Graph neural networks to improve ground-based monitoring

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

• generating, by the one or more processors, training data comprising:
• a first set of training data representing a simulated event,
• a second set of training data, and
• a third set of training data,
• wherein the second set of training data and the third set of training data each represent a distinct set of sensor values corresponding to distinct simulated observation systems, and each distinct sensor value of the second set of training data and the third set of training data comprises an associated measurement value;
• introducing, by the one or more processors, a variation value to each measurement value of the second set of training data and the third set of training data to generate a first set of modified training data and a second set of modified training data;
• generating, by the one or more processors and using the first set of modified training data and a second set of modified training data, a plurality of graphs, each graph of the plurality of graphs based on at least one of the first set of training data, the first set of modified training data, or the second set of modified training data;
• and training, using the plurality of graphs as input, a neural network such that the trained neural network generates a variation value correction for each measurement value of the first set of modified training data and the second set of modified training data.

A system for monitoring seismic events, comprising:

• a network of distributed receivers;
• and
• a seismic monitoring system that comprises one or more processors configured to perform operations comprising:
• generating, by the one or more processors, seismic training data comprising:
• a first set of training data representing a simulated seismic event,
• a second set of training data, and
• a third set of training data,
• wherein the second set of training data and the third set of training data each represent a distinct set of sensor values corresponding to distinct simulated seismic observation systems, and each distinct sensor value of the second set of training data and the third set of training data comprises an associated arrival time value;
• introducing, by the one or more processors, a time offset to each arrival time value of the second set of training data and the third set of training data to generate a first set of modified training data and a second set of modified training data;
• generating, by the one or more processors and using the first set of modified training data and a second set of modified training data, a plurality of graphs, each graph of the plurality of graphs based on at least one of the first set of training data, the first set of modified training data, or the second set of modified training data;
• and
• training, using the plurality of graphs as input, a neural network such that the trained neural network generates a time value correction for each arrival time value of the first set of modified training data and the second set of modified training data.