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I am studying the seismo-ionospheric anomalies associated with the Japan earthquake using total electron content derived from a GPS signal. I am trying to detect acoustic-gravity waves in the signal caused by the earthquake if possible.

I am trying to perform spectral analysis for this task using matplotlib's built-in functions. So far, I have written the following code

fig, [ax1, ax2, ax3] = plt.subplots(nrows=3)
fig.tight_layout()

(spec, freq, ln) = ax1.magnitude_spectrum(hourly_usud["TEC"], scale="dB", )
ax1.set_title("Time and Frequency Domain TEC")

ax2.phase_spectrum(hourly_usud["TEC"]);

ax3.plot(hourly_usud["TEC"])
ax3.set_xticklabels(ax3.get_xticklabels(), rotation = 45);
ax3.xaxis_date()

I've got the following figure, but I don't know what to do next, how to interpret that graph, or how to extract meaningful insights from it.

enter image description here

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1 Answer 1

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I think you are approaching this backwards. In general I would recommend

  1. Start with a mathematical model of your system
  2. Analyze that model and identify certain properties or behaviors that might be identifiable in that data
  3. Create some hypothesis around these properties
  4. Pick the analysis method that's most suitable to look for the properties
  5. Grab your data and go to town.

Applying a random analysis method and staring at it can be occasionally useful, but if you don't know what to look for the likelihood of finding anything useful is low.

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