Method and system for kinematics-driven deep learning framework for seismic velocity estimation

A computer-implemented method of training a machine-learned model, comprising:

• obtaining a first initial velocity model;
• perturbing the first 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 form a first plurality of transformed seismic data sets, wherein traveltime information is enhanced in each of the transformed seismic data sets in the 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.

A system, comprising:

• a first initial velocity model;
• a forward modelling procedure;
• a machine-learned model;
• a drilling system comprising a wellbore planning system;
• a computer comprising one or more computer processors and a non-transitory computer readable medium, the computer configured to:
• receive a non-synthetic seismic data set for a subsurface region of interest;
• perturb the first initial velocity model to form a first plurality of velocity models;
• use the forward modelling procedure to simulate a first plurality of seismic data sets from the first plurality of velocity models;
• transform the first plurality of seismic data sets to form a first plurality of transformed seismic data sets, wherein traveltime information is enhanced in each of the transformed seismic data sets;
• train 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 one or more transformed seismic data sets;
• transform the non-synthetic seismic data set to form a non-synthetic transformed seismic data set;
• and
• process the non-synthetic seismic data set with the trained machine-learned model to predict a velocity model for the subsurface region of interest.