fatiando.gravmag.imaging
)¶Imaging methods for potential fields.
Implements some of the methods described in Fedi and Pilkington (2012). Most methods convert the observed data (gravity, magnetic, etc) into a physical property distribution (density, magnetization, etc). Most methods require gridded data to work.
geninv
: The Generalized Inverse solver in
the frequency domain (Cribb, 1976)sandwich
: Sandwich model (Pedersen, 1991).
Uses depth weighting as in Pilkington (1997)migrate
: 3D potential field migration
(Zhdanov et al., 2011). Actually uses the formula of Fedi and Pilkington
(2012), which are comprehensible.Warning
Most of these methods provide estimates of physical property values that are completely out of scale (mostly due to depth weighting). Therefore, I don’t recommend using the actual values of the physical properties for anything other than finding an approximate location for the sources.
Note
If you want the estimate physical property values in SI units, you
must pass the data also in SI units! Use the unit conversion functions in
fatiando.utils
References
Cribb, J. (1976), Application of the generalized linear inverse to the inversion of static potential data, Geophysics, 41(6), 1365, doi:10.1190/1.1440686
Fedi, M., and M. Pilkington (2012), Understanding imaging methods for potential field data, Geophysics, 77(1), G13, doi:10.1190/geo2011-0078.1
Pedersen, L. B. (1991), Relations between potential fields and some equivalent sources, Geophysics, 56(7), 961, doi:10.1190/1.1443129
Pilkington, M. (1997), 3-D magnetic imaging using conjugate gradients, Geophysics, 62(4), 1132, doi:10.1190/1.1444214
Zhdanov, M. S., X. Liu, G. A. Wilson, and L. Wan (2011), Potential field migration for rapid imaging of gravity gradiometry data, Geophysical Prospecting, 59(6), 1052-1071, doi:10.1111/j.1365-2478.2011.01005.x
fatiando.gravmag.imaging.
geninv
(x, y, z, data, shape, zmin, zmax, nlayers)[source]¶Generalized Inverse imaging in the frequency domain (Cribb, 1976).
Calculates a physical property distribution given potential field data on a regular grid.
Note
Only works on gravity data for now.
Note
The data must be leveled, i.e., on the same height!
Note
The coordinate system adopted is x->North, y->East, and z->Down
Warning
The Generalized Inverse does not use depth weights. This means that the solution will tend to be concentrated on the surface!
Parameters:
The x and y coordinates of the grid points
The z coordinate of the grid points
The potential field at the grid points
The shape of the grid
The top and bottom, respectively, of the region where the physical property distribution is calculated
The number of layers used to divide the region where the physical property distribution is calculated
Returns:
fatiando.mesher.PrismMesh
The estimated physical property distribution set in a prism mesh (for easy 3D plotting)
fatiando.gravmag.imaging.
migrate
(x, y, z, gz, zmin, zmax, meshshape, power=0.5, scale=1)[source]¶3D potential field migration (Zhdanov et al., 2011).
Actually uses the formula of Fedi and Pilkington (2012), which are comprehensible.
Note
Only works on gravity data for now.
Note
The data do not need to be leveled or on a regular grid.
Note
The coordinate system adopted is x->North, y->East, and z->Down
Parameters:
The x and y coordinates of the grid points
The z coordinate of the grid points
The gravity anomaly data at the grid points
The top and bottom, respectively, of the region where the physical property distribution is calculated
Number of prisms in the output mesh in the x, y, and z directions, respectively
The power law used for the depth weighting. This controls what depth the bulk of the solution will be.
A scale factor for the depth weights. Simply changes the scale of the physical property values.
Returns:
fatiando.mesher.PrismMesh
The estimated physical property distribution set in a prism mesh (for easy 3D plotting)
fatiando.gravmag.imaging.
sandwich
(x, y, z, data, shape, zmin, zmax, nlayers, power=0.5)[source]¶Sandwich model (Pedersen, 1991).
Calculates a physical property distribution given potential field data on a regular grid. Uses depth weights.
Note
Only works on gravity data for now.
Note
The data must be leveled, i.e., on the same height!
Note
The coordinate system adopted is x->North, y->East, and z->Down
Parameters:
The x and y coordinates of the grid points
The z coordinate of the grid points
The potential field at the grid points
The shape of the grid
The top and bottom, respectively, of the region where the physical property distribution is calculated
The number of layers used to divide the region where the physical property distribution is calculated
The power law used for the depth weighting. This controls what depth the bulk of the solution will be.
Returns:
fatiando.mesher.PrismMesh
The estimated physical property distribution set in a prism mesh (for easy 3D plotting)