StdLoc¶
Generic locator plugin for SeisComP.
Description¶
StdLoc is a SeisComP locator plugin that combines standard location methods and was developed with the focus on local seismicity, although the methods are generic enough to work at larger scales as well.
Plugin¶
To enable StdLoc the plugin stdloc
must be loaded.
How does it work?¶
The locator can apply a multitude of location methods and it is particularly useful to combine them to achieve better solutions:
LeastSquares: this is the classic algorithm that solves the linearized problem of travel time residual minimization via iterative least squares. However an initial location estimate is required. This is the intended method to select when StdLoc is used in combination with a pick associator: it provides the initial location estimate and StdLoc will improve it. When used in scolv or screloc, the location of the origin to be relocated is used as starting estimate. The configuration doesn’t require any mandatory parameters:
method = LeastSquares
GridSearch: finds the source parameters by evaluating the hypocenter probability of each point in a grid and returning the maximum likelihood hypocenter. Because the search space is fully evaluated there is no need for an initial location estimate and the location uncertainty is completely known. However the method is very slow. It can be used to relocate events in scolv that seem difficult to locate via other methods or to verify the uncertainty of a solution. The following example configuration computes a grid search around the average location of the picked stations. The grid points are spaced apart 0.5km horizontally and 2km vertically.
method = GridSearch GridSearch.center = auto,auto,15 GridSearch.size = 40,40,30 GridSearch.numPoints = 81,81,16
GridSearch+LeastSquares: this method can be used in very complex networks where a bad initial location estimates can get LeastSquares stuck in a local minimum. The method finds a LeastSquares solution for each cell in a (coarse) grid, using the cell centroid as initial location estimate. If finally returns the maximum likelihood solution. This method is intended to be used in screloc or scolv to relocate existing events. The following example configuration returns the best among the 75 (5x5x3) LeastSquares solutions, computed for every point in the grid.
method = GridSearch+LeastSquares GridSearch.center = auto,auto,15 GridSearch.size = 100,100,30 GridSearch.numPoints = 5,5,3
OctTree: this method produces similar results to GridSearch but it is extremely faster and it follows the NonLinLoc approach. The OctTree search starts by evaluating the hypocenter probability of each cell in a grid, computed as the probability density at the cell center coordinates times the cell volume. The search then continues by repeatedly fetching the cell with highest probability and splitting it in 8 sub-cells. These 8 cells are then inserted in the pool of cells to fetch from at next iteration. The search terminates after either a maximum number of iterations or after reaching a minimum cell size. At that point the maximum likelihood hypocenter is selected. Because the algorithms splits only the cells with higher probability, the search space is sampled in a very efficient way and it makes the method way faster than GridSearch. This method is intended to be used in screloc or scolv to relocate existing events. The following example is a plausible configuration for the entire Swiss network:
method = OctTree GridSearch.center = 47.0,8.5,50 GridSearch.size = 700,700,100 GridSearch.numPoints = 21,21,11 OctTree.maxIterations = 100000 OctTree.minCellSize = 0.001
However in this example we are at the size limit for a flat earth study geometry and for bigger regions GridSearch.center should be set to auto and GridSearch.size to a smaller size.
OctTree+LeastSquares: this method allows the OctTree search to find the maximum probability cell in the network and uses that as the initial location estimate for LeastSquares. This method is intended to be used in screloc or scolv to relocate existing events. The following example is a plausible configuration for the entire Swiss network:
method = OctTree+LeastSquares GridSearch.center = 47.0,8.5,50 GridSearch.size = 700,700,100 GridSearch.numPoints = 21,21,11 OctTree.maxIterations = 10000 OctTree.minCellSize = 1.0
However in this example we are at the size limit for a flat earth study geometry and for bigger regions GridSearch.center should be set to auto and GridSearch.size to a smaller size.
The algorithms implemented in StdLoc are standard methods described in “Routine Data Processing in Earthquake Seismology” by Jens Havskov and Lars Ottemoller. The OctTree search algorithm is based on NonLibLoc by Antony Lomax.
Why is stdloc suitable for local seismicity?¶
When dealing with very local seismicity (few kilometers or hundreds of meters) simplifications that are common for regional seismicity have to be removed. In particular the locator should take into consideration:
station elevation and even negative elevation (e.g. borehole sensors)
earthquake location can be above a seismic sensor (e.g. borehole sensors)
possible negative earthquake depth (above surface)
More importantly the travel time tables used by the locator must be able to take into consideration all the above too.
Travel Time Table¶
StdLoc can be configured with any Travel Time Table type available in SeisComP, however only the homogeneous type is able to take into consideration station elevation, negative source depth and sources happening above stations. For this reason homogeneous should be the preferred choice when working on very local seismicity and especially with borehole sensors.
Module Configuration¶
- StdLoc.profiles¶
Type: list:string
Defines a list of profiles to make available to the plugin.
Note
StdLoc.profile.$name.*
$name is a placeholder for the name to be used and needs to be added to StdLoc.profiles
to become active.
StdLoc.profiles = a,b
StdLoc.profile.a.value1 = ...
StdLoc.profile.b.value1 = ...
# c is not active because it has not been added
# to the list of StdLoc.profiles
StdLoc.profile.c.value1 = ...
- StdLoc.profile.$name.method¶
Default:
LeastSquares
Type: string
Values:
LeastSquares,GridSearch,OctTree,GridSearch+LeastSquares,OctTree+LeastSquares
The location method to use: LeastSquares, GridSearch, OctTree, GridSearch+LeastSquares or OctTree+LeastSquares.
- StdLoc.profile.$name.tableType¶
Default:
LOCSAT
Type: string
Travel time table format type.
- StdLoc.profile.$name.tableModel¶
Default:
iasp91
Type: string
The model to be used. The format depends on tableType.
- StdLoc.profile.$name.PSTableOnly¶
Default:
true
Type: boolean
If enabled the arrival travel time information are fetched using ‘P’ and ‘S’ tables only and the user selected phase type is not considered (e.g Pg, Sg, PmP, SmS, P1, S1, etc).
- StdLoc.profile.$name.usePickUncertainties¶
Default:
false
Type: boolean
Whether to use pick time uncertainties rather than a fixed time error. If true, then the uncertainties are retrieved from each individual picks and a weight associated accordingly to ‘pickUncertaintyClasses’ parameter.
- StdLoc.profile.$name.pickUncertaintyClasses¶
Default:
0.000,0.025,0.050,0.100,0.200,0.400
Type: list:string
Unit: s
Define the pick time uncertainty classes from which the pick weight is computed. This parameter defines a list of uncertainty time intervals: the interval in which a pick time uncertainty falls defines the pick class e.g. a pick wich uncertainty of 0.150 falls in the 4th interval and the pick class is then 4. if pick uncertainties are absent, the lowest class will be used. The pick weight then is computed as: 1 / 2^(class-1)
- StdLoc.profile.$name.confLevel¶
Default:
0.9
Type: double
Confidence level, between 0.5 and 1.0, used in computing the confidence ellipsoid.
- StdLoc.profile.$name.enableConfidenceEllipsoid¶
Default:
false
Type: boolean
Compute the confidence ellipsoid for the location. This is optional since if it is not required it can be disabled to save some computation.
Note
StdLoc.profile.$name.GridSearch.* Parameters controlling the GridSearch and OctTree methods.
- StdLoc.profile.$name.GridSearch.center¶
Default:
auto,auto,20
Type: list:string
Unit: deg,deg,km
Grid center defined as: latitude,longitude,depth. The special value "auto" can be used and the corresponding latitude, longitude and/or depth will be automatically computed as the average of the arrival station locations.
- StdLoc.profile.$name.GridSearch.size¶
Default:
40,40,30
Type: list:string
Unit: km
Grid size in km defined as: X,Y,Z direction extents around the ‘GridSearch.center’, where X is the longitudinal extent, Y the latitudinal extent and Z the vertical extent.
- StdLoc.profile.$name.GridSearch.numPoints¶
Type: list:string
Grid number of points in the X, Y, Z direction defined as: numX,numY,numZ. The first and last point will lie on the grid boundary unless the number of points is 1 and the point will be in the grid center.
- StdLoc.profile.$name.GridSearch.misfitType¶
Default:
L1
Type: string
Values:
L1,L2
The type of misfit to use, from which the likelyhood function is derived: L1 or L2 norm. L1 is less sensitive to outliers and so more suitable with automatic picks, L2 is the preferred choice for manual picks.
- StdLoc.profile.$name.GridSearch.travelTimeError¶
Default:
0.25
Type: double
Unit: s
Typical error in seconds for travel-time to one station. This value affects the uncertainty of the location. In OctTree it also influences the probability density computation: a too conservative value increases the number of iterations required by OctTree to converge to a high resolution solution.
Note
StdLoc.profile.$name.OctTree.* Parameters controlling the OctTree method. This method uses the parameters defined in GridSearch, but applies the OctTree search algorithm to the grid. The starting cells of the OctTree search are created by dividing the initial grid in equally sized cells. The number of cells in each direction is ‘GridSearch.numPoints’ - 1, since the grid points becomes the cell vertices.
- StdLoc.profile.$name.OctTree.maxIterations¶
Default:
50000
Type: int
Number of iterations after which the search stops. A zero or negative value disable this stopping mechanism.
- StdLoc.profile.$name.OctTree.minCellSize¶
Default:
0.1
Type: double
Unit: km
Minimum cell size to be generate by the OctTree search to stop. A zero or negative value disable this stopping mechanism.
Note
StdLoc.profile.$name.LeastSquares.* Parameters controlling the LeastSquares method.
- StdLoc.profile.$name.LeastSquares.depthInit¶
Default:
20
Type: double
The initial depth estimate, used only when method is ‘LeastSquares’ and no initial location is provided.
- StdLoc.profile.$name.LeastSquares.iterations¶
Default:
20
Type: int
Number of iterations. Each iteration will use the location and time from the previous Least Square solution.
- StdLoc.profile.$name.LeastSquares.dampingFactor¶
Default:
0
Type: double
Damping factor value to be used when solving the equation system. 0 menas no damping factor.
- StdLoc.profile.$name.LeastSquares.solverType¶
Default:
LSMR
Type: string
Algorithm to use: either LSMR or LSQR