Search volume¶

The search volume is the region where Qseek looks for earthquakes; you configure it in the octree of the search. You place it with a center location and its bounds in east, north and depth direction, relative to the center. Qseek divides the volume into root nodes and refines the nodes around detected events, see octree refinement.

Octree refinement — The octree refines around a seismic source, from level 0 with 5977 nodes to level 2 with 6812 nodes. Map view (top) and depth section (bottom) of the semblance; the cross marks the maximum.

Set up the volume¶

Center: set location to the center of your network or of the expected seismicity. The location must not be 0, 0.
Bounds: east_bounds, north_bounds and depth_bounds are in meters, relative to the center. Depth is positive down. Leave a margin around the seismicity: detections at the border of the volume are ignored, see ignore_boundary.
Root nodes: every extent must be a multiple of root_node_size. The number of root nodes sets the cost of the coarse search: the default 20 km × 20 km × 20 km volume with 1 km root nodes has 8000 root nodes.
Resolution: the smallest node size is root_node_size / 2**(n_levels - 1). The defaults, 1 km root nodes and 5 levels, refine down to 62.5 m.

Tip

Qseek stacks every root node in every window, but refines only the nodes around detections. For a finer resolution, add levels rather than shrinking the root nodes.

Octree

{
  "location": {
    "lat": 52.38,
    "lon": 13.06,
    "east_shift": 0.0,
    "north_shift": 0.0,
    "elevation": 0.0,
    "depth": 0.0
  },
  "root_node_size": 1000.0,
  "n_levels": 5,
  "east_bounds": [
    -10000.0,
    10000.0
  ],
  "north_bounds": [
    -10000.0,
    10000.0
  ],
  "depth_bounds": [
    0.0,
    20000.0
  ]
}

Octree `pydantic-model` ¶

Bases: BaseModel, Iterator[Node], Sequence[Node]

The search volume, divided into an octree of nodes.

The volume is set by its center location and its bounds in east, north and depth direction, and divided into root nodes of root_node_size. Around detected events, Qseek splits nodes into eight smaller nodes to refine the localization.

Config:

ignored_types: (cached_property,)

Fields:

Validators:

check_reference → location
check_limits

location `pydantic-field` ¶

location: Location = Location(lat=0.0, lon=0.0)

The geographical center of the octree.

root_node_size `pydantic-field` ¶

root_node_size: PositiveFloat = 1 * KM

Size of the root node at the initial level (level 0) in meters.

n_levels `pydantic-field` ¶

n_levels: int = 5

Number of levels of the octree, including the root level. The smallest node size, and the resolution of the localization, is root_node_size / 2**(n_levels - 1).

east_bounds `pydantic-field` ¶

east_bounds: Range = Range(-10 * KM, 10 * KM)

East bounds of the octree in meters.

north_bounds `pydantic-field` ¶

north_bounds: Range = Range(-10 * KM, 10 * KM)

North bounds of the octree in meters.

depth_bounds `pydantic-field` ¶

depth_bounds: Range = Range(0 * KM, 20 * KM)

Depth bounds of the octree in meters.

n_nodes `cached` `property` ¶

n_nodes: int

Number of nodes in the octree.

n_leaf_nodes `cached` `property` ¶

n_leaf_nodes: int

Number of nodes in the octree.

nodes `property` ¶

nodes: list[Node]

List of nodes in the octree.

volume `property` ¶

volume: float

Volume of the octree in cubic meters.

effective_depth_bounds `property` ¶

effective_depth_bounds: Range

Effective depth bounds of the octree in meters.

leaf_nodes `cached` `property` ¶

leaf_nodes: list[Node]

Get all leaf nodes of the octree.

Returns:

Type	Description
`list[Node]`	list[Node]: List of leaf nodes.

semblance `property` ¶

semblance: ndarray

Returns the semblance values of all nodes.

check_limits `pydantic-validator` ¶

check_limits() -> Octree

Check that the size limits are valid.

extent ¶

extent() -> tuple[float, float, float]

Returns the extent of the octree.

Returns:

Type	Description
`tuple[float, float, float]`	tuple[float, float, float]: EW, NS and depth extent of the octree in meters.

reset ¶

reset() -> Self

Reset the octree to its initial state and return it.

clear ¶

clear() -> None

Clear the octree's cached data.

set_level ¶

set_level(level: int) -> None

Set the octree to a specific level.

Parameters:

Name	Type	Description	Default
`level`	`int`	Level to set the octree to.	required

reduce_axis ¶

reduce_axis(
    surface: Literal["NE", "ED", "ND"] = "NE",
    max_level: int = -1,
    accumulator: Callable[ndarray] = max,
) -> ndarray

Reduce the octree's nodes to the surface.

Parameters:

Name	Type	Description	Default
`surface`	`Literal['NE', 'ED', 'ND']`	Surface to reduce to. Defaults to "NE".	`'NE'`
`max_level`	`int`	Maximum level to reduce to. Defaults to -1.	`-1`
`accumulator`	`Callable`	Accumulator function. Defaults to np.max.	`max`

Returns:

Type	Description
`ndarray`	np.ndarray: Of shape (n-nodes, 4) with columns (east, north, depth, value).

map_semblance ¶

map_semblance(
    semblance: ndarray, leaf_only: bool = True
) -> None

Maps semblance values to nodes.

Parameters:

Name	Type	Description	Default
`semblance`	`ndarray`	Of shape (n-nodes,).	required
`leaf_only`	`bool`	If True, only leaf nodes are mapped. Defaults to True.	`True`

distances_stations ¶

distances_stations(stations: StationInventory) -> ndarray

Returns the 3D distances from all nodes to all stations.

Parameters:

Name	Type	Description	Default
`stations`	`Stations`	Stations to calculate distance to.	required

Returns:

Type	Description
`ndarray`	np.ndarray: Of shape (n-nodes, n-stations).

distances_stations_surface ¶

distances_stations_surface(
    stations: StationInventory,
) -> ndarray

Returns the surface distance from all nodes to all stations.

Parameters:

Name	Type	Description	Default
`stations`	`Stations`	Stations to calculate distance to.	required

Returns:

Type	Description
`ndarray`	np.ndarray: Distances in shape (n-nodes, n-stations).

get_nodes ¶

get_nodes(indices: Iterable[int]) -> list[Node]

Retrieves a list of nodes from the octree based on the given indices.

Parameters:

Name	Type	Description	Default
`indices`	`Iterable[int]`	The indices of the nodes to retrieve.	required

Returns:

Type	Description
`list[Node]`	list[Node]: A list of nodes corresponding to the given indices.

get_nodes_by_threshold ¶

get_nodes_by_threshold(
    semblance_threshold: float = 0.0,
) -> list[Node]

Get all nodes with a semblance above a threshold.

Parameters:

Name	Type	Description	Default
`semblance_threshold`	`float`	Semblance threshold. Default is 0.0.	`0.0`

Returns:

Type	Description
`list[Node]`	list[Node]: List of nodes.

get_nodes_level ¶

get_nodes_level(level: int = 0) -> list[Node]

Get all nodes at a specific level.

Parameters:

Name	Type	Description	Default
`level`	`int`	Level to get nodes from.	`0`

Returns:

Type	Description
`list[Node]`	list[Node]: List of nodes.

get_node_size ¶

get_node_size(level: int = 0) -> float

Get the size of a node at a specific level.

Parameters:

Name	Type	Description	Default
`level`	`int`	Level to get node size from.	`0`

Returns:

Name	Type	Description
`float`	`float`	Size of the node.

smallest_node_size ¶

smallest_node_size() -> float

Returns the smallest possible node size.

Returns:

Name	Type	Description
`float`	`float`	Smallest possible node size.

total_number_nodes ¶

total_number_nodes() -> int

Returns the total number of nodes of all levels.

Returns:

Name	Type	Description
`int`	`int`	Total number of nodes.

interpolate_max_semblance `async` ¶

interpolate_max_semblance(peak_node: Node) -> Location

Interpolate the location of the maximum semblance value.

This method calculates the location of the maximum semblance value by performing interpolation using surrounding nodes. It uses the scipy Rbf (Radial basis function) interpolation method to fit a smooth function to the given data points. The function is then minimized to find the location of the maximum value.

Returns:

Name	Type	Description
`Location`	`Location`	Location of the maximum semblance value.

Raises:

Type	Description
`AttributeError`	If no semblance values are set.

cached_bottom ¶

cached_bottom() -> Self

Returns a copy of the octree refined to the cached bottom nodes.

Raises:

Type	Description
`EnvironmentError`	If the octree has never been split.

Returns:

Name	Type	Description
`Self`	`Self`	Copy of the octree with cached bottom nodes.

save_pickle ¶

save_pickle(filename: Path) -> None

Save the octree to a pickle file.

Parameters:

Name	Type	Description	Default
`filename`	`Path`	Filename to save to.	required

get_corners ¶

get_corners() -> list[Location]

Get the corners of the octree.

Returns:

Type	Description
`list[Location]`	list[Location]: List of locations.

Search volume¶

Set up the volume¶

Octree pydantic-model ¶

location pydantic-field ¶

root_node_size pydantic-field ¶

n_levels pydantic-field ¶

east_bounds pydantic-field ¶

north_bounds pydantic-field ¶

depth_bounds pydantic-field ¶

n_nodes cached property ¶

n_leaf_nodes cached property ¶

nodes property ¶

volume property ¶

effective_depth_bounds property ¶

leaf_nodes cached property ¶

semblance property ¶

check_limits pydantic-validator ¶

extent ¶

reset ¶

clear ¶

set_level ¶

reduce_axis ¶

map_semblance ¶

distances_stations ¶

distances_stations_surface ¶

get_nodes ¶

get_nodes_by_threshold ¶

get_nodes_level ¶

get_node_size ¶

smallest_node_size ¶

total_number_nodes ¶

interpolate_max_semblance async ¶

cached_bottom ¶

save_pickle ¶

get_corners ¶

Octree `pydantic-model` ¶

location `pydantic-field` ¶

root_node_size `pydantic-field` ¶

n_levels `pydantic-field` ¶

east_bounds `pydantic-field` ¶

north_bounds `pydantic-field` ¶

depth_bounds `pydantic-field` ¶

n_nodes `cached` `property` ¶

n_leaf_nodes `cached` `property` ¶

nodes `property` ¶

volume `property` ¶

effective_depth_bounds `property` ¶

leaf_nodes `cached` `property` ¶

semblance `property` ¶

check_limits `pydantic-validator` ¶

interpolate_max_semblance `async` ¶