Discrimination between different rocket types is an important application for utilizing infrasound in event monitoring within a range of 0-100 km. This is in contrast to traditional nuclear weapons monitoring which leverages infrasound propagation over thousands of kilometers. The motivation of this research is to demonstrate the utilization of deep neural network architectures to discriminate infrasonic signals produced by rocket launches and collected by an near-field infrasound sensor array. The data collection contains three space bound rocket classes: Delta IV, Atlas V, and Falcon 9. In particular, we investigate the classification accuracy of a multi-class convolutional neural network (CNN) and a deep neural network (DNN) on various feature representations, such as neural network derived features, spectrograms, and wavelet scattering transform coefficients. Our experiments validate the viability of a CNN and DNN framework for near-field infrasonic applications, with our proposed method achieving favorable results.
Infrasound propagation through various atmospheric conditions and interaction with environmental factors in- duce highly non-linear and non-stationary effects that make it difficult to extract reliable attributes for classi- fication. We present featureless classification results on the Library of Typical Infrasonic Signals using several deep learning techniques, including long short-term memory, self-normalizing, and fully convolutional neural net- works with statistical analysis to establish significantly superior models. In general, the deep classifiers achieve near-perfect classification accuracies on the four classes of infrasonic events including mountain associated waves, microbaroms, auroral infrasonic waves, and volcanic eruptions. Our results provide evidence that deep neural network architectures be considered the leading candidate for classifying infrasound waveforms which can directly benefit applications that seek to identify infrasonic events such as severe weather forecasting, natural disaster early warning systems, and nuclear weapons monitoring.
Infrasonic waves continue to be a staple of threat identification due to their presence in a variety of natural and man-made events, along with their low-frequency characteristics supporting detection over great distances. Considering the large set of phenomena that produce infrasound, it is critical to develop methodologies that exploit the unique signatures generated by such events to aid in threat identification. In this work, we propose a new infrasonic time-series classification technique based on the recently introduced Wavelet Scattering Transform (WST). Leveraging concepts from wavelet theory and signal processing, the WST induces a deep feature mapping on time series that is locally time invariant and stable to time-warping deformations through cascades of signal filtering and modulus operators. We demonstrate that the WST features can be utilized with a variety of classification methods to gain better discrimination. Experimental validation on the Library of Typical Infrasonic Signals (LOTIS)—containing infrasound events from mountain associated waves, microbaroms, internal atmospheric gravity waves and volcanic eruptions—illustrates the effectiveness of our approach and demonstrate it to be competitive with other state-of-the-art classification techniques.