AI Microseism Node Edge Processing Reduces Data Transmission
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Solution Overview
Problem
Conventional microseism monitoring systems face high data transmission pressure and computational load in data centers due to massive data analysis, and inefficiency caused by excessive manual intervention, making real-time processing impossible.
Innovation Solution
An AI real-time microseism monitoring node equipped with a processor, data acquisition device, and AI calculation device, which processes microseism data to determine valid event data and reduces data transmission by sending only calculated valid event data to the remote data center, using a pre-trained microseism data analysis device based on a convolutional neural network and recurrent neural network architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all microseism data are transmitted to the data center for centralized analysis, then comprehensive data processing can be achieved, but data transmission pressure and computational load on the data center increase significantly
Solution Approach 1:
The patent segments the centralized data processing function into distributed edge computing nodes. Each monitoring node runs local AI models to process seismic data independently, dividing the overall processing task across multiple distributed units rather than concentrating all computation at the data center. This segmentation reduces the volume of data that must be transmitted while maintaining comprehensive analysis capabilities at the edge.
Solution Approach 2:
The patent introduces AI calculation devices as intermediary components between the data acquisition devices and the data center. These intermediaries perform preliminary data processing and filtering locally, transforming raw seismic data into processed results before transmission. This intermediary layer reduces the burden on both the transmission network and the data center while preserving analytical completeness.
2Measurement precision
If centralized data processing is used, then all data can be analyzed, but the data center experiences excessive computational load making real-time processing impossible
Solution Approach 1:
The patent implements preliminary data processing and filtering at the edge computing nodes before data is sent to the data center. AI models perform initial analysis, event detection, and data preprocessing locally, so that only processed results and critical information need to be transmitted for further centralized analysis. This preliminary action at the edge enables real-time processing capabilities while maintaining analytical completeness.
3Device complexity
If manual data collection and analysis methods are used, then system complexity is low, but processing efficiency is reduced due to excessive manual intervention
Solution Approach 1:
The patent enables the monitoring nodes to perform self-service through embedded AI calculation devices that automatically detect seismic events, filter data, and perform preliminary analysis without requiring manual intervention. The system serves itself by making autonomous processing decisions at the edge, reducing the need for human operators while significantly improving processing efficiency. This self-service capability maintains relative system simplicity while eliminating manual bottlenecks.
Data Source
AI summary
The application discloses an AI real-time microseism monitoring node, which includes a processor and a data acquisition device, an AI calculation device, and a communication device connected to the processor, wherein the AI calculation device is provided with pre-trained microseism data analysis Device, and the processor is configured to perform the following operations: controlling the data acquisition equipment to acquire microseism data; turning on the AI calculation device to calculate the acquired microseism data by means of the microseism data analysis device to determine the valid event data associated with the microseism; and sending the valid event data to the remote data center through the communication device.