  The task of determining the origin of a drifting object after it has been
located is highly complex due to the uncertainties in drift properties and
environmental forcing (wind, waves and surface currents). Usually the origin is
inferred by running a trajectory model (stochastic or deterministic) in
reverse. However, this approach has some severe drawbacks, most notably the
fact that many drifting objects go through nonlinear state changes underway
(e.g., evaporating oil or a capsizing lifeboat). This makes it difficult to
naively construct a reverse-time trajectory model which realistically predicts
the earliest possible time the object may have started drifting. We propose
instead a different approach where the original (forward) trajectory model is
kept unaltered while an iterative seeding and selection process allows us to
retain only those particles that end up within a certain time-space radius of
the observation. An iterative refinement process named BAKTRAK is employed
where those trajectories that do not make it to the goal are rejected and new
trajectories are spawned from successful trajectories. This allows the model to
be run in the forward direction to determine the point of origin of a drifting
object. The method is demonstrated using the Leeway stochastic trajectory model
for drifting objects due to its relative simplicity and the practical
importance of being able to identify the origin of drifting objects. However,
the methodology is general and even more applicable to oil drift trajectories,
drifting ships and hazardous material that exhibit non-linear state changes
such as evaporation, chemical weathering, capsizing or swamping. The
backtracking method is tested against the drift trajectory of a life raft and
is shown to predict closely the initial release position of the raft and its
subsequent trajectory.
