Estimating subsurface properties using a semisupervised neural network approach

Estimating static rock properties (e.g., density and porosity) from seismic and well logs is one of the essential but challenging tasks in subsurface interpretation and characterization. To compensate for the sparsity of well logs and the limited bandwidth of seismic data, a semisupervised learning workflow is used for efficiently integrating seismic and logs and simultaneously estimating multiple subsurface properties. It consists of two...