Sediment layer tracking using neural networks

The detection of sediment layer interfaces in normal incidence acoustic reflection data is a requirement for automatic classification and geologic mapping of subsurface layers. The detection is difficult because of the constructive and destructive interference caused by the impedance changes in the sediment column and high scattering noise levels. The purpose of this work is to implement a procedure using neural networks that automatically detects the sediment layers from the envelope of acoustic reflections. The data was collected using a sub-bottom profiler that transmits a 2 to 10 kHz FM pulse. The detection procedure is a three step method: a first neural network removes most of the reflections due to random scatterers, a second neural network tracks the layers and a third algorithm recognizes the segments of detected layers corresponding to the same sediment interface Applied on different sub-bottom images, the procedure detects more than 80% of the layers correctly.