# Transforms#

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#

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 casa.colorado.edu/~ajsh/FFTLog. 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`

(github.com/prisae/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 under certain circumstances FFTLog can be faster and more precise than digital filters, particularly at early times for responses with source and receiver at the interface between air and subsurface. Credit to use FFTLog in electromagnetics goes to David Taylor and [PeMS06] who, as far as I know unaware of each other, used FFTLog in the mid-2000s for geophysical electromagnetic purposes. Taylor implemented FFTLog into the isotropic layered CSEM forward modellers of the company Multi-Transient ElectroMagnetic (MTEM Ltd, later Petroleum Geo-Services PGS). The implementation was driven by land responses. Pervago used it to model layered DC responses with arbitrary anisotropy.

## 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,

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,

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

and the step-off by

## 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 \(s=2\mathrm{i}\pi f\).