High-resolution backprojection at regional distance: Application to the Haiti M7.0 earthquake and comparisons with finite source studies

A catastrophic M_w7 earthquake ruptured on 12 January 2010 on a complex fault system near Port-au-Prince, Haiti. Offshore rupture is suggested by aftershock locations and marine geophysics studies, but its extent remains difficult to define using geodetic and teleseismic observations. Here we perform the multitaper multiple signal classification (MUSIC) analysis, a high-resolution array technique, at regional distance with recordings from the Venezuela National Seismic Network to resolve high-frequency (about 0.4 Hz) aspects of the earthquake process. Our results indicate westward rupture with two subevents, roughly 35 km apart. In comparison, a lower-frequency finite source inversion with fault geometry based on new geologic and aftershock data shows two slip patches with centroids 21 km apart. Apparent source time functions from USArray further constrain the intersubevent time delay, implying a rupture speed of 3.3 km/s. The tips of the slip zones coincide with subevents imaged by backprojections. The different subevent locations found by backprojection and source inversion suggest spatial complementarity between high- and low-frequency source radiation consistent with high-frequency radiation originating from rupture arrest phases at the edges of main slip areas. The centroid moment tensor (CMT) solution and a geodetic-only inversion have similar moment, indicating most of the moment released is captured by geodetic observations and no additional rupture is required beyond where it is imaged in our preferred model. Our results demonstrate the contribution of backprojections of regional seismic array data for earthquakes down to M ≈ 7, especially when incomplete coverage of seismic and geodetic data implies large uncertainties in source inversions

Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process

A time frequency approach to blind deconvolution in multipath underwater channels

Blind deconvolution is studied in the underwater acoustic channel context, by time-frequency (TF) processing. The acoustic propagation environment is modelled by ray tracing and mathematically described by a multipath propagation channel. Representation of the received signal by means of a signal-dependent TF distribution (radially Gaussian kernel distribution) allowed to visualize the resolved replicas of the emitted signal, while signi cantly attenuating the inherent interferences of classic quadratic TF distributions. The source signal instantaneous frequency estimation was the starting point for both source and channel estimation. Source signature estimation was performed by either TF inversion, based on the Wigner-Ville distribution of the received signal, or a subspace- -based method. The channel estimate was obtained either via a TF formulation of the conventional matched- lter, or via matched- - ltering with the previously obtained source estimate. A shallow water realistic scenario is considered, comprising a 135-m depth water column and an acoustic source located at 90-m depth and 5.6-km range from the receiver. For the corresponding noiseless simulated data, the quality of the best estimates was 0.856 for the source signal, and 0.9664 and 0.9996 for the amplitudes and time-delays of the impulse response, respectively. Application of the proposed deconvolution method to real data of the INTIMATE '96 sea trial conduced to source and channel estimates with the quality of 0.530 and 0.843, respectively. TF processing has proved to remove the typical ill-conditioning of single sensor deterministic deconvolution techniques