4 research outputs found
Deep-pretrained-FWI: combining supervised learning with physics-informed neural network
An accurate velocity model is essential to make a good seismic image.
Conventional methods to perform Velocity Model Building (VMB) tasks rely on
inverse methods, which, despite being widely used, are ill-posed problems that
require intense and specialized human supervision. Convolutional Neural
Networks (CNN) have been extensively investigated as an alternative to solve
the VMB task. Two main approaches were investigated in the literature:
supervised training and Physics-Informed Neural Networks (PINN). Supervised
training presents some generalization issues since structures, and velocity
ranges must be similar in training and test set. Some works integrated
Full-waveform Inversion (FWI) with CNN, defining the problem of VMB in the PINN
framework. In this case, the CNN stabilizes the inversion, acting like a
regularizer and avoiding local minima-related problems and, in some cases,
sparing an initial velocity model. Our approach combines supervised and
physics-informed neural networks by using transfer learning to start the
inversion. The pre-trained CNN is obtained using a supervised approach based on
training with a reduced and simple data set to capture the main velocity trend
at the initial FWI iterations. We show that transfer learning reduces the
uncertainties of the process, accelerates model convergence, and improves the
final scores of the iterative process.Comment: Paper present at machine Learning and the Physical Sciences workshop,
NeurIPS 202
Deep-tomography: iterative velocity model building with deep learning
The accurate and fast estimation of velocity models is crucial in seismic
imaging. Conventional methods, like Tomography and Full-Waveform Inversion
(FWI), obtain appropriate velocity models; however, they require intense and
specialized human supervision and consume much time and computational
resources. In recent years, some works investigated deep learning(DL)
algorithms to obtain the velocity model directly from shots or migrated angle
panels, obtaining encouraging predictions of synthetic models. This paper
proposes a new flow to increase the complexity of velocity models recovered
with DL. Inspired by the conventional geophysical velocity model building
methods, instead of predicting the entire model in one step, we predict the
velocity model iteratively. We implement the iterative nature of the process
when, for each iteration, we train the DL algorithm to determine the velocity
model with a certain level of precision/resolution for the next iteration; we
name this process as Deep-Tomography. Starting from an initial model that
roughly approaches the true model, the Deep-Tomography is able to predict an
appropriate final model, even in complete unseen data, like the Marmousi model.Comment: 27 pages, 9 figures. First manuscript version submitted to
Geophysical Journal International in February 202