seisgo.utils

The utils module provides a broad collection of signal processing, coordinate transformation, SNR estimation, and I/O utility functions used throughout SeisGo.

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Function Summary

Signal processing

Coordinate utilities

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Key Functions

rms

from seisgo import utils
noise_rms = utils.rms(noise_trace)   # √(mean(d²))

get_snr

Compute the signal-to-noise ratio using physics-based signal and noise windows:

Parameter	Default	Description
d	required	1-D or 2-D data array.
t	required	Time vector (s).
dist	required	Source–receiver distance (km).
vmin, vmax	required	Velocity range defining the signal window.
extend	0	Extend the signal window by this many seconds.
offset	20	Gap (s) between the signal and noise windows.
axis	1	Trace axis for 2-D arrays.
db	False	Return SNR in decibels.
getwindow	False	Also return the window index arrays.
side	"a"	"a" (both lags), "n" (negative), or "p" (positive).
shorten_noise	False	Allow noise window to be shorter than signal window if needed.

snr = utils.get_snr(corrdata.data, t, dist=50.0, vmin=1.5, vmax=4.5)
# snr[i] = [snr_negative, snr_positive] for each trace i

ricker and gaussian

Generate standard source wavelets:

t_r, w_r = utils.ricker(dt=0.001, f=30, t0=0.05)
t_g, w_g = utils.gaussian(dt=0.001, width=0.02, shift=0.05)

The Gaussian wavelet follows the FWANT convention:

\[g(t) = \frac{\exp[-(t-t_0)^2/a^2]}{\sqrt{\pi}\,a}\]

where a is the width parameter (σ) and t₀ is the time shift.

cart2compass

Convert Cartesian slowness components to compass bearing and slowness magnitude:

bearing_deg, slowness = utils.cart2compass(Ux, Uy)

box_smooth

Apply a simple box-car (moving-average) smoother to a 1-D array:

smoothed = utils.box_smooth(distortion_curve, n=3)