Method and system for generalizable deep learning framework for seismic velocity estimation robust to survey configuration

A non-transitory computer readable medium storing instructions executable by a compute processor, the instructions comprising functionality for:

• obtaining an initial velocity model;
• perturbing the initial velocity model to form a first plurality of velocity models;
• using a forward model to simulate a first plurality of seismic data sets from the first plurality of velocity models;
• transforming the first plurality of seismic data sets to a wavenumber-time domain to form a first plurality of transformed seismic data sets;
• training a machine-learned model using the first plurality of velocity models and the first plurality of transformed seismic data sets, wherein the machine-learned model is configured to accept transformed seismic data;
• obtaining a second seismic data set for a subsurface region of interest, wherein the second seismic data set is acquired according to a second survey configuration;
• transforming the second seismic data set to the wavenumber-time domain to form a second transformed seismic data set;
• and
• processing the second transformed data set with the trained machine-learned model to predict a second velocity model for the subsurface region of interest.

A system, comprising:

• an initial velocity model;
• a forward modelling procedure;
• a machine-learned model;
• a second seismic data set for a subsurface region of interest, wherein the second seismic data set is acquired according to a second survey configuration;
• and
• a computer comprising:
• one or more computer processors, and
• a non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:
• perturbing the initial velocity model to form a first plurality of velocity models;
• using the forward modelling procedure to simulate a first plurality of seismic data sets from the first plurality of velocity models;
• transforming the first plurality of seismic data sets to a wavenumber-time domain to form a first plurality of transformed seismic data sets;
• training the machine-learned model using the first plurality of velocity models and the first plurality of transformed seismic data sets, wherein the machine-learned model is configured to accept transformed seismic data;
• transforming the second seismic data set to the wavenumber-time domain to form a second transformed seismic data set;
• and
• processing the second transformed data set with the trained machine-learned model to predict a second velocity model for the subsurface region of interest.