Estimating total organic carbon by a graph convolutional prediction model considering geological context weight

Key Points

• Enhanced predictions of total organic carbon were obtained using a graph convolutional model, improving accuracy significantly.
• The model integrates geological context information, yielding more precise carbon estimates in diverse geological settings.
• Analysis utilized a graph convolutional prediction model to understand the relationship between geological factors and organic carbon.
• This study highlights the need for incorporating geological context in predictions, potentially informing energy and environmental management.