Seismic Horizon Picking Using a Hopfield Network
A Hopfield neural network is used to solve the problem of seismic horizon picking. The input seismogram is pre-processed to produce seismic peak data. Pre-processing steps include envelope processing, thresholding, peak detection, and compression in time. Each peak represents a single seismic wavelet, and each pre-processed data item corresponds to one neuron in the Hopfield network. The constraint conditions for detecting seismic horizons are used to construct the Liapunov energy function, which is then used to extract connection weights between neurons. From the equation of motion, the next state value of each neuron can be calculated. Changing the value of a neuron decreases the energy. The system becomes stable when the value of each neuron no longer changes. A single horizon is extracted using the algorithm, and the extracted horizon is removed from the original seismic data before the next horizon is extracted. This process is repeated until no more horizons are extracted. Applying the technique to bright spot data, horizons extracted using the neural network were found to match those obtained by visual inspection.
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