  Sidescan sonar intensity encodes information about the changes of surface
normal of the seabed. However, other factors such as seabed geometry as well as
its material composition also affect the return intensity. One can model these
intensity changes in a forward direction from the surface normals from
bathymetric map and physical properties to the measured intensity or
alternatively one can use an inverse model which starts from the intensities
and models the surface normals. Here we use an inverse model which leverages
deep learning's ability to learn from data; a convolutional neural network is
used to estimate the surface normal from the sidescan. Thus the internal
properties of the seabed are only implicitly learned. Once this information is
estimated, a bathymetric map can be reconstructed through an optimization
framework that also includes altimeter readings to provide a sparse depth
profile as a constraint. Implicit neural representation learning was recently
proposed to represent the bathymetric map in such an optimization framework. In
this article, we use a neural network to represent the map and optimize it
under constraints of altimeter points and estimated surface normal from
sidescan. By fusing multiple observations from different angles from several
sidescan lines, the estimated results are improved through optimization. We
demonstrate the efficiency and scalability of the approach by reconstructing a
high-quality bathymetry using sidescan data from a large sidescan survey. We
compare the proposed data-driven inverse model approach of modeling a sidescan
with a forward Lambertian model. We assess the quality of each reconstruction
by comparing it with data constructed from a multibeam sensor.
