  Earthquake signals are non-stationary in nature and thus in real-time, it is
difficult to identify and classify events based on classical approaches like
peak ground displacement, peak ground velocity. Even the popular algorithm of
STA/LTA requires extensive research to determine basic thresholding parameters
so as to trigger an alarm. Also, many times due to human error or other
unavoidable natural factors such as thunder strikes or landslides, the
algorithm may end up raising a false alarm. This work focuses on detecting
earthquakes by converting seismograph recorded data into corresponding audio
signals for better perception and then uses popular Speech Recognition
techniques of Filter bank coefficients and Mel Frequency Cepstral Coefficients
(MFCC) to extract the features. These features were then used to train a
Convolutional Neural Network(CNN) and a Long Short Term Memory(LSTM) network.
The proposed method can overcome the above-mentioned problems and help in
detecting earthquakes automatically from the waveforms without much human
intervention. For the 1000Hz audio data set the CNN model showed a testing
accuracy of 91.1% for 0.2-second sample window length while the LSTM model
showed 93.99% for the same. A total of 610 sounds consisting of 310 earthquake
sounds and 300 non-earthquake sounds were used to train the models. While
testing, the total time required for generating the alarm was approximately 2
seconds which included individual times for data collection, processing, and
prediction taking into consideration the processing and prediction delays. This
shows the effectiveness of the proposed method for Earthquake Early Warning
(EEW) applications. Since the input of the method is only the waveform, it is
suitable for real-time processing, thus the models can also be used as an
onsite EEW system requiring a minimum amount of preparation time and workload.
