Iterative ml powered horizon interpretation workflow

A system comprising:

• a computer processor, and
• memory storing a data processing engine that comprises instructions that are executable by the computer processor to:
• receive first seismic data captured at a resource site by one or more sensors, the first seismic data comprising subsurface information and noise data;
• constrain a machine learning (ML) engine for analyzing the seismic data using a first dataset, wherein to constrain the ML engine comprises customizing or labeling the ML engine with one or more boundary condition data associated with one or more subsurface geo-properties comprised in the first seismic data;
• analyze the first seismic data in a first iteration based on the constrained ML engine to generate a multidimensional report;
• determine whether to execute a second analysis operation on the generated multidimensional report in a second iteration based on at least one of:
• an update of the first dataset used for labeling the ML engine;
• or
• a second dataset distinct from the first dataset and which indicates optimized boundary condition data associated with the resource site;
• in response to determining that a second analysis operation is not required, execute, using the multidimensional report one or more of:
• determining an optimal site associated with the subsurface of the resource site to install a wind turbine;
• determining an optimal drill rate for controlling a drill bit boring one or more holes into the subsurface associated with the resource site;
• determining an optimal subsurface location to store a captured gas;
• or
• executing one or more hydrocarbon exploration operations within the subsurface of the resource site.

A computer program for analyzing seismic data, the computer program comprising instructions, that when executed by a computer processor of a computing device, causes the computing device to:

• receive first seismic data captured at a resource site by one or more sensors, the first seismic data comprising subsurface information and noise data;
• constrain a machine learning (ML) engine for analyzing the seismic data using a first dataset, wherein to constrain the ML engine comprises customizing or labeling the ML engine with one or more boundary condition data associated with one or more subsurface geo-properties comprised in the first seismic data;
• analyze the first seismic data in a first iteration based on the constrained ML engine to generate a multidimensional report;
• determine whether to execute a second analysis operation on the generated multidimensional report in a second iteration based on at least one of:
• an update of the first dataset used for labeling the ML engine;
• or
• a second dataset distinct from the first dataset and which indicates optimized boundary condition data associated with the resource site;
• in response to determining that a second analysis operation is not required, execute, using the multidimensional report one or more of:
• determining an optimal site associated with the subsurface of the resource site to install a wind turbine;
• determining an optimal drill rate for controlling a drill bit boring one or more holes into the subsurface associated with the resource site;
• determining an optimal subsurface location to store a captured gas;
• or
• executing one or more hydrocarbon exploration operations within the subsurface of the resource site.