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Intro to Spectral Analysis and Matlab Q: How Could you quantify how much lower the tone of a race car is after it passes you compared to as it is coming towards you? How would

Intro to Spectral Analysis and Matlab

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Intro to Spectral Analysis and Matlab. Q: How Could you quantify how much lower the tone of a race car is after it passes you compared to as it is coming towards you? How would you set the experiment up?. Running the Experiment . - PowerPoint PPT Presentation

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Page 1: Intro to Spectral Analysis and Matlab

Intro to Spectral Analysis and Matlab

Q: How Could you quantify how much lower the tone of a race car is after it passes you compared to as it is coming towards you? How would you set the experiment up?

Page 2: Intro to Spectral Analysis and Matlab

Running the Experiment .

Data is often recorded in the time domain. The stored dataset is called a timeseries. It is a set of time and amplitude pairs.

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Frequency Domain (Do a Fourier Transform on Timeseries)

We have converted to the Frequency Domain. This dataset is called a Spectra. It is a set of frequency and Amplitude pairs.

Page 5: Intro to Spectral Analysis and Matlab

Time Domain

What’s the Frequency?What’s the Period?What will this look like in the Frequency Domain?

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What’s the new (red) period?How Does its amplitude Compare to the 1 s signal?

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Power Spectral Densities

Secondary Microseism (~8 s)

Primary Microseism (~ 16 s)

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QSPA PSD PDF

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The Mysterious Case of HOWD

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Sampling Frequency

• Digital signals aren’t continuous– Sampled at discrete times

• How often to sample?– Big effect on data volume

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How many samples/second are needed?

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Are red points enough?

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AliasingFFT will give wrong frequency

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Nyquist frequency1/2 sampling frequency

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Nyquist frequency

• Can only accurately measure frequencies <1/2 of the sampling frequency– For example, if sampling frequency is 200

Hz, the highest theoretically measurable frequency is 100 Hz

• How to deal with higher frequencies?– Filter before taking spectra

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Summary• Infinite sine wave is spike in frequency

domain• Can create arbitrary seismogram by adding

up enough sine waves of differing amplitude, frequency and phase

• Both time and frequency domains are complete representations– Can transform back and forth – FFT and iFFT

• Must be careful about aliasing– Always sample at least 2X highest frequency

of interest

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To create arbitrary seismogram

• Becomes integral in the limit • Fourier Transform

– Computer: Fast Fourier Transform - FFT

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Exercise plots

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Sine_wave column 2

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Sine_wave column 2

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Sine_wave column 2 and 3

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Sine_wave column 2 and 3 sum

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Spectra, column 2

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Spectra, columns 2, 3

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Spectra, column 2, 3, 2 and 3 sum

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Multi_sine, individual columns

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Multi_sine, individual columns

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Multi_sine spectra

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Spike in time

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Spike in time, frequency

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Rock, sed, bog time series

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Rock spectra

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Rock (black), Sed (red), bog (blue)

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Spectral ratio sed/rock

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Basin Thickness

• Sediment site• 110 m/s /2.5 Hz = 44 m wavelength• Basin thickness = 11 m

• Peat Bog• 80 m/s /1 Hz = 80 m• Basin thickness = 20 m

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Station LKWY, Utah

raw

Filtered2-19 Hz

Filtered twice

Page 38: Intro to Spectral Analysis and Matlab

Station LKWY, Utah

raw

Filtered2-19 Hz

Filtered twice

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Zoomed in once

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Zoomed in once

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Zoomed in again

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Triggered earthquakes