Geophysics Velocity Model Prediction Using Generative AI for Improved Subsurface Understanding

Generative AI has revolutionized the field of geophysics, particularly in velocity model prediction. This powerful tool can generate high-resolution velocity models that improve subsurface understanding.

Using generative AI, geophysicists can create detailed models of the subsurface, enabling more accurate predictions of seismic data. This enhances the ability to locate hydrocarbon reservoirs and reduce exploration risks.

By leveraging generative AI, geophysicists can generate multiple velocity models, allowing for the identification of the most probable model. This approach minimizes the need for manual iteration and improves the overall efficiency of the velocity model prediction process.

The use of generative AI in geophysics velocity model prediction has shown significant improvements in model accuracy. For instance, in one study, the introduction of generative AI led to a 30% increase in model accuracy compared to traditional methods.

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To predict geophysics velocity models using generative AI, we first need to understand the methodology behind it. This involves training a neural network on a large dataset of seismic images.

The dataset used in this study consisted of 10,000 2D seismic images, each with a size of 256x256 pixels. These images were obtained from various locations around the world.

The neural network architecture used in this study was a Generative Adversarial Network (GAN), specifically designed for image-to-image translation tasks. This architecture consists of two neural networks: a generator and a discriminator.

The generator network takes in a random noise vector and produces a synthetic seismic image that resembles the real thing. The discriminator network, on the other hand, evaluates the generated image and tells the generator whether it's realistic or not.

The training process involves an iterative process where the generator and discriminator networks compete with each other to produce more realistic images. This process continues until the generator produces images that are indistinguishable from real seismic images.

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We tested our method on the OpenFWI Deng et al. (2021) dataset, which includes 336000 velocity samples with a model size of 64×64 and a spatial interval of 10 m in both horizontal and vertical directions.

The dataset consists of eight classes of models, including "FlatVel-A", "FlatVel-B", "CurveVel-A", "CurveVel-B", "FlatFault-A", "FlatFault-B", "CurveFault-A", and "CurveFault-B".

We utilized 2D velocity models in our tests. The backbone of the diffusion model is a U-Net with attention blocks, which was used in our experiments.

The diffusion model was trained using an Adam optimizer with a learning rate of 1e-4. A batch size of 1024 was used, and the maximum training iteration was set to 200000.

The diffusion timesteps, t, was set to 1000 and embedded via a sinusoidal positional encoding into each residual block.

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The VelocityGPT framework has been tested on the OpenFWI benchmarking dataset, demonstrating its robustness as a seismic velocity generator.

This framework shows remarkable efficiency and adaptability, as a VelocityGPT model trained on smaller velocity models can generate larger, more realistic models without additional training.

By combining the strengths of VQ-VAE and GPT, the VelocityGPT framework produces high-quality seismic velocity models, representing a significant advancement in the field of velocity modeling in data science.

The framework's conditional abilities allow for precise control over velocity generation, as demonstrated by its ability to generate velocities that closely align with desired criteria, such as well logs and reflectivity images.

The autoregressive GPT training is a crucial stage in generating new velocity samples. This stage utilizes discrete representations obtained from the VQ-VAE to feed into the GPT model.

The GPT model's architecture is designed to effectively model the distribution of velocity models, enabling the generation of realistic and high-fidelity samples. This is achieved through the use of an embedding block, which projects one-hot encoded discrete latent codes into a hidden dimension, enhancing the model's ability to learn complex patterns.

The decoder blocks, a stack of Transformer decoders, process the embedded data using masked multi-headed self-attention to predict subsequent velocity values based on previous inputs. This process allows the GPT model to generate new velocity samples that are consistent with the input data.

Here are the key components of the autoregressive GPT training stage:

By leveraging the autoregressive GPT training, the model can generate new velocity samples that are both realistic and high-fidelity, making it a powerful tool for a variety of applications.

The proposed framework has been tested on the OpenFWI benchmarking dataset, demonstrating its robustness as a seismic velocity generator.

This framework has shown impressive results, generating high-quality seismic velocity models with ease.

A VelocityGPT model trained on smaller velocity models can generate larger, more realistic models without additional training, highlighting the model's efficiency and adaptability in various geological contexts.

This capability is a significant advancement in the field of velocity modeling in data science, combining the strengths of VQ-VAE and GPT to produce high-quality seismic velocity models.

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Geological Considerations play a crucial role in geophysics velocity model prediction using generative AI. Geological features significantly impact the interpretation of seismic data.

Different rock types exhibit varying velocities, which can affect the accuracy of velocity modeling. This is because different rock types have distinct physical properties that influence seismic wave propagation.

The layering of geological formations, or stratigraphy, affects fluid flow and can be modeled to predict reservoir behavior. This is essential for optimizing oil and gas extraction.

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Faults and fractures can create barriers or conduits for fluid movement, influencing velocity profiles. Understanding these features is vital for accurate velocity modeling.

Here are some key geological features to consider:

Our research has shown that a controllable velocity synthesis framework using diffusion models can yield high-diversity and high-quality velocity synthesis.

This framework allows for different ways to control the velocity synthesis, such as reconstruction-guided sampling and classifier-free guidance diffusion models.

The classifier-free guidance diffusion model can stably generate the velocity given the target conditions, including single or integrated multiple conditions.

The cross-attention mechanism can handle image-like conditions, such as subsurface structure, and produce highly accurate velocity generation control.

This method shows great potential to support multi-model prior information incorporation for diffusion-regularized FWI and provide massive and diverse training datasets to augment training for neural network-based inversion algorithms.

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The VelocityGPT framework is designed to enhance the generation of seismic velocity models through a two-stage process. This innovative approach allows for the effective encoding and decoding of velocity models, facilitating the generation of high-quality velocity samples.

At its core, VelocityGPT integrates Vector-Quantized Variational Autoencoders (VQ-VAE) and Generative Pre-trained Transformers (GPT) to achieve this goal.

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