Fourier Transform:
Applications
• Seismograms
• Eigenmodes of the Earth
• Time derivatives of seismograms
• The pseudo-spectral
method for acoustic wave propagation
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Fourier: Applications
Fourier: Space and Time
Space
x space variable
L spatial wavelength k=2π/λ spatial wavenumber F(k) wavenumber spectrum
Space
x space variable
L spatial wavelength k=2π/λ spatial wavenumber F(k) wavenumber spectrum
t Time variableTime
T period
f frequency
ω=2πf angular frequency t Time variableTime
T period
f frequency
ω=2πf angular frequency Fourier integrals
Fourier integrals
With the complex representation of sinusoidal functions eikx (or (eiwt) the Fourier transformation can be written as:
With the complex representation of sinusoidal functions eikx (or (eiwt) the Fourier transformation can be written as:
∫
∫
∞
∞
−
∞
∞
−
−
=
=
dx e
x f k
F
dx e
k F x
f
ikx ikx
) 2 (
) 1 (
) 2 (
) 1 (
π π
The Fourier Transform
discrete vs. continuous
∫
∫
∞
∞
−
∞
∞
−
−
=
=
dx e
x f k
F
dx e
k F x
f
ikx ikx
) 2 (
) 1 (
) 2 (
) 1 (
π π
1 ,...,
1 , 0 ,
1 ,...,
1 , 0 1 ,
/ 2 1
0
/ 2 1
0
−
=
=
−
=
=
∑
∑
−
=
− −
=
N k
e F f
N k
e N f
F
N ikj N
j
j k
N ikj N
j
j k
π
π
discrete
continuous
Whatever we do on the computer with data will be based on the discrete Fourier transform
Whatever we do on the computer with data will be based on the discrete Fourier transform
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Fourier: Applications
The Fast Fourier Transform
... the latter approach became interesting with the introduction of the Fast Fourier Transform (FFT). What’s so fast about it ?
The FFT originates from a paper by Cooley and Tukey (1965, Math.
Comp. vol 19 297-301) which revolutionised all fields where Fourier transforms where essential to progress.
The discrete Fourier Transform can be written as
1 ,...,
1 , 0 ˆ ,
1 ,...,
1 , 0 1 ,
ˆ
/ 2 1
0
/ 2 1
0
−
=
=
−
=
=
∑
∑
−
=
− −
=
N k
e u u
N k
e N u
u
N ikj N
j
j k
N ikj N
j
j k
π
π
The Fast Fourier Transform
... this can be written as matrix-vector products ...
for example the inverse transform yields ...
⎥⎥
⎥⎥
⎥⎥
⎥⎥
⎦
⎤
⎢⎢
⎢⎢
⎢⎢
⎢⎢
⎣
⎡
=
⎥⎥
⎥⎥
⎥⎥
⎥⎥
⎦
⎤
⎢⎢
⎢⎢
⎢⎢
⎢⎢
⎣
⎡
⎥⎥
⎥⎥
⎥⎥
⎥⎥
⎦
⎤
⎢⎢
⎢⎢
⎢⎢
⎢⎢
⎣
⎡
−
− −
−
−
−
1 2 1 0
1 2 1 0
) 1 ( 1
2 2 6
4 2
1 3
2
ˆ ˆ
ˆ ˆ
1 1 1
1 1
1 1
1
2
N N
N N
N N
u u
u u
u u
u u
M M M
M L
L L
M M
M
M M
M
K K K
ω ω
ω ω
ω ω
ω ω
ω ω
.. where ...
N
e2πi /
ω =
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Fourier: Applications
The Fast Fourier Transform
... the FAST bit is recognising that the full matrix - vector multiplication can be written as a few sparse matrix - vector multiplications
(for details see for example Bracewell, the Fourier Transform and its applications, MacGraw-Hill) with the effect that:
Number of multiplications Number of multiplications
full matrix FFT
N2 2Nlog2 N
this has enormous implications for large scale problems.
Note: the factorisation becomes particularly simple and effective when N is a highly composite number (power of 2).
The Fast Fourier Transform
.. the right column can be regarded as the speedup of an algorithm when the FFT is used instead of the full system.
Number of multiplications Number of multiplications
Problem full matrix FFT Ratio full/FFT 1D (nx=512) 2.6x105 9.2x103 28.4
1D (nx=2096) 94.98 1D (nx=8384) 312.6
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Fourier: Applications
Spectral synthesis
The red trace is the sum of all blue traces!
The red trace is the sum of all blue traces!
Phase and amplitude spectrum
)
)
(( )
( ω = F ω e
iΦ ωF
The spectrum consists of two real-valued functions of angular frequency, the amplitude spectrum mod (F(ω)) and the phase spectrum φ(ω)
In many cases the amplitude spectrum is the most important part to be considered. However there are cases where the phase spectrum plays an important role (-> resonance, seismometer)
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Fourier: Applications
… remember …
2
2
* ( )( )
) sin(
cos
) sin
(cos
*
r ib
a ib
a zz
z
r ri
r
i r
ib a
z
i
=
− +
=
=
=
−
−
−
=
−
=
−
=
− φ
φ φ
φ
φ
The spectrum
Amplitude spectrum
Amplitude spectrum Phase spectrumPhase spectrum
FourierspaceFourierspace PhysicalspacePhysicalspace
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Fourier: Applications
The Fast Fourier Transform (FFT)
Most processing tools (e.g. octave, Matlab, Mathematica,
Fortran, etc) have intrinsic functions for FFTs
Most processing tools (e.g. octave, Matlab, Mathematica,
Fortran, etc) have intrinsic functions for FFTs
>> help fft
FFT Discrete Fourier transform.
FFT(X) is the discrete Fourier transform (DFT) of vector X. For matrices, the FFT operation is applied to each column. For N-D arrays, the FFT operation operates on the first non-singleton dimension.
FFT(X,N) is the N-point FFT, padded with zeros if X has less than N points and truncated if it has more.
FFT(X,[],DIM) or FFT(X,N,DIM) applies the FFT operation across the dimension DIM.
For length N input vector x, the DFT is a length N vector X, with elements
X(k) = sum x(n)*exp(-j*2*pi*(k-1)*(n-1)/N), 1 <= k <= N.N The inverse DFT (computed by IFFT) is given byn=1
x(n) = (1/N) sum X(k)*exp( j*2*pi*(k-1)*(n-1)/N), 1 <= n <= N.N k=1
See also IFFT, FFT2, IFFT2, FFTSHIFT.
>> help fft
FFT Discrete Fourier transform.
FFT(X) is the discrete Fourier transform (DFT) of vector X. For matrices, the FFT operation is applied to each column. For N-D arrays, the FFT operation operates on the first non-singleton dimension.
FFT(X,N) is the N-point FFT, padded with zeros if X has less than N points and truncated if it has more.
FFT(X,[],DIM) or FFT(X,N,DIM) applies the FFT operation across the dimension DIM.
For length N input vector x, the DFT is a length N vector X, with elements
X(k) = sum x(n)*exp(-j*2*pi*(k-1)*(n-1)/N), 1 <= k <= N.N The inverse DFT (computed by IFFT) is given byn=1
x(n) = (1/N) sum X(k)*exp( j*2*pi*(k-1)*(n-1)/N), 1 <= n <= N.N k=1
See also IFFT, FFT2, IFFT2, FFTSHIFT.
Matlab FFT
Frequencies in seismograms
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Fourier: Applications
Amplitude spectrum
Eigenfrequencies
Sound of an instrument
a‘ - 440Hz
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Fourier: Applications
Instrument Earth
26.-29.12.2004 (FFB )
0 S2 – Earth‘s gravest tone T=3233.5s =53.9min
Theoretical eigenfrequencies
Fourier Spectra: Main Cases
random signals
Random signals may contain all frequencies. A spectrum with constant contribution of all frequencies is called a white spectrum
Random signals may contain all frequencies. A spectrum with constant contribution of all frequencies is called a white spectrum
Modern Seismology – Data processing and inversion
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Fourier: Applications
Fourier Spectra: Main Cases
Gaussian signals
The spectrum of a Gaussian function will itself be a Gaussian function. How does the spectrum change, if I make the Gaussian
narrower and narrower?
The spectrum of a Gaussian function will itself be a Gaussian function. How does the spectrum change, if I make the Gaussian
narrower and narrower?
Fourier Spectra: Main Cases
Transient waveform
A transient wave form is a wave form limited in time (or space) in comparison with a harmonic wave form that is infinite
A transient wave form is a wave form limited in time (or space) in comparison with a harmonic wave form that is infinite
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Fourier: Applications
Puls-width and Frequency Bandwidth
time (space) spectrum
Narrowingphysicalsignal Wideningfrequencyband
Spectral analysis: an Example
24 hour ground motion, do you see any signal?
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Fourier: Applications
Seismo-Weather
Running spectrum of the same data
Some properties of FT
• FT is linear
signals can be treated as the sum of several signals, the transform will be the sum of their transforms
• FT of a real signals
has symmetry properties
the negative frequencies can be obtained from symmetry
properties
• Shifting corresponds to changing the phase (shift theorem)
• Derivative
) (
* )
( ω F ω
F − =
) ( )
(
) ( )
(
t f e
a F
F e
a t f
a i
a i
ω ω
ω
ω
−
−
→
−
→
−
) ( )
( )
(t iω F ω
dt f
dn → n
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Fourier: Applications
Fourier Derivatives
∫
∫
∞
∞
−
−
∞
∞
−
−
−
=
⎟⎟⎠
⎞
⎜⎜⎝
∂ ⎛
=
∂
dk e
k ikF
dk e
k F x
f
ikx ikx x
x
) (
) ( )
(
.. let us recall the definition of the derivative using Fourier integrals ...
... we could either ...
1) perform this calculation in the space domain by convolution 2) actually transform the function f(x) in the k-domain and back
Acoustic Wave Equation - Fourier Method
let us take the acoustic wave equation with variable density
⎟⎟⎠
⎜⎜ ⎞
⎝
⎛ ∂
∂
=
∂ p p
c t x ρ x
ρ
1
1 2
2
the left hand side will be expressed with our standard centered finite-difference approach
[ ] ⎟⎟
⎠
⎜⎜ ⎞
⎝
⎛ ∂
∂
=
− +
−
+ dt p t p t dt p
t dt p
c x ρ x
ρ
) 1 (
) ( 2 )
1 (
2 2
... leading to the extrapolation scheme ...
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Fourier: Applications
Acoustic Wave Equation - Fourier Method
where the space derivatives will be calculated using the Fourier Method.
The highlighted term will be calculated as follows:
) (
) ( 1 2
)
(t dt c 2 dt 2 p p t p t dt
p x x ⎟⎟ + − −
⎠
⎜⎜ ⎞
⎝
⎛ ∂
∂
=
+ ρ ρ
n j x n
n n
j P ik P P
P → FFT → ˆυ → υ ˆυ → FFT −1 → ∂
multiply by 1/ρ
⎟⎟⎠
⎜⎜ ⎞
⎝
⎛ ∂
∂
→
⎟⎟ →
⎠
⎜⎜ ⎞
⎝
⎛ ∂
⎟⎟ →
⎠
⎜⎜ ⎞
⎝
⎛ ∂
→
→
∂ − x x jn
n x
n x
n j
xP P ik P P
ρ ρ
ρ
ρ υ υ υ
FFT 1 1 ˆ
1 ˆ
1 FFT 1
... then extrapolate ...
... and the first derivative using FFTs ...
function df=sder1d(f,dx)
% SDER1D(f,dx) spectral derivative of vector nx=max(size(f));
% initialize k kmax=pi/dx;
dk=kmax/(nx/2);
for i=1:nx/2, k(i)=(i)*dk; k(nx/2+i)=-kmax+(i)*dk; end k=sqrt(-1)*k;
% FFT and IFFT
ff=fft(f); ff=k.*ff; df=real(ifft(ff));
.. simple and elegant ...
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Fourier: Applications
Fourier Method - Comparison with FD - Table
0 20 40 60 80 100 120 140 160
5 Hz 10 Hz 20 Hz
3 point 5 point Fourier
Difference (%) between numerical and analytical solution as a function of propagating frequency
Simulation time 5.4s
7.8s 33.0s
500 1000 1500 0
1 2 3 4 5 6 7 8 9 10
500 100 0
1 2 3 4 5 6 7 8 9 10
500 1000 1500 0
1 2 3 4 5 6 7 8 9 10
Numerical solutions and Green’s Functions
3 point operator 5 point operator Fourier Method
Frequency increases Impulse response (analytical) concolvedwith source Impulse response (numerical convolved with source