Subsurface imaging techniques with improved resolution is the industry's pressing challenge. In this paper, we present a novel Machine Learning based data processing method to generate accurate seismic profiles. Through this method, the processing of humongous acoustic data and subsequent inference of subsurface properties is significantly expedited. The work being presented is an application of a recently developed novel class of algorithms called the Physics Informed Neural Networks (PINNs).
The method has been proven in its efficacy for approximating the solutions to Partial Differential Equations (PDE) governing complex physical phenomena and in data-driven discovery of the underlying PDEs such as the Navier-Stokes system.
Herein, we have followed a two-stage procedure. Stage one is the accurate prediction of spatiotemporal propagation of a seismic pulse through a simulated domain resembling thesubsurface. For this we developed a simulator to generate vast amounts of virtual seismic data. Dense Deep Neural Networks (DNN) were used to overfit the data so that it approximates the solution to the wave equations. This was followed by the data-driven discovery of local velocities in different reflector layers. In the second stage, we have extended the algorithm to infer the real subsurface velocity profile based on the data collected at receiver positions during an actual seismic survey from Volve, a field on the Norwegian continental shelf.
Moreover, we have bench-marked and established the performance of various models in their ability to learn the receiver data obtained from a simulated 2D seismic survey. The results are promising as the network chosen can learn the receiver data with a minimal number of iterations and with near-perfect accuracy. The models were further tested in their ability to represent solutions of the wave equation in the spatiotemporal domain and in their ability to represent the actual subsurface velocity profiles.
The novelty of this paper lies in the overlap of a nascent-stage deep learning tool, called PINNs, with seismic data processing to expedite and improvise high-resolution subsurface imaging. For this, various other uncommon developments were done such as that of adapting PINN as a dynamic wave function approximator. Moreover, the effectiveness of the proposed method is numerically demonstrated by the excellent accuracy obtained when validated against the velocity profile from the field as well as the velocity profile from the simulator.