Seismic Image Enhancement Using Teacher-Student Diffusion Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing seismic image prediction models trained solely on synthetic datasets fail to generalize well to real-world seismic imaging scenarios due to overfitting, leading to inaccuracies and poor performance when applied to actual seismic data.
Innovation Solution
A teacher-student knowledge distillation framework using a teacher NN algorithm trained on synthetic image pairs and a student NN algorithm trained on real data, leveraging a diffusion process based on a Brownian bridge diffusion model to enhance low-resolution seismic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a prediction model is trained solely on synthetic datasets, then training efficiency is improved, but generalization performance to real-world scenarios deteriorates
Solution Approach 1:
The patent introduces a two-stage training framework where a teacher model trained on synthetic data serves as an intermediary to generate pseudo-labels for real data. This intermediary mechanism allows the system to leverage the efficiency of synthetic data training while bridging the gap to real-world performance through the teacher-student knowledge distillation process.
Solution Approach 2:
The system performs preliminary training on synthetic datasets to establish a teacher model before applying it to real data. This preliminary action on synthetic data provides a head start in learning general patterns, which then serves as a foundation for subsequent fine-tuning on real datasets, combining the benefits of both approaches.
2Measurement precision
If a model is trained on real datasets with limited annotations, then real-world accuracy is improved, but training data requirements increase
Solution Approach 1:
The teacher model generates its own training data by producing pseudo-labels for real seismic images. This self-service capability allows the system to create its own training dataset without requiring external annotation resources, effectively unlimited the quantity of training data available while maintaining high real-world accuracy.
Solution Approach 2:
The system changes the parameter of data availability by transforming the teacher model's predictions into pseudo-labels that serve as training targets. This parameter change allows the system to convert unlimited unlabeled real data into labeled training data, eliminating the constraint of limited annotated datasets.
3Loss of time
If synthetic data is used for training, then data acquisition time is reduced, but image realism and applicability deteriorate
Solution Approach 1:
The training process is segmented into two distinct phases: first training on synthetic data to learn general patterns quickly, then fine-tuning on real data to acquire realism. This segmentation allows the system to benefit from the speed of synthetic data generation while ultimately achieving real-world applicability through the second phase.
Solution Approach 2:
The teacher model acts as an intermediary that transfers knowledge from the synthetic data domain to the real data domain. This intermediary enables the system to bridge the gap between synthetic and real imagery, allowing fast synthetic training to ultimately produce models that work on real seismic images.
Data Source
AI summary
Systems and methods are disclosed relating to image enhancement. In an example, a first training of a machine learning (ML) algorithm can be implemented using a synthetic training dataset to provide a first model. The synthetic training dataset can include low-resolution and high-resolution synthetic image pairs. A second training of the ML algorithm can be implemented using a real training dataset and an output of the first model to provide a second model. The real training dataset can include real low-resolution images. A low-resolution image can be enhanced using the second model to provide an enhanced image of the low-resolution image.