Markov-chain-inspired search for MH370

Abstract

Markov-chain models are constructed for the probabilistic description of the drift of marine debris from Malaysian Airlines flight MH370. En route from Kuala Lumpur to Beijing, the MH370 mysteriously disappeared in the southeastern Indian Ocean on 8 March 2014, somewhere along the arc of the 7th ping ring around the Inmarsat-3F1 satellite position when the airplane lost contact. The models are obtained by discretizing the motion of undrogued satellite-tracked surface drifting buoys from the global historical data bank. A spectral analysis, Bayesian estimation, and the computation of most probable paths between the Inmarsat arc and confirmed airplane debris beaching sites are shown to constrain the crash site, near 25 • S on the Inmarsat arc.PACS numbers: 02.50.Ga; 47.27.De; 92.10.Fj Application of tools from ergodic theory on historical Lagrangian ocean data is shown to constrain the crash site of Malaysian Airlines flight MH370 given airplane debris beaching site information. The disappearance of flight MH370 constitutes one of the most enigmatic episodes in the history of commercial aviation. The tools employed have far-reaching applicability as they are particularly well suited in inverse modeling, critical for instance in revealing contamination sources in the ocean and the atmosphere. The only requirement for their success is sufficient spatiotemporal Lagrangian sampling.

Observation and quantification of inertial effects on the drift of floating objects at the ocean surface

“…The nominal range of τ values is clarified in Section IV B. In (8) γ := µ a µ > 0; (9) parameter k > 0 (resp., k a > 0) determines the projected length scale of the submerged (resp., emerged) inertial particle piece upon multiplication by the immersion (resp., emersion) depth (resp., height); and 0 < K ≤ 1 is a correction factor that accounts for the effects of particle's shape deviating from spherical, satisfying 42…”

“…The assessment of motions of floating matter in the ocean is of importance for a number of key reasons. These range from improving search-and-rescue operations at sea 1,2 ; to better understanding the drift of flotsam of different nature including macroalgae such as Sargassum [3][4][5] , plastic litter 6,7 , airplane wreckage 8,9 , tsunami debris 10,11 , sea-ice pieces 12 , larvae 13,14 , and oil 15,16 ; to better interpreting "Lagrangian" observations in the ocean 17,18 . At present, largely piecemeal, ad-hoc approaches are taken to simulate the effects of ocean currents and winds on the drift of floating objects.…”

Estimating the Travel Time and the Most Likely Path from Lagrangian Drifters

“…There are a variety of works that use Markov transition matrices for different aims to this work. Ser-Giacomi et al (2015b) and Miron et al (2019) look at probable paths, where both of these works find a path going between two points in a certain number of days using a dynamic program. Froyland et al (2014) and Miron et al (2017) study ocean dynamics by analyzing eigenvalues of the transition matrix.…”

“…The Lagrangian study of transport and mixing in the ocean is of fundamental interest to ocean modelers (van Sebille et al , 2009LaCasce 2008). In particular, the analysis of data obtained from Lagrangian drifting objects greatly contribute to our knowledge of ocean circulation, e.g., through analyzing the accuracy of numerical and stochastic models (Huntley et al 2011;Sykulski et al 2016), or the use of drifter data to better understand various pathways and where to search for marine debris (Miron et al 2019; van Sebille et al 2012; McAdam and . Meehl (1982) used ship-drift data to create a surface velocity dataset on a 58 3 58 grid.…”

Building a Maxey–Riley framework for surface ocean inertial particle dynamics

“…We focus on the North Atlantic for simplicity as subtropical gyres in the other oceans behave similarly. The exception is the Indian Ocean, where aggregation of inertial particles is not so evident, suggesting that ocean and atmospheric conditions are peculiar there 23,92 .…”