Inversion of the Gravity Gradiometry Data by ResUnet Network: An Application in Nordkapp Basin, Barents Sea

The study and assessment of the subsurface density distribution are vital for mining and oil and gas exploration. This can be achieved by the 3-D inversion of the observed gravity and gravity gradiometry (GG) data. Due to the ill-posedness of the geophysical inverse problem, the nonuniqueness and instability of solutions represent the main difficulties in inversion. In recent years, convolutional neural networks, especially U-net technology, have found wide applications in image processing, recognition, and reconstruction. This article proposes using this method for fast reconstruction of the subsurface density models based on the ResUnet technology. The developed new method was examined on two 3-D synthetic gravity and GG datasets inversion. The results show that the ResUnet network can reconstruct the density anomaly with sharp boundaries and is robust to the noise, making the solution stable.

Automated summary

The study and assessment of subsurface density distribution are crucial for mining and oil and gas exploration. 3-D inversion of observed gravity and gravity gradiometry data is used for this purpose. The nonuniqueness and instability of solutions due to the ill-posedness of the geophysical inverse problem pose challenges to inversion. This article proposes a fast reconstruction method using ResUnet technology for subsurface density models, which has shown promising results in synthetic datasets.