Spatial Context Generator for Seismic Data Interpretation
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Solution Overview
Problem
Human interpretation of seismic data is labor-intensive, expensive, and lacks repeatability and reliability, while modern machine learning algorithms require large training datasets that can be prohibitively costly to obtain in the context of seismic analysis.
Innovation Solution
The use of a conditional Generative Adversarial Network (cGAN) to generate simulated seismic datasets that provide contextual spatial information, allowing for the creation of larger, contextualized datasets from partial seismic information, which can be used to train machine learning models for improved seismic interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human interpretation methods are used for seismic data, then expertise and geological understanding can be applied, but the process becomes labor-intensive and expensive
Solution Approach 1:
The patent uses Generative Adversarial Networks to create synthetic seismic data that copies the statistical and spatial characteristics of real seismic data. This allows machine learning models to be trained on large volumes of synthetic data without requiring equivalent volumes of expensive real seismic data, thereby improving productivity while maintaining interpretation quality through realistic data simulations
Solution Approach 2:
The patent transforms the interpretation process by changing key parameters: replacing manual human interpretation with automated machine learning models, and transforming limited real data into expanded synthetic datasets. This parameter change enables high-throughput processing while maintaining geological accuracy through properly trained models
2Productivity
If machine learning algorithms are used for seismic interpretation, then productivity and repeatability are improved, but large training datasets are required which are costly to obtain
Solution Approach 1:
The core invention uses Generative Adversarial Networks to copy the essential characteristics of real seismic data into synthetic datasets. The GAN architecture learns the statistical properties, spatial correlations, and geological features from limited real data, then generates abundant synthetic copies that can be used for training machine learning models without requiring large volumes of expensive real seismic data
Solution Approach 2:
The patent performs preliminary data preparation by generating synthetic training data before the actual machine learning model training. This preliminary action of creating synthetic datasets in advance allows the machine learning models to be pre-trained on large volumes of realistic data, improving their performance and reducing the need for extensive real data collection later
3Measurement precision
If more real seismic data is collected for training, then model accuracy improves, but data acquisition costs and time increase significantly
Solution Approach 1:
Instead of acquiring more real seismic data through time-consuming field surveys, the patent uses GANs to generate synthetic copies of seismic data that preserve the essential geological and statistical characteristics. This copying approach maintains model training accuracy while eliminating the time and cost associated with additional real data acquisition
Solution Approach 2:
The patent performs preliminary generation of synthetic training data before model development, creating a充足的 training dataset in advance. This preliminary action eliminates the need for time-consuming real data collection during model development and deployment phases, significantly reducing overall project timelines
Data Source
AI summary
Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving an input dataset that represents partial spatial information of an area of interest; providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest; and using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, where each output dataset comprises simulated contextual spatial information for the area of interest.