Bayesian-Deep-Learning Estimation of Earthquake Location From Single-Station Observations

Single station estimation of earthquake early warning parameters by using amplitude envelope curve

In this study, new empirical relationships to estimate key parameters in Earthquake Early Warning (EEW) system including magnitude, epicentral distance and Peak Ground Acceleration (PGA) are introduced based on features of the initial portion of P-wave’s amplitude envelope curve. For this purpose, 226 time series recorded by bore-hole accelerometers of Japanese KiK-net are processed for earthq...

Location of lightning discharges from a single station

A new computer-based ELF/VLF system for locating lightning discharges has been developed. Both the arrival azimuths of atmospherics and the distances to their sources are estimated. The direction-finding technique uses the Poynting vector calculated directly in the time domain over the full band pass of the receiver. Both the distance of the lightning discharge and the ionospheric height can be...

On Bayesian Learning from Bernoulli Observations

We provide a reason for Bayesian updating, in the Bernoulli case, even when it is assumed observations are independent and identically distributed with fixed but unknown parameter θ0. The motivation relies on the use of loss functions and asymptotics. Such a justification is important due to the recent interest and focus on Bayesian consistency which indeed assumes the observations are independ...

Bayesian Deep Learning | Uncertainty in Deep Learning

Bayesian Filtering for Location Estimation

L ocation awareness is important to many pervasive computing applications. Unfortunately, no location sensor takes perfect measurements or works well in all situations. Thus, the motivation behind this article is twofold. First, we believe the pervasive computing community will benefit from a concise survey of Bayesian-filter techniques. Because no sensor is perfect, representing and operating ...