Synchrosqueezing voices through deep neural networks for horizon interpretation

Abstract

Horizon picking is a crucial element in reservoir characterization, but it remains a labor-intensive process. Manually interpreting horizons across thousands of vertical seismic slices in a 3D seismic survey significantly increases the time and labor required for this task. Although several automated methods for extracting the horizons in seismic images have been developed, their effectiveness can be hampered by interruptions in lateral continuity, such as faults and noise. In addition, closely spaced horizons pose a challenge, making it even more difficult to recognize their exact position. To track the horizons through a 3D seismic volume, other seismic attributes extracted from the 3D seismic data must be used. We suggest using spectral voice components together with the original seismic amplitudes to track the surfaces of the target horizons. We generate the time-frequency spectrum using a high-resolution method, namely the synchrosqueezing wavelet transform (SWT), and the real part of the complex SWT spectrum is the voice component. We import the spectral voice component and the seismic amplitude into a neural network. A deep convolutional neural network is used to track the horizon surfaces within a 3D seismic volume. We evaluate this application on a field seismic data set where closely spaced thin layers are located within a complex faulted formation with noisy seismic data with a low signal-to-noise ratio. The integration of the amplitude and phase within the attribute of the voice component indicates that it improves the quality of the generated horizons, especially compared with using the seismic amplitude only for this task. An example from field data demonstrates the ability of our method to accurately delineate the horizons across fault surfaces and in the immediate vicinity of unconformities. This exceeds the current limits of the existing methods for horizon detection.