Seismic Sensor Channel Reduction via Signal Aggregation
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Solution Overview
Problem
Seismic survey systems with a large number of seismic sensors require a correspondingly large number of data channels, leading to increased complexity and reduced reliability, as well as higher costs.
Innovation Solution
The seismic sensors are partitioned into groups, with aggregation units applying different time delays or encodings to their signals, which are then aggregated and transmitted over a reduced number of channels to a central processing system, allowing for signal separation processing to extract individual seismic signals from the aggregated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of seismic sensors are deployed to improve measurement precision, then data quality is improved, but the number of required data channels increases leading to increased device complexity
Solution Approach 1:
The patent divides the large number of seismic sensors into multiple groups, with each group processed by a separate aggregation unit. This segmentation allows the system to handle many sensors while using fewer aggregation units and channels, reducing overall system complexity while maintaining the ability to process data from all sensors.
Solution Approach 2:
The patent combines multiple sensor signals from the same group into a single aggregated signal through aggregation units. By merging signals from multiple sensors into fewer channels using techniques like random delays and encoding, the system reduces the number of required data channels while preserving the information from all original sensors.
2Measurement precision
If the number of data channels is increased to handle more seismic sensors, then measurement precision is improved, but the cost of the acquisition system increases
Solution Approach 1:
The patent merges multiple sensor signals into fewer aggregated signals using aggregation units that apply random delays and encoding. This merging approach allows the system to process data from a large number of sensors using a smaller number of expensive data channels, thereby reducing the overall system cost while maintaining measurement precision.
Solution Approach 2:
The patent uses encoding techniques that create transformed copies of the original sensor signals through random delays and modulations. These encoded copies can be transmitted over fewer channels and later decoded to recover the original signal information, reducing the need for expensive direct transmission channels for each sensor.
3Measurement precision
If the number of data channels is increased to accommodate more seismic sensors, then measurement precision is improved, but the reliability of the acquisition system is reduced
Solution Approach 1:
The patent combines signals from multiple sensors into fewer aggregated channels, reducing the total number of channels that need to be managed and monitored. This reduction in channel count simplifies the system architecture and reduces potential failure points, thereby improving reliability while still capturing data from all sensors through the aggregation process.
Data Source
AI summary
To reduce a number of required channels for a survey system having seismic sensors, the seismic sensors are partitioned into groups of corresponding seismic sensors. An aggregation unit applies different transformations of signals of the seismic sensors within a particular one of the groups. The differently transformed signals within the particular group are aggregated to form an aggregated signal. The aggregated signal for the particular group is transmitted, over a channel of the survey system, to a processing system.