Decoding Complex Flight Kinematics from Seismic and Acoustic Signatures Using a Bayesian Framework

Aircraft generate distinctive Doppler signatures in seismic and acoustic recordings, yet most analyses assume constant-velocity flight. In reality, aircraft frequently accelerate, decelerate, and turn, producing more complex signals that demand refined interpretation. We develop a unified framework integrating analytical modeling, finite-difference simulations, and Bayesian inversion to recover full flight kinematics from seismic and acoustic data. The approach accurately reconstructs acceleration, deceleration, and turning trajectories, validated using both field observations and archival datasets, including the September 11, 2001 aircraft impact on the World Trade Center. Results resolve previously inaccessible parameters such as acceleration profiles and turning radii, highlighting the potential of seismoacoustic monitoring for aviation safety, airspace management, and forensic event reconstruction.