An improved algorithm for locating a gas source using inverse methods

We apply an inverse problem approach to locating a known gas source in a desert setting from simultaneous measurements of gas concentration and wind data. We use a random search algorithm with simulated annealing to generate candidate distributions of source strengths and positions. These distributions are then assessed by means of a cost function, which quantifies the degree to which the postulated source distribution accounts for the measured gas concentrations. We present results from using three cost functions with differing regularisation terms. We assess the robustness of these and the differing regularisation terms by the progressive addition of random noise and systematic offsets to the concentration data. We show that for our application, the best reconstructions are obtained by using a multiplicative regularisation parameter defined to minimise the total gas emissions.