167 lines
4.4 KiB
Markdown
167 lines
4.4 KiB
Markdown
# P and S Waves Detection with Deep Learning
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## Installation
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```
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pip install --extra-index-url https://epos-apps.grid.cyfronet.pl/api/packages/epos-ai/pypi/simple epos_ai_picking_tools
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```
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## Generalized Phase Detection (GPD) Model
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### Documentation
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<!-- [[[cog
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import cog
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from epos_ai_picking_tools import cli_gpd as cli
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from click.testing import CliRunner
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runner = CliRunner()
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result = runner.invoke(cli.cli, ["--help"])
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help = result.output.replace("Usage: cli", "Usage: gpd_tool")
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cog.out(
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"```\n{}\n```".format(help)
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)
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]]] -->
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```
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Usage: gpd_tool [OPTIONS] COMMAND [ARGS]...
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Options:
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--help Show this message and exit.
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Commands:
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citation Prints citation of the model
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list-pretrained Show pretrained model names
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pick Detect phases in streams
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version Prints version
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```
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<!-- [[[end]]] -->
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### Pick
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<!-- [[[cog
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import cog
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from epos_ai_picking_tools import cli_gpd as cli
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from click.testing import CliRunner
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runner = CliRunner()
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result = runner.invoke(cli.cli, ["pick", "--help"])
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help = result.output.replace("Usage: cli", "Usage: gpd_tool")
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cog.out(
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"```\n{}\n```".format(help)
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)
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]]] -->
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```
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Usage: gpd_tool pick [OPTIONS] [STREAM_FILE_NAMES]...
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Detect phases in streams
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Options:
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-w, --weights TEXT for possible options see output of 'list-pretrained'
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[default: original]
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-o, --output PATH directory to store results [default: .]
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-s, --stride INTEGER stride in samples for point prediction models
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[default: 10]
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-tp, --threshold-p FLOAT detection threshold for the P phase [default: 0.75]
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-ts, --threshold-s FLOAT detection threshold for the S phase [default: 0.75]
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--help Show this message and exit.
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```
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<!-- [[[end]]] -->
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### Citation
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<!-- [[[cog
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import cog
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from epos_ai_picking_tools import cli_gpd as cli
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from click.testing import CliRunner
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runner = CliRunner()
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result = runner.invoke(cli.cli, ["citation"])
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help = result.output.replace("Usage: cli", "Usage: gpd_tool")
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cog.out(
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"\n{}\n".format(help)
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)
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]]] -->
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Ross, Z. E., Meier, M.-A., Hauksson, E., & Heaton, T. H. (2018). Generalized Seismic Phase Detection with Deep Learning. ArXiv:1805.01075 [Physics]. https://arxiv.org/abs/1805.01075
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<!-- [[[end]]] -->
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## PhaseNet
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### Documentation
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<!-- [[[cog
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import cog
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from epos_ai_picking_tools import cli_phasenet as cli
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from click.testing import CliRunner
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runner = CliRunner()
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result = runner.invoke(cli.cli, ["--help"])
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help = result.output.replace("Usage: cli", "Usage: phasenet_tool")
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cog.out(
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"```\n{}\n```".format(help)
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)
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]]] -->
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```
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Usage: phasenet_tool [OPTIONS] COMMAND [ARGS]...
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Options:
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--help Show this message and exit.
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Commands:
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citation Prints citation of the model
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extras Downloads extra model weights from EPOS AI Platform
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list-pretrained Show pretrained model names
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pick Detect phases in streams
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version Prints version
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```
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<!-- [[[end]]] -->
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### Pick
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<!-- [[[cog
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import cog
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from epos_ai_picking_tools import cli_phasenet as cli
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from click.testing import CliRunner
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runner = CliRunner()
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result = runner.invoke(cli.cli, ["pick", "--help"])
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help = result.output.replace("Usage: cli", "Usage: phasenet_tool")
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cog.out(
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"```\n{}\n```".format(help)
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)
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]]] -->
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```
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Usage: phasenet_tool pick [OPTIONS] [STREAM_FILE_NAMES]...
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Detect phases in streams
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Options:
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-w, --weights TEXT for possible options see output of 'list-pretrained'
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[default: original]
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-o, --output PATH directory to store results [default: .]
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-tp, --threshold-p FLOAT detection threshold for the P phase [default: 0.3]
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-ts, --threshold-s FLOAT detection threshold for the S phase [default: 0.3]
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-b, --blinding TUPLE number of prediction samples to discard on each side
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of each window prediction [default: 0, 0]
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--help Show this message and exit.
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```
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<!-- [[[end]]] -->
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### Citation
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<!-- [[[cog
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import cog
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from epos_ai_picking_tools import cli_phasenet as cli
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from click.testing import CliRunner
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runner = CliRunner()
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result = runner.invoke(cli.cli, ["citation"])
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help = result.output.replace("Usage: cli", "Usage: phasenet_tool")
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cog.out(
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"\n{}\n".format(help)
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)
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]]] -->
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Zhu, W., & Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261-273. https://doi.org/10.1093/gji/ggy423
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<!-- [[[end]]] -->
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