Deep Learning Seismic Inversion With Physics-Guided Refinement
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Solution Overview
Problem
Existing seismic inversion workflows are complicated, require significant computational resources, and heavily rely on human supervision, often resulting in lower resolution and inaccurate synthetic seismic data.
Innovation Solution
A deep learning workflow utilizing multi-task convolutional neural networks (CNNs) for large-scale structural model construction, followed by physics-guided refinement, to automate geophysical property estimation with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic inversion workflows are used, then computational resources and human supervision are required, but the resolution and accuracy of synthetic seismic data are reduced
Solution Approach 1:
The patent replaces traditional mechanical/computational seismic inversion systems with a neural network-based system. The neural network is trained on seismic data and well logs to directly predict geophysical properties, eliminating the need for complex iterative inversion algorithms and reducing computational resource requirements while improving accuracy and resolution.
2Measurement precision
If traditional seismic inversion workflows are used, then significant computational resources are required, but the resolution of synthetic seismic data is reduced
Solution Approach 1:
The patent applies preliminary action by training the neural network in advance on large datasets of seismic data and well logs. This pre-training phase captures the complex relationships between seismic characteristics and geophysical properties, allowing the network to make accurate predictions during actual inversion operations without requiring significant computational resources at that stage.
3Extent of automation
If traditional seismic inversion workflows are used, then human supervision is required, but the accuracy of geophysical property estimation is reduced
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically perform seismic inversion without human intervention. The network learns from training data and independently predicts geophysical properties, eliminating the need for human supervision while maintaining or improving accuracy through its ability to process and interpret complex seismic patterns.
4Extent of automation
If neural networks are used for geophysical property estimation, then automation is improved, but training data requirements increase
Solution Approach 1:
The patent merges multiple data sources including seismic data, well logs, and synthetic seismic data into a unified training dataset. By combining these different types of data, the neural network can learn from diverse patterns and relationships, improving its predictive capability while efficiently utilizing the available training data.
Data Source
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AI summary
A method for converting seismic data into geophysical property models. The method includes receiving seismic data from a site, wherein the seismic data includes seismic stacks and well logs received from the site. A large-scale structural model (LSSM) is then constructed from the received seismic data. The method includes estimating initial geophysical properties related to the site using a first neural network trained by the seismic data and the constructed LSSM. Data may then be extracted from the received data and used to train a second neural network. The method also includes revising the initial geophysical properties using a second trained neural network. The revised geophysical properties, which may include p-velocity, density, or s-velocity, may be displayed on a screen. The method also includes performing a site action based on the revised geophysical properties.