import numpy as np
from fracspy.location.utils import get_max_locs
# Dictionary of the MT components values
MT_comp_dict = [{'elementID': 0, 'laymans': 'xx', 'pq': [0, 0], 'ODscaler': 1, 'MCweighting': 1},
{'elementID': 1, 'laymans': 'yy', 'pq': [1, 1], 'ODscaler': 1, 'MCweighting': 1},
{'elementID': 2, 'laymans': 'zz', 'pq': [2, 2], 'ODscaler': 1, 'MCweighting': 1},
{'elementID': 3, 'laymans': 'xy', 'pq': [0, 1], 'ODscaler': 2, 'MCweighting': 1},
{'elementID': 4, 'laymans': 'xz', 'pq': [0, 2], 'ODscaler': 2, 'MCweighting': 1},
{'elementID': 5, 'laymans': 'yz', 'pq': [1, 2], 'ODscaler': 2, 'MCweighting': 1},
]
[docs]
def get_mt_at_loc(mt_image_set, location_indices):
"""Moment tensor at specific location
Extract moment tensor at a given location from moment tensor component images
Parameters
----------
mt_image_set : :obj:`numpy.ndarray`
Moment tensor component images of size :math:`n_c \times n_x \times n_y \times n_z`
location_indices : :obj:`tuple`
Indices of location to extract moment tensor from the moment tensor component images
Returns
-------
M : :obj:`tuple`
Extracted moment tensor
"""
mxx = mt_image_set[0][location_indices[0], location_indices[1], location_indices[2]]
myy = mt_image_set[1][location_indices[0], location_indices[1], location_indices[2]]
mzz = mt_image_set[2][location_indices[0], location_indices[1], location_indices[2]]
mxy = mt_image_set[3][location_indices[0], location_indices[1], location_indices[2]]
mxz = mt_image_set[4][location_indices[0], location_indices[1], location_indices[2]]
myz = mt_image_set[5][location_indices[0], location_indices[1], location_indices[2]]
M = (mxx, myy, mzz, mxy, mxz, myz)
return M
[docs]
def get_mt_max_locs(mt_image_set, n_max=50, rem_edge=True, edgebuf=10):
"""Source location from moment tensor component images
Compute the source location from the sum of the absolute values of the
six moment tensor component images.
Parameters
----------
mt_image_set : :obj:`numpy.ndarray`
Moment tensor component images of size :math:`n_c \times n_x \times n_y \times n_z`
n_max : :obj:`int`, optional
Number of maximum values to extract (if ``n_max>1``, the centroid of these values
will be computed and provided as the estimated source location)
rem_edge : :obj:`bool`, optional
Remove edges of volume
edgebuf : :obj:`int`, optional
Number of grid points to remove from each edge if ``rem_edge=True``
Returns
-------
ev_loc : :obj:`tuple`
Most likely source location
ev_locs : :obj:`tuple`
`n_max` most likely source locations
"""
energy_images = np.sum(np.abs(mt_image_set), axis=0)
ev_loc, ev_locs = get_max_locs(energy_images,
n_max=n_max,
rem_edge=rem_edge,
edgebuf=edgebuf,
absval=False)
return ev_loc, ev_locs
[docs]
def get_magnitude(mt):
"""Seismic moment and local magnitude
Determine seismic moment ``m0`` and local magnitude ``mw``
from moment tensor array.
Parameters
----------
mt : :obj:`numpy.ndarray`
Moment tensor
Returns
-------
m0 : :obj:`tuple`
Seismic moment
mw : :obj:`tuple`
Local magnitude
"""
mt_array = np.array(mt)
m0 = np.sqrt(np.sum(mt_array ** 2))
mw = ((2 / 3) * (np.log10(m0) - 9.1))
return m0, mw
