seisscan 0.1.0

A package to detect and locate microearthquakes.

Welcome to SeisScan's documentation!

SeisScan is an open source Python package to detect and locate microearthquakes. This package adopts the method developed in the article Roy et al., (2024). The method leverages the signal coherence across clusters of seismic stations to generate characteristic functions that are backprojected (migrated) to detect and locate seismic events.

For a tutorial on how to use the package, please refer to the documentation available on the website (ReadTheDocs).

Installation

SeisScan is currently running on Mac OS. SeisScan runs on Python 3.10 and up. We recommend you use the latest version of python 3 if possible.

Install via Pip

$ pip install seisscan

Install via Anaconda

conda create -n seisscan python=3.10
conda activate seisscan
conda -c conda-forge obspy=1.4.0 pandas=2.2.2 numpy=1.26.4 utm=0.7.0 dask=2024.8.0 distributed=2024.8.0 jupyter=1.0.0

Usage

from obspy import UTCDateTime
import seisscan as ss

# Read example data
event_dict, st, inventory, subnetworks, model_name = ss.read_example()

# Extract event information
evt0 = UTCDateTime(event_dict["evt0"])    # event origin time
evlo = event_dict["evlo"]                 # event longitude
evla = event_dict["evla"]                 # event latitude
evdp = event_dict["evdp"]                 # event depth (km)

# Plot record section
fig = ss.prs(st.select(channel="DPZ"),
             evt0, evlo, evla, evdp, scale=0.1, model_name=model_name,
             xmin=0.0, xmax=6.0, width=15, height=6, handle=True)

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License

SeisScan is licensed under the terms of the MIT license.

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Author

Ketan Singha Roy
PhD Student
Department of Earth Sciences
Southern Methodist University
Dallas, Texas, USA

Email: [e-mail], [e-mail]

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Citing

If you make use of this code in published work, please cite:

Ketan Singha Roy, Stephen Arrowsmith, Brian Stump, Chris Hayward, Junghyun Park; Exploiting Signal Coherence to Simultaneously Detect and Locate Earthquakes. Seismological Research Letters 2024; doi: https://doi.org/10.1785/0220240089

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