Den and I decided to try to classify seismic signals in the frequency domain rather than the time domain. We looked at amplitude spectral density plots of all of the data in our set, and noted that there were noticeable differences in the frequency domain for midnight quiet, trucks, and earthquakes.

For example, here is the time series of quiet, midnight seismic noise as compared to the seismic noise at the peak of an earthquake - the earthquake signal is noticeably higher in the 1 - 3 Hz region. Likewise, for the truck signal, there are noticeable bumps that arise at 10 and 30 Hz during the peak of the truck's motion due to the resonant frequency of the truck bouncing on its wheels.

We investigated this potential means of classification further by considering the linear separability of the power of our signals in various frequency bands. Below is a plot of the power of a normalized signal in the 0.1 - 3.0 Hz region vs. the power of the normalized signal in the 3.0 - 30.0 Hz region - calculated by means of fft and separation of the discrete resulting frequencies (in short, an ideal filter).

There is rather clear linear separability of the normalized signals in this case, as two lines could potentially be drawn to separate trucks from quiet and earthquake in this case (with a few misclassified points due to quiet - since the lab isn't actually empty and quiet in the middle of the night, and man-made seismic disturbances to occur). The reason we have to normalize our signals lies in the fact that the data set had different gains for various seismometers at different times. Normalization not only allows us to use our data set for training effectively, but it also assures that the online classification, if the online signals are also normalized, will allow for variable seismometer gains in the future and still be able to classify signals.

I looked at the linear separability of our training set using various combinations of frequency bands, and deduced that the current separation in the BLRMS preforms best (coincidentally, since the BLRMS separations are just decades), which meant that we could use the current BLRMS system we have for online classification of seismic noise.

Thus, I built a neural network which performed classification with the following parameters:

- One hidden layer of 20 neurons

- Gradient descent backpropagation with learning parameter mu = 0.175

- Sigmoidal activation functions for each neuron (computationally achieved by a parametrized hyperbola rather than an actual hyper-tangent in order to save on computation time). 

- 5 inputs - the normalized fft^2 of the signal (since the root of a signal doesn't add linearly to 1) in the following frequency regions: 0.1 - 0.3, 0.3 - 1.0, 1.0 - 3.0, 3.0 - 10.0 and 10.0 - 30.0 Hz. Since this division was done through the (frequency, fft value) return in Matlab, the signal was essentially filtered ideally into these frequency bands.

- 3 output neurons representing an output vector, with desired output vectors of [1, 0, 0] for earthquake, [0, 1, 0] for truck, and [0, 0, 1] for quiet.

- 1,600,000 training epochs (batch backpropagation on all of the data)

Below is the best learning curve for this network, representing the total amount of inputs misclassified out of 224. The best result achieved was 30 misclassified signals out of 224. Obviously this is not ideal, but our data is not totally linearly separable. This could, however, be reduced with further iterations, but given the close to 0 slope of the learning curve between iteration number 1,000,000 and number 1,500,000, this could take a very long time.

Thus, I trained the network, generated the weight vectors and optimal activation function parameters, and was ready to implement a feed-forward neural network (with no online training). My next e-log (Part 2) will be about this system and will be posted shortly.