Bathymetry Data Correction Using Global Optimization Method

The bathymetry data of marine bodies have been collected over a century, and the collected data have a wide range of resolution and accuracy. Acquisition of bathymetry data is very costly and time-consuming. One can use the old, low-quality bathymetry data to fill the gap in high-quality, recently acquired bathymetry data after correcting the old data to improve its quality so that it is comparable to the high-quality data. The old data correction can be treated as a nonlinear inverse problem. Simulated annealing (SA) global optimization method was used here in solving this problem. The two sets of data that were used are project survey (PS) and Vietnamese Navy Chart (VNC) data. The PS data were collected in 2000 in an offshore survey from the Vietnam coast in the South China Sea (SCS). The VNC data were obtained by digitizing VNC that was published in 1981. Inverse distance weighted (IDW) interpolation method was used for forward modeling. Weperformed the SA algorithm run starting at a high "temperature," then lowering the "temperature" gradually up to the "critical temperature" and then staying there for the rest of the run. The best model chosen by the algorithm showed an improvement of 63% from the original model. We then constructed a digital bathymetry model (DBM) of the study area with the combined corrected VNC and the PS data.     

Bathymetry Data Correction Using Global Optimization Method

The bathymetry data of marine bodies have been collected over a century, and the collected data have a wide range of resolution and accuracy. Acquisition of bathymetry data is very costly and time-consuming. One can use the old, low-quality bathymetry data to fill the gap in high-quality, recently acquired bathymetry data after correcting the old data to improve its quality so that it is comparable to the high-quality data. The old data correction can be treated as a nonlinear inverse problem. Simulated annealing (SA) global optimization method was used here in solving this problem. The two sets of data that were used are project survey (PS) and Vietnamese Navy Chart (VNC) data. The PS data were collected in 2000 in an offshore survey from the Vietnam coast in the South China Sea (SCS). The VNC data were obtained by digitizing VNC that was published in 1981. Inverse distance weighted (IDW) interpolation method was used for forward modeling. Weperformed the SA algorithm run starting at a high "temperature," then lowering the "temperature" gradually up to the "critical temperature" and then staying there for the rest of the run. The best model chosen by the algorithm showed an improvement of 63% from the original model. We then constructed a digital bathymetry model (DBM) of the study area with the combined corrected VNC and the PS data.