Least-squares reverse time migration via deep learning-based updating operators
Two common issues of least-squares reverse time migration (LSRTM) consist of the many
iterations required to produce substantial subsurface imaging improvements and the …
iterations required to produce substantial subsurface imaging improvements and the …
Least-squares reverse time migration using convolutional neural networks
Least-squares reverse time migration (LSRTM) has the potential to reconstruct a high-
resolution image of subsurface reflectivity. However, the current data-domain LSRTM …
resolution image of subsurface reflectivity. However, the current data-domain LSRTM …
Deep-learning for accelerating prestack correlative least-squares reverse time migration
Prestack least-squares reverse time migration based on a correlative objective function
denoted as PCLSRTM can retrieve a higher quality image of the subsurface than standard …
denoted as PCLSRTM can retrieve a higher quality image of the subsurface than standard …
Minibatch least-squares reverse time migration in a deep-learning framework
Migration techniques are an integral part of seismic imaging workflows. Least-squares
reverse time migration (LSRTM) overcomes some of the shortcomings of conventional …
reverse time migration (LSRTM) overcomes some of the shortcomings of conventional …
A 2-D local correlative misfit for least-squares reverse time migration with sparsity promotion
Least-squares reverse time migration (LSRTM) attempts to produce a high-quality image for
complicated subsurface structures. However, large amplitude discrepancies between the …
complicated subsurface structures. However, large amplitude discrepancies between the …
Convolutional neural-network-based reverse-time migration with multiple reflections
S Huang, D Trad - Sensors, 2023 - mdpi.com
Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures
and offer high-resolution images of the complex subsurface. Nevertheless, there are some …
and offer high-resolution images of the complex subsurface. Nevertheless, there are some …
Least-squares reverse time migration with a multiplicative Cauchy constraint
One of reverse time migration's main limitations is that an unscaled adjoint operator is prone
to produce images with low resolution, inaccurate amplitudes, and even artifacts. Least …
to produce images with low resolution, inaccurate amplitudes, and even artifacts. Least …
Least-squares reverse-time migration with sparsity constraints
Least-squares reverse-time migration (RTM) works with an inverse operation, rather than an
adjoint operation in a conventional RTM, and thus produces an image with a higher …
adjoint operation in a conventional RTM, and thus produces an image with a higher …
Consistent least-squares reverse time migration using convolutional neural networks
The data-consistency item is a necessary condition for a reliable solution to the inverse
problem. However, the current supervised-based deep-learning reconstruction approaches …
problem. However, the current supervised-based deep-learning reconstruction approaches …
A structural rank reduction operator for removing artifacts in least-squares reverse time migration
Least-square reverse time migration (LSRTM) has been widely accepted because of its
exceptional performance in mitigating migration artifacts and preserving the reflection …
exceptional performance in mitigating migration artifacts and preserving the reflection …