Subsurface Structure Identification With CNN Seismic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interpretation of large seismic volumes is a time-consuming and expensive task, especially with increasing data collection and improved acquisition techniques, necessitating automated seismic interpretation systems.
Innovation Solution
A seismic interpretation system using machine learning techniques, specifically convolutional neural networks, to automate geo-body segmentation by training on labeled seismic data and accelerating the training process through parallel processing and hyperparameter variation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual seismic interpretation methods are used, then interpretation accuracy can be maintained, but the time required for geo-body segmentation increases significantly
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with an automated computer-based system that uses machine learning algorithms (specifically convolutional neural networks) to perform seismic data analysis and geo-body segmentation, thereby eliminating the time-consuming manual review process while maintaining interpretation quality
Solution Approach 2:
The system enables self-service interpretation by training the neural network on labeled seismic data, allowing the computer to automatically learn and perform segmentation tasks without requiring continuous human intervention or manual review of each interpretation result
2Measurement precision
If more comprehensive seismic data is collected, then subsurface analysis quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training the neural network on large volumes of labeled seismic data before actual interpretation tasks, enabling the model to quickly process new comprehensive seismic datasets without requiring proportional increases in processing time during actual operations
Solution Approach 2:
The patent changes the processing parameters by transitioning from traditional sequential processing methods to parallel neural network processing, fundamentally altering how computational resources are utilized to handle large seismic datasets more efficiently
3Productivity
If automated interpretation systems are implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional neural network system that can handle various seismic interpretation tasks (segmentation, feature extraction, analysis) through a single trained model, reducing the need for multiple specialized systems and thereby managing complexity
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A subsurface structure identification system includes one or more processors and a memory coupled to the one or more processors. The memory is encoded with instructions that when executed by the one or more processors cause the one or more processors to provide a convolutional neural network trained to identify a subsurface structure in an input migrated seismic volume, and to partition the input migrated seismic volume into multi-dimensional sub-volumes of seismic data. The instructions also cause the one or more processors to process each of the multi-dimensional sub-volumes of seismic data in the convolutional neural network, and identify the subsurface structure in the input migrated seismic volume based on a probability map of the input migrated seismic volume generated by the convolutional neural network.