__all__ = [
"Location",
]
import numpy as np
from fracspy.location.migration import diffstack, kmigration
from fracspy.location.imaging import lsi, sparselsi, xcorri
_location_kind = {"kmigration": kmigration,
"diffstack": diffstack,
"lsi": lsi,
"sparselsi": sparselsi,
"xcorri": xcorri,
}
[docs]
class Location():
"""Event location
This class acts as an abstract interface for users to perform
event location on a microseismic dataset.
It assumes that grid vectors are regularly spaced.
Parameters
----------
x : :obj:`numpy.ndarray`
X-axis
y : :obj:`numpy.ndarray`
Y-axis
z : :obj:`numpy.ndarray`
Z-axis
"""
def __init__(self, x, y, z):
self.x, self.y, self.z = x, y, z
self.dx = self.x[1]-self.x[0]
self.dy = self.y[1]-self.y[0]
self.dz = self.z[1]-self.z[0]
self.n_xyz = x.size, y.size, z.size
def apply(self, data, kind="kmigration", **kwargs):
"""Perform event location
This method performs event location for the provided dataset using
the pre-defined acquisition geometry using one of the available imaging techniques.
.. note:: This method can be called multiple times using different input datasets
and/or imaging methods as the internal parameters are not modified during the
location procedure.
Parameters
----------
data : :obj:`numpy.ndarray`
Data of shape :math`n_r \times n_t`
kind : :obj:`str`, optional
Algorithm kind (`diffstack`, `semblancediffstack`,
`lsi`, `sparselsi`, or `xcorri`
kwargs : :obj:`dict`, optional
Keyword arguments to pass to the location algorithm
Returns
-------
im : :obj:`numpy.ndarray`
Migrated volume
hc : :obj:`numpy.ndarray`
Estimated hypocentral location
"""
im, hc = _location_kind[kind](data, self.n_xyz, **kwargs)[:2]
return im, hc
def grid(self):
"""Construct the grid array from the internal grid vectors
This method constructs the grid array of size (3, self.n_xyz)
from the internal grid vectors.
.. note:: This method can be called multiple times as the internal parameters are not modified.
"""
# Create a meshgrid
X, Y, Z = np.meshgrid(self.x, self.y, self.z, indexing='ij')
# Stack the arrays into a (3, self.n_xyz) array
return np.vstack((X.flatten(), Y.flatten(), Z.flatten()))
def indtogrid(self, points:np.ndarray):
"""Return the grid coordinates for points provided as grid indices
This method computes the spatial grid coordinates of points with coordinates provided as grid indices.
Points have shape (3,npoints) where `npoints` is number of points.
.. note:: This method can be called multiple times as the internal parameters are not modified.
"""
return np.array([
self.x[0] + points[0] * self.dx,
self.y[0] + points[1] * self.dy,
self.z[0] + points[2] * self.dz
])
def gridtoind(self, points:np.ndarray):
"""Return the grid indices for points with coordinates on a grid
This method computes the grid indices for point with provided spatial grid coordinates
Points have shape (3,npoints) where `npoints` is number of points.
.. note:: This method can be called multiple times as the internal parameters are not modified.
"""
return np.array([
((points[0] - self.x[0]) / self.dx).astype(int),
((points[1] - self.y[0]) / self.dy).astype(int),
((points[2] - self.z[0]) / self.dz).astype(int)
])
