Evaluating the location capabilities of a regional infrasonic network in Utah, US, using both ray tracing-derived and empirical-derived celerity-range and backazimuth models

Abstract

SUMMARY More realistic models for infrasound signal propagation across a region can be used to improve the precision and accuracy of spatial and temporal source localization estimates. Motivated by incomplete infrasound event bulletins in the Western US, the location capabilities of a regional infrasonic network of stations located between 84 - 458 km from the Utah Test and Training Range, Utah, USA, is assessed using a series of near-surface explosive events with complementary ground truth information. Signal arrival times and backazimuth estimates are determined with an automatic F-statistic based signal detector and manually refined by an analyst. This study represents the first application of three distinct celerity-range and backazimuth models to an extensive suite of realistic signal detections for event location purposes. A singular celerity and backazimuth deviation model was previously constructed using ray tracing analysis based on an extensive archive of historical atmospheric specifications and is applied within this study to test location capabilities. Similarly, a set of multi-variate, season and location specific models for celerity and backazimuth are compared to an empirical model that depends on the observations across the infrasound network and the ground truth events, which accounts for atmospheric propagation variations from source to receiver. Discrepancies between observed and predicted signal celerities result in locations with poor accuracy. Application of the empirical model improves both spatial localization precision and accuracy; all but one location estimates retain the true GT location within the 90% confidence bounds. Average mislocation of the events is 15.49 km and average 90% error ellipse areas are 4141 km2. The empirical model additionally reduces origin time residuals; origin time residuals from the other location models are in excess of 160 seconds while residuals produced with the empirical model are within 30 seconds of the true origin time. We demonstrate that event location accuracy is driven by a combination of signal propagation model and the azimuthal gap of detecting stations. A direct relationship between mislocation, error ellipse area and increased station azimuthal gaps indicate that for sparse networks, detection backazimuths may drive location biases over travel time estimates.

ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location

“…This area indicates events in different acoustic scenes, especially in spatial room impulse responses (SRIRs), realistic spatialization, and reverberation environments. The task of sound event location can be applied to many areas such as machine automatic driver [1][2][3][4], smart automation [5][6][7][8], ocean positioning technology [9][10][11], quality monitoring [12], medical supervision [13], navigation without visual input or with occluded targets [14], selflocalization [15], smart-phone audio assistant [16], acoustic device monitoring [17], scene visualization systems [18], moving source tracking [19], virtual reality [20] and etc. The processing flow and applications can be shown in Fig.…”

ESRNN: Effective Residual Self-Attention Recurrent Neural Network for Sound Event Location

“…Sound event location is a submission DOA which including 1D or 2D angles for each active sound event class calculated from the coordinates using the estimation of parameter or time difference of arrival method. The task of sound event location can be applied to many areas such as machine automatic driver [1]- [4], smart automation [5]- [8], ocean positioning technology [9]- [11], quality monitoring [12], medical supervision [13], navigation without visual input or with occluded targets [14], self-localization [15], smart-phone audio assistant [16], acoustic device monitoring [17], scene visualization systems [18], moving source tracking [19], Virtual Reality [20] and etc. The processing flow and…”