Range-independent Model Research Articles

Deep-learning geoacoustic inversion using multi-range vertical array data in shallow water.

A multi-range vertical array data processing (MRP) method based on a convolutional neural network (CNN) is proposed to estimate geoacoustic parameters in shallow water. The network input is the normalized sample covariance matrices of the broadband multi-range data received by a vertical line array. Since the geoacoustic parameters (e.g., bottom sound speed, density, and attenuation) have different scales, the multi-task learning is used to estimate these parameters simultaneously. To reduce the influence of the uncertainty of the source position, the training and validation data are composed of the simulation data of different source depths. Simulation results demonstrate that compared with the conventional matched-field inversion (MFI), the CNN with MRP alleviates the coupling between the geoacoustic parameters and is more robust to different source depths in the shallow water environment. Based on the inversion results, better localization performance is achieved when the range-dependent environment is assumed to be a range-independent model. Real data from the East China Sea experiment are used to validate the MRP method. The results show that, compared with the MFI and the CNN with single-range vertical array data processing, the use of geoacoustic parameters from MRP achieves better localization performance.

Quantifying the Impacts of Acoustic Target Detections Using a Range-Independent Model Versus Range-Dependent Model

The purpose of this study is to identify and quantify the difference in detections of a subsurface target from a subsurface sensor source between range-independent and range-dependent versions of the same acoustic propagation model. Environmental data were pulled from open source databases to provide an application for the comparison and using novel measures of merit, the authors were able to quantify the difference in detection performance between models. This study suggests that provided multiple types of environments are considered, it is possible to use a range-independent model to give good approximations to the accuracy of detections, one would achieve using a full range-dependent sound propagation model.

Effective parameters for matched field geoacoustic inversion in range-dependent environments

Using the well-established technique of geoacoustic inversion, one can estimate a set of acoustic sea-bed parameters from sonar array data. Simultaneously, one can search for geometric parameters such as range, water depth, and hydrophone depth. When the technique is applied in a range-dependent environment, there is a potentially much larger set of parameters to match, unless one has perfect knowledge of the bathymetry. From the point of view of optimization, one needs to handle uncertainties in bathymetry without hugely increasing the amount of computation. A simple time-domain view (which is shown to be equivalent to the adiabatic approximation) suggests that it is sufficient to use a range-independent model with an empirical "effective" depth even when the bottom is not flat. In fact, there is a set of effective environments that will suffice; one can choose whichever is the most convenient. The success of this concept is demonstrated with some test cases from a recent Geoacoustic Inversion Techniques Workshop.