Semi-automated prestack seismic inversion workflow using temporal convolutional networks

Seismic inversion is the process of transforming seismic data into subsurface rock properties. Recent applications of deep learning in the seismic inversion domain have demonstrated great potential in revealing subsurface properties. In this study we test a temporal convolutional network (TCN) for the inversion of acoustic impedance, Vp/Vs ratio and density from a field dataset. The deep neural network learns to map sequences of angle-stack data to elastic properties using only data acquired at well locations. We show that the trained model produces promising volumetric property predictions despite the limited training data,. The method has the potential to semi-automate the prestack inversion workflow for relatively simple geological scenarios, since common steps such as the construction of an initial model and wavelet extraction are not explicitly required.