Station corrections¶

Station corrections add a delay to the modeled travel times of a station. They account for what the velocity model misses, e.g. the local geology below a station. Corrected travel times line up the phase arrivals better: the locations get more precise, and the stack detects more events.

Corrections	Delay	Source
`SimpleCorrections`	Per station and phase	You give the delays
`StationCorrections`	Per station and phase (SST)	Extracted from a previous run
`SourceSpecificStationCorrections`	Per station, phase and source location (SSST)	Extracted from a previous run

station_corrections takes the corrections, or the path to a directory with a corrections.json file.

Extract corrections from a previous run¶

The extracted corrections are statistics of the travel time residuals, the differences between the picked and the modeled arrival times of a previous search:

Run a search without corrections. Its run directory holds the detections with their picks.
Add the corrections to the configuration, with the run directory of the first search in import_rundirs.
Run the search again. Qseek extracts the corrections when it starts and applies them to the travel times.

Station corrections from a previous run

"station_corrections": {
  "corrections": "StationCorrections",
  "import_rundirs": ["my-search/"]
}

Constant corrections¶

Constant delays per station and phase, in seconds. Stations and phases without an entry are not corrected.

Constant station corrections

"station_corrections": {
  "corrections": "SimpleCorrections",
  "stations": {
    "GE.RUE.": {"cake:P": 0.12, "cake:S": 0.2}
  }
}

SimpleCorrections `pydantic-model` ¶

Bases: TravelTimeCorrections

Constant travel time corrections per station and phase.

The station delays are added to the modeled travel times of all source locations.

Fields:

corrections (Literal['SimpleCorrections'])
stations (dict[NSL, dict[PhaseDescription, float]])

stations `pydantic-field` ¶

stations: dict[NSL, dict[PhaseDescription, float]] = {}

Travel time delay in seconds per station and phase, e.g. {"GE.RUE.": {"cake:P": 0.12, "cake:S": 0.2}}. Stations and phases without an entry are not corrected.

get_subclasses `classmethod` ¶

get_subclasses() -> tuple[type[TravelTimeCorrections], ...]

Get the subclasses of this class.

Returns:

Type	Description
`tuple[type[TravelTimeCorrections], ...]`	tuple[type]: The subclasses of this class.

prepare `async` ¶

prepare(
    stations: StationInventory,
    octree: Octree,
    phases: Iterable[PhaseDescription],
    rundir: Path,
) -> None

Prepare the station for the corrections.

Parameters:

Name	Type	Description	Default
`stations`	`Stations`	The station to prepare.	required
`octree`	`Octree`	The octree to use for the preparation.	required
`phases`	`Iterable[PhaseDescription]`	The phases to prepare the station for.	required
`rundir`	`Path`	The run directory of the search.	required

Station-specific corrections¶

Station-specific corrections (SST) are one delay per station and phase, extracted from the travel time residuals of all detections of the previous runs.

Station delay statistics — Statistics of the station delay times.

StationCorrections

{
  "corrections": "StationCorrections",
  "import_rundirs": [
    "."
  ],
  "plot_corrections": false,
  "statistic": "median",
  "weighting": "mul-confidence-semblance",
  "min_num_station_picks": 50,
  "min_distance_border": 500.0,
  "min_num_picks": 3
}

StationCorrections `pydantic-model` ¶

Bases: TravelTimeCorrections

Static station travel time corrections.

Fields:

corrections (Literal['StationCorrections'])
import_rundirs (list[DirectoryPath])
plot_corrections (bool)
statistic (ArrivalStatistic)
weighting (WeightingMethod)
min_num_station_picks (PositiveInt)
min_distance_border (float)
min_num_picks (PositiveInt)

import_rundirs `pydantic-field` ¶

import_rundirs: list[DirectoryPath] = [Path('.')]

Path to rundir, to extract the station corrections from.

plot_corrections `pydantic-field` ¶

plot_corrections: bool = False

Plot the station corrections statistics.

statistic `pydantic-field` ¶

statistic: ArrivalStatistic = 'median'

Arithmetic measure for the traveltime delays. Choose from median and average.

weighting `pydantic-field` ¶

weighting: WeightingMethod = 'mul-confidence-semblance'

Weighting of the traveltime delays. Choose from none, confidence, semblance, add-confidence-semblance and mul-confidence-semblance.

min_num_station_picks `pydantic-field` ¶

min_num_station_picks: PositiveInt = 50

Minimum number of picks at a station required to calculate station corrections.

min_distance_border `pydantic-field` ¶

min_distance_border: float = 500.0

Minimum event distance from the border of the octree grid.

min_num_picks `pydantic-field` ¶

min_num_picks: PositiveInt = 3

Minimum number of picks per event to be included in the statistics.

get_subclasses `classmethod` ¶

get_subclasses() -> tuple[type[TravelTimeCorrections], ...]

Get the subclasses of this class.

Returns:

Type	Description
`tuple[type[TravelTimeCorrections], ...]`	tuple[type]: The subclasses of this class.

get_delay ¶

get_delay(
    station_nsl: NSL,
    phase: PhaseDescription,
    node: Node | None = None,
) -> float

Get the traveltime delay for a station and phase.

The delay is the difference between the observed and predicted traveltime.

Parameters:

Name	Type	Description	Default
`station_nsl`	`NSL`	The station NSL.	required
`phase`	`PhaseDescription`	The phase description.	required
`node`	`Node \| None`	The node to get the delay for. Is ignored for these median and average delays.	`None`

Returns:

Name	Type	Description
`float`	`float`	The traveltime delay in seconds.

get_delays `async` ¶

get_delays(
    station_nsls: Sequence[NSL],
    phase: PhaseDescription,
    nodes: Sequence[Node] = (),
) -> ndarray

Get the traveltime delays for a set of stations and a phase.

Parameters:

Name	Type	Description	Default
`station_nsls`	`Sequence[NSL]`	The stations to get the delays for.	required
`phase`	`PhaseDescription`	The phase to get the delays for.	required
`nodes`	`Sequence[Node]`	The nodes to get the delays for. Is ignored for these median and average delays.	`()`

Returns:

Type	Description
`ndarray`	np.ndarray: The traveltime delays for the given stations and phase.

prepare `async` ¶

prepare(
    stations: Stations,
    octree: Octree,
    phases: Iterable[PhaseDescription],
    rundir: Path,
) -> None

Prepare the station corrections for the console.

Source-specific corrections¶

Source-specific station corrections (SSST) vary with the source location. The delays are calculated on a grid of octree nodes, at the level set by resolution_octree_level, from the weighted travel time residuals of the events within a Gaussian sphere around each node. Between the nodes the delays are interpolated.

SourceSpecificStationCorrections

{
  "corrections": "SourceSpecificStationCorrections",
  "import_rundirs": [],
  "weighting": "mul-confidence-semblance",
  "min_confidence": 10.0,
  "min_distance_border": 500.0,
  "min_num_picks": 6,
  "max_rms": 0.6,
  "spatial_weighting_exponent": 3.0,
  "resolution_octree_level": 0,
  "delay_statistic": "weighted-median",
  "delay_interpolation_method": "linear",
  "export_stations": []
}

SourceSpecificStationCorrections `pydantic-model` ¶

Bases: TravelTimeCorrections

Source specific station corrections, spatial travel time corrections.

Fields:

corrections (Literal['SourceSpecificStationCorrections'])
import_rundirs (list[DirectoryPath])
weighting (WeightingMethod)
min_confidence (PositiveFloat)
min_distance_border (float)
min_num_picks (PositiveInt)
max_rms (float)
spatial_weighting_exponent (float)
resolution_octree_level (int)
delay_statistic (DelayStatistic)
delay_interpolation_method (InterpolationMethod)
export_stations (list[NSL])

import_rundirs `pydantic-field` ¶

import_rundirs: list[DirectoryPath]

Path to rundir, to extract the station corrections from.

weighting `pydantic-field` ¶

weighting: WeightingMethod = 'mul-confidence-semblance'

Weighting of the traveltime delays. Choose from none, confidence, semblance, add-confidence-semblance and mul-confidence-semblance.

min_confidence `pydantic-field` ¶

min_confidence: PositiveFloat = 10.0

Minimum cumulative pick confidence defining sigma of the Gaussian sphere surrouding the node. Distance sigma will start withresolution_octree_level until min_confidence is reached. Individually for each node-station combination.

min_distance_border `pydantic-field` ¶

min_distance_border: float = 500.0

Minimum event distance from the border of the octree grid.

min_num_picks `pydantic-field` ¶

min_num_picks: PositiveInt = 6

Minimum number of picks per event to be included in the statistics. Higher values will result in fewer events.

max_rms `pydantic-field` ¶

max_rms: float = 0.6

Maximum RMS of the travel time residuals for a station to be included.

spatial_weighting_exponent `pydantic-field` ¶

spatial_weighting_exponent: float = 3.0

The exponent of the spatial weighting function around the sphere.

resolution_octree_level `pydantic-field` ¶

resolution_octree_level: int = 0

The octree level (resolution) to use for the station corrections. This is the SSST grid spacing.

delay_statistic `pydantic-field` ¶

delay_statistic: DelayStatistic = 'weighted-median'

Statistical delay aggregation method.

delay_interpolation_method `pydantic-field` ¶

delay_interpolation_method: InterpolationMethod = 'linear'

The interpolation method to use for interpolating delays between nodes.

export_stations `pydantic-field` ¶

export_stations: list[NSL]

List of station NSLs for which debug information will be generated, this includes CSV and VTK files with the station corrections and statistics about the delay

get_subclasses `classmethod` ¶

get_subclasses() -> tuple[type[TravelTimeCorrections], ...]

Get the subclasses of this class.

Returns:

Type	Description
`tuple[type[TravelTimeCorrections], ...]`	tuple[type]: The subclasses of this class.

load_volumes ¶

load_volumes(path: Path) -> None

Load the station correction volumes (.qssst) from a directory.

Parameters:

Name	Type	Description	Default
`path`	`Path`	The path to the directory to load the station corrections from.	required

create_volumes ¶

create_volumes(
    stations: list[tuple[Station, PhaseDescription]],
    octree: Octree,
    export_dir: Path,
) -> list[StationCorrectionVolume]

Create a new source specific station correction.

Parameters:

Name	Type	Description	Default
`station`		The station to prepare.	required
`octree`	`Octree`	The octree to use for the preparation.	required
`phase`		The phase to prepare the station for.	required

add_volume ¶

add_volume(volume: StationCorrectionVolume) -> None

Add a volume to the station corrections.

Parameters:

Name	Type	Description	Default
`phase`		The phase of the volume.	required
`station`		The station of the volume.	required
`volume`	`StationCorrectionVolume`	The volume to add.	required

get_volume ¶

get_volume(
    station_nsl: NSL, phase: PhaseDescription
) -> StationCorrectionVolume

Get the volume for a station and phase.

Parameters:

Name	Type	Description	Default
`phase`	`PhaseDescription`	The phase of the volume.	required
`station`		The station of the volume.	required

Returns:

Name	Type	Description
`StationCorrectionVolume`	`StationCorrectionVolume`	The volume.

has_volume ¶

has_volume(
    station_nsl: NSL | Station, phase: PhaseDescription
) -> bool

Check if the volume for a station and phase exists.

Parameters:

Name	Type	Description	Default
`phase`	`PhaseDescription`	The phase of the volume.	required
`station`		The station of the volume.	required

Returns:

Name	Type	Description
`bool`	`bool`	True if the volume exists, False otherwise.

get_delay ¶

get_delay(
    station_nsl: NSL, phase: PhaseDescription, node: Node
) -> float

Get the traveltime delay for a station and phase.

The delay is the difference between the observed and predicted traveltime.

Parameters:

Name	Type	Description	Default
`station_nsl`	`NSL`	The station NSL.	required
`phase`	`PhaseDescription`	The phase description.	required

Returns:

Name	Type	Description
`float`	`float`	The traveltime delay in seconds.

get_delays `async` ¶

get_delays(
    station_nsls: Sequence[NSL],
    phase: PhaseDescription,
    nodes: Sequence[Node],
) -> ndarray

Get the traveltime delays for a set of stations and a phase.

Parameters:

Name	Type	Description	Default
`station_nsls`	`Sequence[NSL]`	The stations to get the delays for.	required
`phase`	`PhaseDescription`	The phase to get the delays for.	required
`octree`		The octree to use for the delays.	required

Returns:

Type	Description
`ndarray`	np.ndarray: The traveltime delays for the given stations and phase of shape is n_nodes X n_stations

Station corrections¶

Extract corrections from a previous run¶

Constant corrections¶

SimpleCorrections pydantic-model ¶

stations pydantic-field ¶

get_subclasses classmethod ¶

prepare async ¶

Station-specific corrections¶

StationCorrections pydantic-model ¶

import_rundirs pydantic-field ¶

plot_corrections pydantic-field ¶

statistic pydantic-field ¶

weighting pydantic-field ¶

min_num_station_picks pydantic-field ¶

min_distance_border pydantic-field ¶

min_num_picks pydantic-field ¶

get_subclasses classmethod ¶

get_delay ¶

get_delays async ¶

prepare async ¶

Source-specific corrections¶

SourceSpecificStationCorrections pydantic-model ¶

import_rundirs pydantic-field ¶

weighting pydantic-field ¶

min_confidence pydantic-field ¶

min_distance_border pydantic-field ¶

min_num_picks pydantic-field ¶

max_rms pydantic-field ¶

spatial_weighting_exponent pydantic-field ¶

resolution_octree_level pydantic-field ¶

delay_statistic pydantic-field ¶

delay_interpolation_method pydantic-field ¶

export_stations pydantic-field ¶

get_subclasses classmethod ¶

load_volumes ¶

create_volumes ¶

add_volume ¶

get_volume ¶

has_volume ¶

get_delay ¶

get_delays async ¶

SimpleCorrections `pydantic-model` ¶

stations `pydantic-field` ¶

get_subclasses `classmethod` ¶

prepare `async` ¶

StationCorrections `pydantic-model` ¶

import_rundirs `pydantic-field` ¶

plot_corrections `pydantic-field` ¶

statistic `pydantic-field` ¶

weighting `pydantic-field` ¶

min_num_station_picks `pydantic-field` ¶

min_distance_border `pydantic-field` ¶

min_num_picks `pydantic-field` ¶

get_subclasses `classmethod` ¶

get_delays `async` ¶

prepare `async` ¶

SourceSpecificStationCorrections `pydantic-model` ¶

import_rundirs `pydantic-field` ¶

weighting `pydantic-field` ¶

min_confidence `pydantic-field` ¶

min_distance_border `pydantic-field` ¶

min_num_picks `pydantic-field` ¶

max_rms `pydantic-field` ¶

spatial_weighting_exponent `pydantic-field` ¶

resolution_octree_level `pydantic-field` ¶

delay_statistic `pydantic-field` ¶

delay_interpolation_method `pydantic-field` ¶

export_stations `pydantic-field` ¶

get_subclasses `classmethod` ¶

get_delays `async` ¶