Time lapse data reconstruction and time lapse data acquisition survey design for co2 monitoring

A method for seismic surveying, the method comprising:

• receiving a baseline dataset and a monitoring dataset;
• generating an output image based on the baseline dataset using a machine learning model;
• generating a selected output by removing one or more traces from the output image;
• determining a loss function by comparing the selected output with the monitoring dataset;
• adjusting the machine learning model based at least in part on the loss function;
• and
• reconstructing an interpolated monitoring dataset based on the monitoring dataset using the machine learning model.

A method for seismic surveying, the method comprising:

• receiving a monitoring dataset and a baseline dataset;
• reconstructing an interpolated monitoring dataset based on the monitoring dataset using a first machine learning model, wherein reconstructing comprises:
• generating an output based on the baseline dataset;
• generating a selected output by removing one or more traces from the output;
• adjusting the first machine learning model based at least in part on the selected output;
• and
• selecting one or more survey geometries for arranging physical sources and physical receivers in a seismic survey based at least in part on the interpolated monitoring dataset that was reconstructed and accuracies associated with the plurality of sparse monitoring datasets, wherein selecting includes:
• generating a decimated baseline dataset by removing one or more sources, receivers, or both from the baseline dataset;
• generating reconstructed monitoring datasets by inputting the plurality of sparse monitoring datasets to a second machine learning model;
• and
• comparing the reconstructed monitoring datasets to the baseline dataset.