The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function is generated from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov function and derive the equation of motion. According to the equation of motion, each neuron is updated until no change. The linking of the converged network neurons represents the best polyline in velocity picking. We have experiments on simulated seismic data. The picking results are good.
|Number of pages||5|
|Journal||SEG Technical Program Expanded Abstracts|
|State||Published - 1 Jan 2015|
|Event||SEG New Orleans Annual Meeting, SEG 2015 - New Orleans, United States|
Duration: 18 Oct 2011 → 23 Oct 2011