Seafloor identification in sonar imagery via simulations of Helmholtz equations and discrete optimization
For researchers in seafloor acoustics and sonar imaging, this work offers a computationally efficient inversion method, though it is an incremental improvement over existing techniques.
The paper presents a multiscale approach for identifying seafloor features in sonar imagery by coupling Helmholtz equations and geometrical optics, enabling detailed recovery of seafloor parameters including material type. The method reduces computational cost via a pre-computed library of acoustic responses.
We present a multiscale approach for identifying features in ocean beds by solving inverse problems in high frequency seafloor acoustics. The setting is based on Sound Navigation And Ranging (SONAR) imaging used in scientific, commercial, and military applications. The forward model incorporates multiscale simulations, by coupling Helmholtz equations and geometrical optics for a wide range of spatial scales in the seafloor geometry. This allows for detailed recovery of seafloor parameters including material type. Simulated backscattered data is generated using numerical microlocal analysis techniques. In order to lower the computational cost of the large-scale simulations in the inversion process, we take advantage of a pre-computed library of representative acoustic responses from various seafloor parameterizations.