An improved algorithm for locating a gas source using inverse methods

By L. Thomson, B. Hirst, Graham M. Gibson, S. Gillespie, Philip Jonathan, K. Skeldon, Miles J. Padgett. 2007

139 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Global and Planetary Change, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5c0a2f8e644de0127fdfe84344c14f0924c406be

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In Global and Planetary Change

1. Philip Jonathan

Citations in the corpus

• A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors (2017, methodology): “The rule for accepting change in state is based on the Boltzmann probability distribution [51] , given as:”
• Locating and quantifying gas emission sources using remotely obtained concentration data (2012, methodology): “ model with stochastic parameters and Bayesian inference using Markov chain Monte Carlo (MCMC). It is noted that the main distinguishing feature of Bayesian inference as opposed to optimisation (e.g. Thomson et al. (2007)) for source estimation is that the former estimates probability distributions ”