Machine learning driven dispersion curve picking

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:

• receiving seismic data comprising a signal;
• generating a semblance in a frequency-wavenumber domain for the seismic data, wherein the semblance represents a coherence of the signal in the frequency-wavenumber domain;
• extracting one or more wave energy modes in the semblance using a machine learning model trained to identify dispersion curves in the semblance based on a visible characteristic of the dispersion curves, wherein extracting the one or more wave energy modes in the semblance using the machine learning model comprises:
• using an encoder network of the machine learning model, the encoder network having initialized tensors that encode spatial features of an input semblance as strided convolution down-samples;
• and
• using a decoder network to construct a segmentation mask as a filter at a same scale as the input semblance to be applied to the semblance in conjunction with identifying the dispersion curves in the semblance;
• and
• generating a model representing surface wave propagation based at least in part on the identified one or more wave energy modes.

A computing system, comprising:

• one or more processors;
• and
• a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
• receiving seismic data comprising a signal;
• generating a semblance in a frequency-wavenumber domain for the seismic data, wherein the semblance represents a coherence of the signal in a frequency-wavenumber domain;
• extracting one or more wave energy modes in the semblance using a machine learning model trained to identify dispersion curves in the semblance based on a visible characteristic of the dispersion curves, wherein extracting the one or more wave energy modes in the semblance using the machine learning model comprises:
• using an encoder network of the machine learning model, the encoder network having initialized tensors that encode spatial features of an input semblance as strided convolution down-samples;
• and
• using a decoder network to construct a segmentation mask as a filter at a same scale as the input semblance to be applied to the semblance in conjunction with identifying the dispersion curves in the semblance;
• and
• generating a model representing surface wave propagation based at least in part on the identified one or more wave energy modes.