Event picking in seismic data

A computing system comprising one or more processors and a memory system including 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 representing a subterranean volume, the seismic data including a plurality of signals;
• obtaining a machine learning model that is globally-trained to predict energy arrivals in the signals;
• predicting energy arrivals in a quality control portion of the plurality of signals of the seismic data using the machine learning model;
• determining that the predicted energy arrivals for the quality control portion are not accurate;
• in response to determining that the predicted energy arrivals are not accurate, training the machine learning model to predict the energy arrivals using a local training data set that represents the subterranean volume;
• predicting the energy arrivals in the seismic data using the machine learning model that was trained based at least in part on the training data set;
• and
• generating a velocity model based on the predicted energy arrivals.

A 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 representing a subterranean volume, the seismic data including a plurality of signals;
• obtaining a machine learning model that is globally-trained to predict energy arrivals in the signals;
• predicting energy arrivals in a quality control portion of the plurality of signals of the seismic data using the machine learning model;
• determining that the predicted energy arrivals for the quality control portion are not accurate;
• in response to determining that the predicted energy arrivals are not accurate, training the machine learning model to predict the energy arrivals using a local training data set that represents the subterranean volume;
• predicting the energy arrivals in the seismic data using the machine learning model that was trained based at least in part on the training data set;
• and
• generating a velocity model based on the predicted energy arrivals.