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Intro to Spectral Analysis and Matlab. Time domain. Seismogram - particle position over time. Amplitude. Time. Frequency domain. Why might frequency be as or more important than amplitude? Filtering signal from noise Understanding earthquake source, propagation effects Ground shaking. - PowerPoint PPT Presentation
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Intro to Spectral Analysis and Matlab
Time domain
• Seismogram - particle position over time
Time
Amplitude
Frequency domain
• Why might frequency be as or more important than amplitude?– Filtering signal from noise– Understanding earthquake source, propagation effects– Ground shaking
Time domain <-> Frequency domain
• Possible to mathematically transform from time to frequency domain
• Relative importance of the frequencies contained in the time series
• Can completely describe the system either way.
• Goal of today’s lab– Begin to become familiar with describing seismograms in either
time or frequency domains– Will leave out most of the mathematics
Sine wave in time
Spectra of infinite sine wave
Spectra of infinite sine wave
Two sine waves in time
Spectra of 2 infinite sine waves
Spectra of discrete, finite sine waves
To create arbitrary seismogram
• Becomes integral in the limit • Fourier Transform
– Computer: Fast Fourier Transform - FFT
Time domain, single spike in time
Spectra of a single spike in time
Sampling Frequency
• Digital signals aren’t continuous– Sampled at discrete times
• How often to sample?– Big effect on data volume
How many samples/second are needed?
Are red points enough?
AliasingFFT will give wrong frequency
Nyquist frequency1/2 sampling frequency
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
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
• Must be careful about aliasing– Always sample at least 2X highest frequency
of interest
Exercise plots
Sine_wave column 2
Sine_wave column 2
Sine_wave column 2 and 3
Sine_wave column 2 and 3 sum
Spectra, column 2
Spectra, columns 2, 3
Spectra, column 2, 3, 2 and 3 sum
Multi_sine, individual columns
Multi_sine, individual columns
Multi_sine spectra
Spike in time
Spike in time, frequency
Rock, sed, bog time series
Rock spectra
Rock (black), Sed (red), bog (blue)
Spectral ratio sed/rock
Basin Thickness
• 110 m/s /2.5 Hz = 44 m wavelength• Basin thickness = 11 m
• 80 m/s /1 Hz = 80 m• Basin thickness = 20 m
Station LKWY, Utah
raw
Filtered2-19 Hz
Filtered twice
Station LKWY, Utah
raw
Filtered2-19 Hz
Filtered twice
Zoomed in once
Zoomed in once
Zoomed in again
Triggered earthquakes