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A friendly earthquake detector.
First, install Pyrocko, then install Lassie:
git clone https://gitext.gfz-potsdam.de/heimann/lassie.git cd lassie sudo python setup.py install
This tutorial explains how to use Lassie to detect events in a regional network. The one-day test dataset from the Alentejo region in South Portugal can be downloaded from http://kinherd.org/lassie-example-alentejo.tar (235 MB). Data from 12 seismic stations from the DOCTAR experiment and from 5 permanent stations from regional networks [REFERENCE] are included. A region of roughly 100 x 100 km is covered by the available data.
# download data and unpack wget http://kinherd.org/lassie-example-alentejo.tar tar -xf lassie-example-alentejo.tar cd lassie-example-alentejo ls # data/ - raw waveforms in mseed format # stations.txt - text file with station coordinates # confirmed-events.txt - catalog with some known events
snuffler --stations=stations.txt --events=confirmed-events.txt data/
jackseisprogram which is part of Pyrocko, e.g. to cut a whole dataset into hour files use:
jackseis INPUT_DIR --tinc=3600 --output-dir=OUTPUT_DIR
The Lassie earthquake detector can be run from the command line or by calling
Lassie's library functions from a Python script. Here we will demonstrate the
use of the
lassie command line tool. To get brief help on available command
line options, run
lassie --help or
lassie <subcommand> --help. Once dataset
and configuration are ready, the command
lassie scan <configfile> runs the
main detector algorithm. But first it must be configured...
Lassie reads its configuration from a text file in the YAML format. To create an initial configuration, run
lassie init > config.yaml # or filling in already the names of the stations file and the waveform directory: lassie init --stations=stations.txt --data=data > config.yaml
%YAML 1.1 --- !lassie.Config ## Configuration file for Lassie, your friendly earthquake detector ## ## Receiver coordinates can be read from a stations file in Pyrocko format: stations_path: 'stations.txt' ## Receivers can also be listed in the config file, lat/lon and carthesian ## (x/y/z) = (North/East/Down) coordinates are supported and may be combined ## (interpreted as reference + offset). Omitted values are treated as zero. # receivers: # - !lassie.Receiver # codes: ['', 'ACC13', ''] # lat: 10. # lon: 12. # x: 2397.56 # y: 7331.94 # z: -404.1 ## List of data directories. Lassie will recurse into subdirectories to find ## all contained waveform files. data_paths: - 'data' ## Processing time interval (default: use time interval of available data) # tmin: '2012-02-06 04:20:00' # tmax: '2012-02-06 04:30:00' ## Search grid; if not given here (if commented), a default grid will be chosen # grid: !lassie.Carthesian3DGrid # lat: 38.7 # lon: -7.9 # xmin: -70e3 # in [m] # xmax: 70e3 # ymin: -70e3 # ymax: 70e3 # zmin: 0.0 # zmax: 0.0 # dx: 2.5e3 # dy: 2.5e3 # dz: 2.5e3 ## Size factor to use when automatically choosing a grid size autogrid_radius_factor: 1.5 ## Grid density factor used when automatically choosing a grid autogrid_density_factor: 10.0 ## Composition of image function image_function_contributions: - !lassie.WavePacketIFC name: 'P' weight: 1.0 fmin: 1.0 fmax: 15.0 fsmooth_factor: 0.1 shifter: !lassie.VelocityShifter velocity: 3500. - !lassie.WavePacketIFC name: 'S' weight: 1.0 fmin: 1.0 fmax: 10.0 fsmooth_factor: 0.1 shifter: !lassie.VelocityShifter velocity: 2500. ## Whether to divide image function frames by their mean value sharpness_normalization: true ## Threshold on detector function detector_threshold: 100.0 ## Output filename for detections detections_path: 'detections.txt'
Make sure, that the entries for
stations_path point to the
respective directory and file.
An example event from the dataset is shown here:
Typical regional, shallow earthquakes appear in this dataset as clear P and S
coda wave packets travelling with characteristic speeds, packet durations and
frequency content through the network. To detect such events automatically, we
can compose an image function with two contributions, one for each dominant
phase. Such image function contributions (IFCs) can be defined under the
image_function_contributions key in Lassie's configuration. Different types
of IFCs are available and can be combined. Here, we use the
which applies a normalized envelope based pre-processing to the waveforms
before travel-time compensated stacking.
To test the configuration, select a short processing time span around one of
the known events in the dataset. The time span can be restricted with the
tmax entries in Lassie's configuration or with corresponding
command line options
--tmin='YYYY-MM-DD HH:MM:SS' and
--tmax='YYYY-MM-DD HH:MM:SS.XXX', which override configuration settings.
Now run the detector with
lassie scan config.yaml
After the detection has finished have a look at the results using Pyrocko's Snuffler:
lassie snuffle config.yaml
Snuffler opens loading waveform data together with station meta data and detections as event markers. Load a reference catalog to compare the detections to and scrutinize detection performance at different detection thresholds, interactively.