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Found 21 papers, 2 papers with code
Controllable seismic velocity synthesis using generative diffusion models
Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards.
Physics-informed neural wavefields with Gabor basis functions
Specifically, for the Helmholtz equation, we augment the fully connected neural network model with an adaptable Gabor layer constituting the final hidden layer, employing a weighted summation of these Gabor neurons to compute the predictions (output).
GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
The computation of the seismic wavefield by solving the Helmholtz equation is crucial to many practical applications, e. g., full waveform inversion.
Optimizing a Transformer-based network for a deep learning seismic processing workflow
StorSeismic is a recently introduced model based on the Transformer to adapt to various seismic processing tasks through its pretraining and fine-tuning training strategy.
Joint Microseismic Event Detection and Location with a Detection Transformer
Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution.
A prior regularized full waveform inversion using 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.
PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks
Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high quality velocity models and seismic images.
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
We use a two-stage training scheme in which the first stage, we learn the latent representations for the distribution of PDE parameters.
Microseismic source imaging using physics-informed neural networks with hard constraints
To be more specific, we modify the representation of the frequency-domain wavefield to inherently satisfy the boundary conditions (the measured data on the surface) by means of a hard constraint, which helps to avoid the difficulty in balancing the data and PDE losses in PINNs.
Efficient physics-informed neural networks using hash encoding
Physics-informed neural networks (PINNs) have attracted a lot of attention in scientific computing as their functional representation of partial differential equation (PDE) solutions offers flexibility and accuracy features.
Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion
However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples.
Transfer learning for self-supervised, blind-spot seismic denoising
To preempt the network's ability to predict the signal and reduce its opportunity to learn the noise properties, we propose an initial, supervised training of the network on a frugally-generated synthetic dataset prior to fine-tuning in a self-supervised manner on the field dataset of interest.
Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty
This work focuses on predicting the hypocenter locations using HypoPINN and investigates the propagation of uncertainties from the random realizations of HypoPINN's weights and biases using the Laplace approximation.
StorSeismic: A new paradigm in deep learning for seismic processing
We present StorSeismic, as a framework for seismic data processing, which consists of neural network pre-training and fine-tuning procedures.
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in seismic modeling and inversion.
Single Reference Frequency Loss for Multi-frequency Wavefield Representation using Physics-Informed Neural Networks
However, the neural network (NN) training can be costly and the cost dramatically increases as we train for multi-frequency wavefields by adding frequency to the NN multidimensional function, as the variation of the wavefield with frequency adds more complexity to the NN training.
The potential of self-supervised networks for random noise suppression in seismic data
Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as inversion.
MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
This is accomplished by applying two operations on the input data to the NN model: 1) The crosscorrelation of the input data (i. e., shot gather, seismic image, etc.)
Direct domain adaptation through reciprocal linear transformations
In the training stage, as expected, the input images are from the source domain, and the mean of auto-correlated images are evaluated from the target domain.
A data-driven choice of misfit function for FWI using reinforcement learning
In the workflow of Full-Waveform Inversion (FWI), we often tune the parameters of the inversion to help us avoid cycle skipping and obtain high resolution models.
ML-misfit: Learning a robust misfit function for full-waveform inversion using machine learning
Most of the available advanced misfit functions for full waveform inversion (FWI) are hand-crafted, and the performance of those misfit functions is data-dependent.
