# Estimation of 2D Velocity Model using Acoustic Signals and Convolutional Neural Networks

## TL;DR

This paper introduces a convolutional neural network model to estimate 2D velocity models of underwater objects from acoustic signals, addressing the challenge of noisy echo data in non-transparent mediums.

## Contribution

It presents a novel Encoder-Decoder CNN architecture trained on simulated data to accurately determine object localization and shape from acoustic echoes.

## Key findings

- Achieved 98.58% intersection over union metric
- Attained 75.88% precision in localization
- Reached 64.69% sensitivity in detection

## Abstract

The parameters estimation of a system using indirect measurements over the same system is a problem that occurs in many fields of engineering, known as the inverse problem. It also happens in the field of underwater acoustic, especially in mediums that are not transparent enough. In those cases, shape identification of objects using only acoustic signals is a challenge because it is carried out with information of echoes that are produced by objects with different densities from that of the medium. In general, these echoes are difficult to understand since their information is usually noisy and redundant. In this paper, we propose a model of convolutional neural network with an Encoder-Decoder configuration to estimate both localization and shape of objects, which produce reflected signals. This model allows us to obtain a 2D velocity model. The model was trained with data generated by the finite-difference method, and it achieved a value of 98.58% in the intersection over union metric 75.88% in precision and 64.69% in sensibility.