410 research outputs found
A prior regularized full waveform inversion using generative diffusion models
Full waveform inversion (FWI) has the potential to provide high-resolution
subsurface model estimations. However, due to limitations in observation, e.g.,
regional noise, limited shots or receivers, and band-limited data, it is hard
to obtain the desired high-resolution model with FWI. To address this
challenge, we propose a new paradigm for FWI regularized by generative
diffusion models. Specifically, we pre-train a diffusion model in a fully
unsupervised manner on a prior velocity model distribution that represents our
expectations of the subsurface and then adapt it to the seismic observations by
incorporating the FWI into the sampling process of the generative diffusion
models. What makes diffusion models uniquely appropriate for such an
implementation is that the generative process retains the form and dimensions
of the velocity model. Numerical examples demonstrate that our method can
outperform the conventional FWI with only negligible additional computational
cost. Even in cases of very sparse observations or observations with strong
noise, the proposed method could still reconstruct a high-quality subsurface
model. Thus, we can incorporate our prior expectations of the solutions in an
efficient manner. We further test this approach on field data, which
demonstrates the effectiveness of the proposed method
Three-dimensional generalization and verification of structured bounding surface model for natural clay
As the proposed structured bounding surface model can only be used to solve planar strain problems of natural soft clay, a three-dimensional adaptive failure criterion is adopted to improve the model to capture the three-dimensional behaviors of natural soft clay. The three-dimensional adaptive failure criterion incorporated in this model can cover the Lade-Duncan criterion and the Matsuoka-Nakai criterion as its special ones. The structured bounding surface model is generalized into three-dimensional stress space by using the three-dimensional adaptive failure criterion. After improved with the three-dimensional adaptive failure criterion, the model can be seen as a modified bounding surface model which considers the destructuration and three-dimensional behaviors and neglects the anisotropy of natural soft clay. The simulations of undrained compression and extension tests of K0 consolidation state Bothkennar clay shows the unimportance of neglecting anisotropy in this model. It was validated on Pisa clay that the improved model can simulate well the three dimensional behaviors of natural soft clay under true triaxial conditions
Formulation of structured bounding surface model with a destructuration law for natural soft clay
A destructuration law considering both isotropic destructuration and frictional destructuration was suggested to simulate the loss of structure of natural soft clay during plastic straining. The term isotropic destructuration was used to address the reduction of the bounding surface, and frictional destructuration addresses the decrease of the critical state stress ratio as a reflection of reduction of internal friction angle. A structured bounding surface model was formulated by incorporating the proposed destructuration law into the framework of bounding surface constitutive model theory. The proposed model was validated on Osaka clay through undrained triaxial compression test and one-dimensional compression test. The influences of model parameters and bounding surface on the performance of the proposed model were also investigated. It is proved by the good agreement between predictions and experiments that the proposed model can well capture the structured behaviors of natural soft clay
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Neural networks have shown great potential in accelerating the solution of
partial differential equations (PDEs). Recently, there has been a growing
interest in introducing physics constraints into training neural PDE solvers to
reduce the use of costly data and improve the generalization ability. However,
these physics constraints, based on certain finite dimensional approximations
over the function space, must resolve the smallest scaled physics to ensure the
accuracy and stability of the simulation, resulting in high computational costs
from large input, output, and neural networks. This paper proposes a general
acceleration methodology called NeuralStagger by spatially and temporally
decomposing the original learning tasks into several coarser-resolution
subtasks. We define a coarse-resolution neural solver for each subtask, which
requires fewer computational resources, and jointly train them with the vanilla
physics-constrained loss by simply arranging their outputs to reconstruct the
original solution. Due to the perfect parallelism between them, the solution is
achieved as fast as a coarse-resolution neural solver. In addition, the trained
solvers bring the flexibility of simulating with multiple levels of resolution.
We demonstrate the successful application of NeuralStagger on 2D and 3D fluid
dynamics simulations, which leads to an additional speed-up.
Moreover, the experiment also shows that the learned model could be well used
for optimal control.Comment: ICML 2023 accepte
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