Domain-Specific Embeddings for Geoscience Semantic Search Accuracy
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Solution Overview
Problem
There is a gap between general-purpose AI models and Large Language Models (LLMs) and specialized applications in specific domains like geoscience and energy, necessitating domain adaptations to enhance their effectiveness.
Innovation Solution
A method for converting raw geoscience or energy domain data into a multi-dimensional vector space using embeddings computing models, involving text and image encoders, training, and configuring the model to generate embeddings for semantic search and classification, with loss functions for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general-purpose AI models and LLMs are used for geoscience and energy domain applications, then broad applicability and ease of operation are achieved, but domain-specific accuracy and measurement precision deteriorate
Solution Approach 1:
The patent applies local quality by creating domain-specific embeddings models tailored to geoscience and energy domains while maintaining the general-purpose LLM framework. The embeddings model is trained on domain-specific data (well logs, seismic data, production data) to capture local characteristics and terminology, while the overall system remains broadly applicable across different energy subdomains.
Solution Approach 2:
The patent introduces an embeddings computing model as an intermediary between the general-purpose LLM and domain-specific data. This embeddings model acts as a mediator that translates domain-specific unstructured data into structured representations that the LLM can process, bridging the gap between general AI capabilities and domain-specific requirements.
2Measurement precision
If domain-specific embeddings models are created through training and configuration, then domain-specific accuracy and measurement precision are improved, but device complexity and processing time increase
Solution Approach 1:
The patent segments the AI system into distinct components: an embeddings computing model for domain-specific data representation and a general-purpose LLM for high-level processing. This segmentation allows each component to be optimized independently - the embeddings model handles domain-specific complexity while the LLM maintains general applicability, reducing overall system complexity.
Solution Approach 2:
The patent applies preliminary action by pre-training the embeddings model on domain-specific data before the LLM processes queries. The embeddings model is configured and trained in advance to capture domain characteristics, so that during actual operation, the LLM receives pre-processed, domain-adapted inputs without needing to learn domain-specific patterns from scratch.
3Measurement precision
If multiple encoders and training processes are implemented for data integration, then data processing capability and predictive modeling accuracy are improved, but loss of time and processing duration increase
Solution Approach 1:
The patent merges multiple data types (textual data from well logs, seismic data, production data) and multiple processing functions (encoding, embedding, classification) into a unified embeddings computing model. This consolidation allows the model to process diverse data types simultaneously through a single trained architecture, reducing the time overhead of multiple separate processing steps while maintaining comprehensive data integration.
Data Source
AI summary
Disclosed are methods and systems for: receiving raw geoscience or energy domain data associated with a resource site, such that the raw geoscience or energy domain data comprises a plurality of textual data and image data having a plurality of disparate file/document formats; receiving a user input associated with the raw geoscience or energy domain data; applying the raw geoscience and energy domain data and the user input to a configured embeddings model thereby generating one or more of: a text embedding associated with the raw geoscience or energy domain data, and an image embedding associated with the raw geoscience or energy domain data; implementing, based on the applying, one or more of: a semantic search computing operation and a classification or clustering computing operation associated with a multidimensional vector space; and generating a report, based at least on the semantic search/classification/clustering computing operation.