harmonica.EquivalentSources#

class harmonica.EquivalentSources(damping=None, points=None, depth: float | str = 'default', block_size=None, parallel=True, dtype='float64')[source]#

Equivalent sources for generic harmonic functions (gravity, magnetics).

These equivalent sources can be used for:

Cartesian coordinates (geographic coordinates must be project before use)
Gravity and magnetic data (including derivatives)
Single data types
Interpolation
Upward continuation
Finite-difference based derivative calculations

They cannot be used for:

Regional or global data where Earth’s curvature must be taken into account
Joint inversion of multiple data types (e.g., gravity + gravity gradients)
Reduction to the pole of magnetic total field anomaly data
Analytical derivative calculations

By default, the point sources are located beneath the observed potential-field measurement points [Cordell1992] that are passed as arguments to the EquivalentSources.fit method, producing the same number of sources as data points. Alternatively, we can reduce the number of sources by using block-averaged sources [Soler2021]: we divide the data region in blocks of equal size and compute the median location of the observations points that fall under each block. Then, we locate one point source beneath each one of these locations. The size of the blocks, that indirectly controls how many sources will be created, can be specified through the block_size argument. We recommend choosing a block_size no larger than the resolution of the grid where interpolations will be carried out.

The depth of the sources can be controlled by the depth argument. Each source is located beneath each data point or block-averaged location at a depth equal to its elevation minus the value of the depth_ attribute. If "default" is passed to the depth argument, then the depth_ attribute is set to 4.5 times the mean distance between first neighboring sources. If a numerical value is passed to the depth argument, then this is the one used for the depth_ attribute. A positive value of depth_ locates sources _beneath_ the data points or the block-averaged locations, thus a negative depth_ will put the sources _above_ them.

Custom source locations can be chosen by specifying the points argument, in which case the block_size and depth arguments will be ignored.

The corresponding coefficient for each point source is estimated through linear least-squares with damping (Tikhonov 0th order) regularization.

The Green’s function for point mass effects used is the inverse Euclidean distance between the observation points and the point sources:

\[\phi(\bar{x}, \bar{x}') = \frac{1}{||\bar{x} - \bar{x}'||}\]

where $\bar{x}$ and $\bar{x}'$ are the coordinate vectors of the observation point and the source, respectively.

Parameters:

dampingNone or float: The positive damping regularization parameter. Controls how much smoothness is imposed on the estimated coefficients. If None, no regularization is used.
pointsNone or list of arrays (optional): List containing the coordinates of the equivalent point sources. Coordinates are assumed to be in the following order: (easting, northing, upward). If None, will place one point source below each observation point at a fixed relative depth below the observation point [Cordell1992]. Defaults to None.
depthfloat or “default”: Parameter used to control the depth at which the point sources will be located. If a non-zero value is provided, each source is located beneath each data point (or block-averaged location) at a depth equal to its elevation minus the depth value. If set to "default", the depth of the sources will be estimated as 4.5 times the mean distance between first neighboring sources. This parameter is ignored if points is specified. Defaults to "default".
block_size: float, tuple = (s_north, s_east) or None: Size of the blocks used on block-averaged equivalent sources. If a single value is passed, the blocks will have a square shape. Alternatively, the dimensions of the blocks in the South-North and West-East directions can be specified by passing a tuple. If None, no block-averaging is applied. This parameter is ignored if points are specified. Default to None.
parallelbool: If True any predictions and Jacobian building is carried out in parallel through Numba’s jit.prange, reducing the computation time. If False, these tasks will be run on a single CPU. Default to True.
dtypedata-type: The desired data-type for the predictions and the Jacobian matrix. Default to "float64".

References

[Soler2021]

Attributes:

points_2d-array: Coordinates of the equivalent point sources.
coefs_array: Estimated coefficients of every point source.
depth_float or None: Estimated depth of the sources calculated as 4.5 times the mean distance between first neighboring sources. This attribute is set to None if points is passed.
region_tuple: The boundaries ([W, E, S, N]) of the data used to fit the interpolator. Used as the default region for the grid method.

Methods

`filter`(coordinates, data[, weights])	Filter the data through the gridder and produce residuals.
`fit`(coordinates, data[, weights])	Fit the coefficients of the equivalent sources.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`grid`(coordinates[, dims, data_names, projection])	Interpolate the data onto a regular grid.
`jacobian`(coordinates, points)	Make the Jacobian matrix for the equivalent sources.
`predict`(coordinates)	Evaluate the estimated equivalent sources on the given set of points.
`profile`(point1, point2, upward, size[, ...])	Interpolate data along a profile between two points.
`scatter`([region, size, random_state, dims, ...])
`score`(coordinates, data[, weights])	Score the gridder predictions against the given data.
`set_fit_request`(*[, coordinates, data, weights])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, coordinates])	Configure whether metadata should be requested to be passed to the `predict` method.
`set_score_request`(*[, coordinates, data, ...])	Configure whether metadata should be requested to be passed to the `score` method.

EquivalentSources.filter(coordinates, data, weights=None)#

Filter the data through the gridder and produce residuals.

Calls fit on the data, evaluates the residuals (data - predicted data), and returns the coordinates, residuals, and weights.

Not very useful by itself but this interface makes gridders compatible with other processing operations and is used by verde.Chain to join them together (for example, so you can fit a spline on the residuals of a trend).

Parameters:

coordinatestuple of arrays: Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, …). For the specific definition of coordinate systems and what these names mean, see the class docstring.
dataarray or tuple of arrays: The data values of each data point. If the data has more than one component, data must be a tuple of arrays (one for each component).
weightsNone or array or tuple of arrays: If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None).

Returns:

coordinates, residuals, weights: The coordinates and weights are same as the input. Residuals are the input data minus the predicted data.

EquivalentSources.fit(coordinates, data, weights=None)[source]#

Fit the coefficients of the equivalent sources.

The data region is captured and used as default for the grid method.

All input arrays must have the same shape.

Parameters:

coordinatestuple of arrays: Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, upward, …). Only easting, northing, and upward will be used, all subsequent coordinates will be ignored.
dataarray: The data values of each data point.
weightsNone or array: If not None, then the weights assigned to each data point. Typically, this should be 1 over the data uncertainty squared.

Returns:

self: Returns this estimator instance for chaining operations.

EquivalentSources.get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

EquivalentSources.get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

EquivalentSources.grid(coordinates, dims=None, data_names=None, projection=None, **kwargs)[source]#

Interpolate the data onto a regular grid.

The coordinates of the regular grid must be passed through the coordinates argument as a tuple containing three arrays in the following order: (easting, nothing, upward). They can be easily created through the verde.grid_coordinates function. If the grid points must be all at the same height, it can be specified in the extra_coords argument of verde.grid_coordinates.

Use the dims and data_names arguments to set custom names for the dimensions and the data field(s) in the output xarray.Dataset. Default names will be provided if none are given.

Parameters:

coordinatestuple of arrays: Tuple of arrays containing the coordinates of the grid in the following order: (easting, northing, upward). The easting and northing arrays could be 1d or 2d arrays, if they are 2d they must be part of a meshgrid. The upward array should be a 2d array with the same shape of easting and northing (if they are 2d arrays) or with a shape of (northing.size, easting.size) (if they are 1d arrays).
dimslist or None: The names of the northing and easting data dimensions, respectively, in the output grid. Default is determined from the dims attribute of the class. Must be defined in the following order: northing dimension, easting dimension. NOTE: This is an exception to the “easting” then “northing” pattern but is required for compatibility with xarray.
data_nameslist of None: The name(s) of the data variables in the output grid. Defaults to ['scalars'].
projectioncallable or None: If not None, then should be a callable object projection(easting, northing) -> (proj_easting, proj_northing) that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. This function will be used to project the generated grid coordinates before passing them into predict. For example, you can use this to generate a geographic grid from a Cartesian gridder.

Returns:

gridxarray.Dataset: The interpolated grid. Metadata about the interpolator is written to the attrs attribute.

See also

verde.grid_coordinates

EquivalentSources.jacobian(coordinates, points)[source]#

Make the Jacobian matrix for the equivalent sources.

Each column of the Jacobian is the Green’s function for a single point source evaluated on all observation points.

Parameters:

coordinatestuple of arrays: Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, upward). Each array must be 1D.
pointstuple of arrays: Tuple of arrays containing the coordinates of the equivalent point sources in the following order: (easting, northing, upward). Each array must be 1D.

Returns:

jacobian2D array: The (n_data, n_points) Jacobian matrix.

EquivalentSources.predict(coordinates)[source]#

Evaluate the estimated equivalent sources on the given set of points.

Requires a fitted estimator (see fit).

Parameters:

coordinatestuple of arrays: Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, upward, …). Only easting, northing and upward will be used, all subsequent coordinates will be ignored.

Returns:

dataarray: The data values evaluated on the given points.

EquivalentSources.profile(point1, point2, upward, size, dims=None, data_names=None, projection=None, **kwargs)[source]#

Interpolate data along a profile between two points.

Generates the profile along a straight line assuming Cartesian distances and the same upward coordinate for all points. Point coordinates are generated by verde.profile_coordinates. Other arguments for this function can be passed as extra keyword arguments (kwargs) to this method.

Use the dims and data_names arguments to set custom names for the dimensions and the data field(s) in the output pandas.DataFrame. Default names are provided.

Includes the calculated Cartesian distance from point1 for each data point in the profile.

To specify point1 and point2 in a coordinate system that would require projection to Cartesian (geographic longitude and latitude, for example), use the projection argument. With this option, the input points will be projected using the given projection function prior to computations. The generated Cartesian profile coordinates will be projected back to the original coordinate system. Note that the profile points are evenly spaced in projected coordinates, not the original system (e.g., geographic).

Parameters:

point1tuple: The easting and northing coordinates, respectively, of the first point.
point2tuple: The easting and northing coordinates, respectively, of the second point.
upwardfloat: Upward coordinate of the profile points.
sizeint: The number of points to generate.
dimslist or None: The names of the northing and easting data dimensions, respectively, in the output dataframe. Default is determined from the dims attribute of the class. Must be defined in the following order: northing dimension, easting dimension. NOTE: This is an exception to the “easting” then “northing” pattern but is required for compatibility with xarray.
data_nameslist of None: The name(s) of the data variables in the output dataframe. Defaults to ['scalars'] for scalar data, ['east_component', 'north_component'] for 2D vector data, and ['east_component', 'north_component', 'vertical_component'] for 3D vector data.
projectioncallable or None: If not None, then should be a callable object projection(easting, northing, inverse=False) -> (proj_easting, proj_northing) that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. Should also take an optional keyword argument inverse (default to False) that if True will calculate the inverse transform instead. This function will be used to project the profile end points before generating coordinates and passing them into predict. It will also be used to undo the projection of the coordinates before returning the results.

Returns:

tablepandas.DataFrame: The interpolated values along the profile.

EquivalentSources.scatter(region=None, size=300, random_state=0, dims=None, data_names=None, projection=None, **kwargs)[source]#: Warning

Not implemented method. The scatter method will be deprecated on Verde v2.0.0.

EquivalentSources.score(coordinates, data, weights=None)#

Score the gridder predictions against the given data.

Calculates the R^2 coefficient of determination of between the predicted values and the given data values. A maximum score of 1 means a perfect fit. The score can be negative.

Warning

The default scoring will change from R² to negative root mean squared error (RMSE) in Verde 2.0.0. This may change model selection results slightly. The negative version will be used to maintain the behaviour of larger scores being better, which is more compatible with current model selection code.

If the data has more than 1 component, the scores of each component will be averaged.

Parameters:

coordinatestuple of arrays: Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, …). For the specific definition of coordinate systems and what these names mean, see the class docstring.
dataarray or tuple of arrays: The data values of each data point. If the data has more than one component, data must be a tuple of arrays (one for each component).
weightsNone or array or tuple of arrays: If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None).

Returns:

scorefloat: The R^2 score

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters:

coordinatesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for coordinates parameter in fit.
datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for data parameter in fit.
weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for weights parameter in fit.

Returns:

selfobject: The updated object.

EquivalentSources.set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

EquivalentSources.set_predict_request(*, coordinates: bool | None | str = '$UNCHANGED$') → EquivalentSources#

Configure whether metadata should be requested to be passed to the predict method.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters:

coordinatesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for coordinates parameter in predict.

Returns:

selfobject: The updated object.

Configure whether metadata should be requested to be passed to the score method.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters:

coordinatesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for coordinates parameter in score.
datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for data parameter in score.
weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for weights parameter in score.

Returns: