.check_memory(): Require 0.11gb memory for cross correlations
2025-04-17 02:02:27,555 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.907 secs for Compute FFTs: 9 channels
2025-04-17 02:02:27,556 139774919121792 INFO correlate.cc_timespan(): Starting CC with 6 station pairs
2025-04-17 02:02:29,121 139774919121792 INFO utils.log_raw(): TIMING CC Main: 1.564 secs for Correlate and write to store
2025-04-17 02:02:29,247 139774919121792 INFO utils.log_raw(): TIMING CC Main: 38.062 secs for Process the chunk of 2012-01-01T00:00:00+0000 - 2012-01-02T00:00:00+0000
2025-04-17 02:02:29,548 139774919121792 INFO utils.log_raw(): TIMING: 0.299 secs for Listing 804 files from s3://ncedc-pds/continuous_waveforms/NC/2012/2012.002/
2025-04-17 02:02:29,565 139774919121792 INFO utils.log_raw(): TIMING: 0.017 secs for Init: 2 timespans and 18 channels
2025-04-17 02:02:29,566 139774919121792 INFO s3store.get_channels(): Getting 9 channels for 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:02:29,569 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.320 secs for get 9 channels
2025-04-17 02:02:29,570 139774919121792 INFO correlate.cc_timespan(): Checking for stations already done: 6 pairs
2025-04-17 02:02:29,571 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.001 secs for check for 3 stations already done (warm up cache)
2025-04-17 02:02:29,573 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.001 secs for check for stations already done
2025-04-17 02:02:29,573 139774919121792 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
2025-04-17 02:02:30,539 139774919121792 INFO correlate._filter_channel_data(): Picked 100.0 as the closest sampling_rate to 20.0.
2025-04-17 02:02:30,540 139774919121792 INFO correlate._filter_channel_data(): Filtered to 9/9 channels with sampling rate == 100.0
2025-04-17 02:02:30,541 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.969 secs for Read channel data: 9 channels
2025-04-17 02:02:34,095 139773094135488 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
2025-04-17 02:02:34,350 139772930557632 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
2025-04-17 02:02:34,361 139773113009856 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
2025-04-17 02:02:34,375 139773131884224 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
2025-04-17 02:02:34,386 139772892808896 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
2025-04-17 02:02:34,388 139773289170624 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
2025-04-17 02:02:34,406 139772911683264 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
2025-04-17 02:02:34,409 139772710356672 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
2025-04-17 02:02:51,194 139773094135488 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
2025-04-17 02:02:56,658 139774919121792 INFO utils.log_raw(): TIMING CC Main: 26.117 secs for Preprocess: 9 channels
2025-04-17 02:02:56,659 139774919121792 INFO correlate.check_memory(): Require 0.11gb memory for cross correlations
2025-04-17 02:02:57,611 139774919121792 INFO utils.log_raw(): TIMING CC Main: 0.951 secs for Compute FFTs: 9 channels
2025-04-17 02:02:57,613 139774919121792 INFO correlate.cc_timespan(): Starting CC with 6 station pairs
2025-04-17 02:02:59,255 139774919121792 INFO utils.log_raw(): TIMING CC Main: 1.642 secs for Correlate and write to store
2025-04-17 02:02:59,380 139774919121792 INFO utils.log_raw(): TIMING CC Main: 30.130 secs for Process the chunk of 2012-01-02T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:02:59,385 139774919121792 INFO utils.log_raw(): TIMING CC Main: 68.201 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()
config.stations = ["*"]
config.networks = ["*"]
stack_cross_correlations(cc_store, stack_store, config)
2025-04-17 02:02:59,512 139774919121792 INFO stack.initializer(): Station pairs: 6
2025-04-17 02:03:02,973 140185966783360 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KCT/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:03:02,998 140221591047040 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:03:03,033 140524080753536 INFO stack.stack_store_pair(): Stacking NC.KCT_NC.KHMB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:03:03,038 140185966783360 INFO utils.log_raw(): TIMING: 0.059 secs for loading CCF data
2025-04-17 02:03:03,055 140185966783360 INFO utils.log_raw(): TIMING: 0.017 secs for stack/rotate all station pairs (NC.KCT, NC.KCT)
2025-04-17 02:03:03,061 140482147371904 INFO stack.stack_store_pair(): Stacking NC.KHMB_NC.KHMB/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:03:03,078 140185966783360 INFO utils.log_raw(): TIMING: 0.023 secs for writing stack pair (NC.KCT, NC.KCT)
2025-04-17 02:03:03,080 140185966783360 INFO stack.stack_store_pair(): Stacking NC.KHMB_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:03:03,092 140221591047040 INFO utils.log_raw(): TIMING: 0.089 secs for loading CCF data
2025-04-17 02:03:03,110 140221591047040 INFO utils.log_raw(): TIMING: 0.018 secs for stack/rotate all station pairs (NC.KCT, NC.KRP)
2025-04-17 02:03:03,128 140524080753536 INFO utils.log_raw(): TIMING: 0.090 secs for loading CCF data
2025-04-17 02:03:03,130 140482147371904 INFO utils.log_raw(): TIMING: 0.064 secs for loading CCF data
2025-04-17 02:03:03,145 140524080753536 INFO utils.log_raw(): TIMING: 0.017 secs for stack/rotate all station pairs (NC.KCT, NC.KHMB)
2025-04-17 02:03:03,146 140482147371904 INFO utils.log_raw(): TIMING: 0.016 secs for stack/rotate all station pairs (NC.KHMB, NC.KHMB)
2025-04-17 02:03:03,170 140482147371904 INFO utils.log_raw(): TIMING: 0.023 secs for writing stack pair (NC.KHMB, NC.KHMB)
2025-04-17 02:03:03,172 140482147371904 INFO stack.stack_store_pair(): Stacking NC.KRP_NC.KRP/2012-01-01T00:00:00+0000 - 2012-01-03T00:00:00+0000
2025-04-17 02:03:03,177 140221591047040 INFO utils.log_raw(): TIMING: 0.067 secs for writing stack pair (NC.KCT, NC.KRP)
2025-04-17 02:03:03,181 140185966783360 INFO utils.log_raw(): TIMING: 0.098 secs for loading CCF data
2025-04-17 02:03:03,198 140185966783360 INFO utils.log_raw(): TIMING: 0.016 secs for stack/rotate all station pairs (NC.KHMB, NC.KRP)
2025-04-17 02:03:03,203 140524080753536 INFO utils.log_raw(): TIMING: 0.058 secs for writing stack pair (NC.KCT, NC.KHMB)
2025-04-17 02:03:03,231 140482147371904 INFO utils.log_raw(): TIMING: 0.055 secs for loading CCF data
2025-04-17 02:03:03,239 140482147371904 INFO utils.log_raw(): TIMING: 0.008 secs for stack/rotate all station pairs (NC.KRP, NC.KRP)
2025-04-17 02:03:03,246 140185966783360 INFO utils.log_raw(): TIMING: 0.048 secs for writing stack pair (NC.KHMB, NC.KRP)
2025-04-17 02:03:03,253 140482147371904 INFO utils.log_raw(): TIMING: 0.014 secs for writing stack pair (NC.KRP, NC.KRP)
2025-04-17 02:03:03,823 139774919121792 INFO utils.log_raw(): TIMING: 4.313 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.KRP, NC.KRP),
(NC.KHMB, NC.KRP),
(NC.KCT, NC.KCT),
(NC.KHMB, NC.KHMB),
(NC.KCT, NC.KHMB),
(NC.KCT, NC.KRP)]
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
2025-04-17 02:03:04,018 139774919121792 INFO utils.log_raw(): TIMING: 0.141 secs for loading 6 stacks
plot_all_moveout(sta_stacks, 'Allstack_linear', 0.1, 0.2, 'ZZ', 1)
2025-04-17 02:03:04,045 139774919121792 INFO plotting_modules.plot_all_moveout(): Plottting: Allstack_linear, 6 station pairs
200 8001
