pylops.utils.signalprocessing.dip_estimate

pylops.utils.signalprocessing.dip_estimate(d, dz=1.0, dx=1.0, smooth=5, eps=0.0)

Local dip estimation

Local dips are estimated using the Structure Tensor algorithm [1].

Note

For stability purposes, it is important to ensure that the orders of magnitude of the samplings are similar.

Parameters:

dnumpy.ndarray

Input dataset of size \(n_z \times n_x\)

dzfloat, optional

Sampling in \(z\)-axis, \(\Delta z\)

dxfloat, optional

Sampling in \(x\)-axis, \(\Delta x\)

smoothfloat or numpy.ndarray, optional

Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

epsfloat, optional

Regularization term. All anisotropies where \(\lambda_\text{max} < \epsilon\) are also set to zero. See Notes. When using with small values of smooth, start from a very small number (e.g. 1e-10) and start increasing by a power of 10 until results are satisfactory.

Returns:

dipsnumpy.ndarray

Estimated local dips. The unit is radians, in the range of \(-\frac{\pi}{2}\) to \(\frac{\pi}{2}\).

anisotropiesnumpy.ndarray

Estimated local anisotropies: \(1-\lambda_\text{min}/\lambda_\text{max}\)

Notes

Thin wrapper around pylops.utils.signalprocessing.slope_estimate with dips=True. See the Notes of pylops.utils.signalprocessing.slope_estimate for details.

[1]

Van Vliet, L. J., Verbeek, P. W., “Estimators for orientation and anisotropy in digitized images”, Journal ASCI Imaging Workshop. 1995.

Examples using pylops.utils.signalprocessing.dip_estimate

Slope estimation via Structure Tensor algorithm

Slope estimation via Structure Tensor algorithm