Included Hankel transforms:

  • DLF: Digital Linear Filters

  • QWE: Quadrature with Extrapolation

  • QUAD: Adaptive quadrature

Included Fourier transforms:

  • DLF: Digital Linear Filters

  • QWE: Quadrature with Extrapolation

  • FFTLog: Logarithmic Fast Fourier Transform

  • FFT: Fast Fourier Transform

Digital Linear Filters

The module empymod.filters comes with many DLFs for the Hankel and the Fourier transform. If you want to export one of these filters to plain ASCII files you can use the tofile-routine of each filter:

>>> import empymod
>>> # Load a filter
>>> filt = empymod.filters.wer_201_2018()
>>> # Save it to pure ASCII-files
>>> filt.tofile()
>>> # This will save the following three files:
>>> #    ./filters/wer_201_2018_base.txt
>>> #    ./filters/wer_201_2018_j0.txt
>>> #    ./filters/wer_201_2018_j1.txt

Similarly, if you want to use an own filter you can do that as well. The filter base and the filter coefficient have to be stored in separate files:

>>> import empymod
>>> # Create an empty filter;
>>> # Name has to be the base of the text files
>>> filt = empymod.filters.DigitalFilter('my-filter')
>>> # Load the ASCII-files
>>> filt.fromfile()
>>> # This will load the following three files:
>>> #    ./filters/my-filter_base.txt
>>> #    ./filters/my-filter_j0.txt
>>> #    ./filters/my-filter_j1.txt
>>> # and store them in filt.base, filt.j0, and filt.j1.

The path can be adjusted by providing tofile and fromfile with a path-argument.


FFTLog is the logarithmic analogue to the Fast Fourier Transform FFT originally proposed by [Talm78]. The code used by empymod was published in Appendix B of [Hami00] and is publicly available at From the FFTLog-website:

FFTLog is a set of fortran subroutines that compute the fast Fourier or Hankel (= Fourier-Bessel) transform of a periodic sequence of logarithmically spaced points.

FFTlog can be used for the Hankel as well as for the Fourier Transform, but currently empymod uses it only for the Fourier transform. It uses a simplified version of the python implementation of FFTLog, pyfftlog (

[HaJo88] proposed a logarithmic Fourier transform (abbreviated by the authors as LFT) for electromagnetic geophysics, also based on [Talm78]. I do not know if Hamilton was aware of the work by Haines and Jones. The two publications share as reference only the original paper by Talman, and both cite a publication of Anderson; Hamilton cites [Ande82], and Haines and Jones cite [Ande79]. Hamilton probably never heard of Haines and Jones, as he works in astronomy, and Haines and Jones was published in the Geophysical Journal.

Logarithmic FFTs are not widely used in electromagnetics, as far as I know, probably because of the ease, speed, and generally sufficient precision of the digital filter methods with sine and cosine transforms ([Ande75]). However, comparisons show that FFTLog can be faster and more precise than digital filters, specifically for responses with source and receiver at the interface between air and subsurface. Credit to use FFTLog in electromagnetics goes to David Taylor who, in the mid-2000s, implemented FFTLog into the forward modellers of the company Multi-Transient ElectroMagnetic (MTEM Ltd, later Petroleum Geo-Services PGS). The implementation was driven by land responses, where FFTLog can be much more precise than the filter method for very early times.

Notes on Fourier Transform

The Fourier transform to obtain the space-time domain impulse response from the complex-valued space-frequency response can be computed by either a cosine transform with the real values, or a sine transform with the imaginary part,

(1)\[\begin{split}E(r, t)^\text{Impulse} &= \ \frac{2}{\pi}\int^\infty_0 \Re[E(r, \omega)]\ \cos(\omega t)\ \text{d}\omega \ , \\ &= -\frac{2}{\pi}\int^\infty_0 \Im[E(r, \omega)]\ \sin(\omega t)\ \text{d}\omega \ ,\end{split}\]

see, e.g., [Ande75] or [Key12]. Quadrature-with-extrapolation, FFTLog, and obviously the sine/cosine-transform all make use of this split.

To obtain the step-on response the frequency-domain result is first divided by \(\mathrm{i}\omega\), in the case of the step-off response it is additionally multiplied by -1. The impulse-response is the time-derivative of the step-response,

(2)\[E(r, t)^\text{Impulse} = \frac{\partial\ E(r, t)^\text{step}}{\partial t}\ .\]

Using \(\frac{\partial}{\partial t} \Leftrightarrow \mathrm{i}\omega\) and going the other way, from impulse to step, leads to the divison by \(\mathrm{i}\omega\). This only holds because we define in accordance with the causality principle that \(E(r, t \le 0) = 0\).

With the sine/cosine transform (ft='dlf'/'sin'/'cos') you can choose which one you want for the impulse responses. For the switch-on response, however, the sine-transform is enforced, and equally the cosine transform for the switch-off response. This is because these two do not need to now the field at time 0, \(E(r, t=0)\).

The Quadrature-with-extrapolation and FFTLog are hard-coded to use the cosine transform for step-off responses, and the sine transform for impulse and step-on responses. The FFT uses the full complex-valued response at the moment.

For completeness sake, the step-on response is given by

(3)\[E(r, t)^\text{Step-on} = - \frac{2}{\pi}\int^\infty_0 \Im\left[\frac{E(r,\omega)}{\mathrm{i} \omega}\right]\ \sin(\omega t)\ \text{d}\omega \ ,\]

and the step-off by

(4)\[E(r, t)^\text{Step-off} = - \frac{2}{\pi}\int^\infty_0 \Re\left[\frac{E(r,\omega)}{\mathrm{i} \omega}\right]\ \cos(\omega t)\ \text{d}\omega \ .\]

Laplace domain

It is also possible to compute the response in the Laplace domain, by using a real value for \(s\) instead of the complex value \(\mathrm{i}\omega\). This simplifies the problem from complex numbers to real numbers. However, the transform from Laplace-to-time domain is not as robust as the transform from frequency-to-time domain, and is currently not implemented in empymod. To compute Laplace-domain responses instead of frequency-domain responses simply provide negative frequency values. If all provided frequencies \(f\) are negative then \(s\) is set to \(-f\) instead of the frequency-domain \(s=2\mathrm{i}\pi f\).