Removing residual airborne sensor motion for measuring seismic signals from a drone

Abstract

Acquiring seismic data using drones requires excellent knowledge of the drone’s motion since positional measurements made from an airborne sensor represent a combination of sensor and ground motion. Recent advancements in laser Doppler vibrometry and repeat lidar surveys show that the frequency and resolution of non-contact motion measurements is increasing to the point necessary for measuring seismic signals. We explore the conditions under which separation of sensor motion from ground motion can be accomplished in practice. We assume (i) that the translation and rotation of a stabilized airborne sensor follows an analytic form in time that is either known or can be estimated from the sensor’s measurements, (ii) that the seismic signal we observe has compact support contained within the measurement window, and (iii) that the ground motion can be described by a rigid translation. We analyze the effectiveness of our signal separation problem as a function of peak signal, sensor noise level, sensor rotation angle, and sensor point sampling density by defining a boundary where SNR = 0 dB for various combinations of these parameters. We find that under the set of assumptions, lower rotation angles, lower sensor noise, and denser point samplings on the ground provide better signal separation using our method.