NNET PARAMETERS

Type Multi-layer Perceptron (MLP) n_input Selected from hydro feature table n_hidden PCA n_output 1 or 2 n_weights n_input x n_hidden x n_output transfer function logistic

Dr. Paul Dysart 4001 N. Fairfax Dr,  Suite 400, M/S 175 Arlington, VA 22203 

A schematic view of the phase identification process (green outline) illustrates its context within the existing system, although it is purely an off-line process up to the point of weights file installation.

Summary and Conclusions

The summary performance of the prototype system applied to recent Wake Island data demonstrates probabilities of detection (Pd) and false-alarm (Pfa) well within performance requirements. Implementations based on more recent machine learning technologies are currently being developed to enhance automatic phase identification capabilities at all IMS hydroacoustic sites.

The figure above illustrating the parameter selection criteria used individually for each stage of the MLP classifier training.

The figure above shows the locations of T and H phases used to train the MLP classifier at station at HA11.

The figure above shows summary performance after application of the 2-stage MLP to the training set using crossover confidence method.

Spectrogram of 0.03kt KRAKEN shot simulation in noise at DGN01 for a source depth of 500 meters (synthetic time series are converted to standard CSS 3.0 format for DFX processing).

HYDROACOUSTIC PHASES

PHASID DEFINITION

N NOISE

T T-PHASE (SOLID EARTH AND WATER-BORNE PATH)

H HYDROACOUSTIC PHASE (WATER-BORNE PATH)

HYDRO_FEATURES

arid termination_time cep_delay time

signal ford peak_time total_time cep_peak std

signal ftype* peak_level num_cross cep_var_trend fzp* total_energy ave_noise cep_delay time

trend prob_weight_time* mean_arrival_time skewness cep_peak_std_tre

nd sigma_time* time_spread kurtosis low_cut lddate onset_time cep_var signal high_cut

DERIVED FEATURES

peak_delay (s) = peak_time-onset_time peak/ave_energy = peak_level/(total_energy/duration) peak_delay - mean_delay/duration mean_delay (s) = mean_time - onset_time total_time/duration duration (s) = onset_time termination_time num_cross_rate = num_cross/duration snr = (total_energy/duration)/ave_noise signal_in_band (Boolean)

*introduced April, 1998

MLP Stage-1 training and event separation. In stage 1, the targets are H and N phases, while T-phases represented clutter events. NNET activation are fit to the Beta distribution to allow the use of uncertainty metrics and eventually enable the application of active learning schemes.

The characterization of in-water explosions relies almost exclusively on time-series simulations. Currently, the impulsive data used to simulate H-phase arrivals in a ground-truth data set are generated using a broad-band version of the KRAKEN normal-mode model. To further simulate the characteristics of an actual in-water explosion, explosion source functions computed for various yields and depths are convolved with the modeled impulse response and embedded in samples of real station noise. To obtain signal features from these synthetic data, the noise-corrupted broad-band simulations are formatted and processed by DFX-H as arrival data.

The process of generating the ground-truth data used for NNET training begins with the assemblage of the highest quality reviewed arrival data and the characterization of hydroacoustic signals. The original development of DFX-H included the definition of a comprehensive set of signal parameters in a fixed number of octave bands covering the operational bandwidth. In addition to the raw features in the hydro feature table, a number of parameters invariant to event magnitude and absolute time are derived from the hydro feature set and used to train a multistage MLP (Multilayer Perceptron) classifier. Artificial neural networks are generally analogous to other techniques making their design and usage subject to many of the constraints of conventional error minimization schemes.

One purpose in assigning uncertain in the phase ID process is to capture impulsive arrivals similar to H-phases whose paths are not exclusively water-borne such as island nuclear test.

The need for a multi-stage classification approach was indicated by the overlap of classes observed repeatedly during the early review process. It was clearly observed that all three populations would be difficult to separate simultaneously based on a single set of signal parameters. Given these observations and the similarity in waveform character between H and N phases, the first stage in the classification process is to separate H and N phases from T-phase arrivals. In the second stage a different set of parameters is selected to separate the H and N phases grouped previously in Stage 1.

ABSTRACT

Release 3 of the International Monitoring System included fully functional hydroacoustic versions of DFX (detection processor), StaPro (station processor) and GA (association, location, magnitude estimation). DFX-H and StaPro H functionality includes detection, windowing, and feature extraction, MLP (Multilayer Perceptron) training, and the use of site-specific Neural Network (NNET) weights files for automatic phase identification at IMS hydroacoustic stations. Currently there are there three definitive hydroacoustic phases - N (noise), T (solid earth and water-borne path), H (predominantly water-borne path), and a fourth class U that can be applied to unknown or uncertain results. These phase types were originally based on observations at prototype stations at PSUR and Wake Island, and assumptions regarding T-phase source coupling and propagation.

The Mahalanobis distance is as statistical measure of class separability which, together with computed pairwise covariance and a simple Principal Component Analysis, provide information that enable optimal selection of signal parameters and the configuration of the MLP network.