Distributed Sensor Network for Real-Time Subsurface Imaging
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Solution Overview
Problem
Current seismic exploration technologies lack the capability for real-time high-resolution imaging of subsurface structures due to bandwidth, energy, and computing power limitations in low-power sensor networks, making it difficult to process raw seismic data in real-time for applications like oil field exploration and volcanic monitoring.
Innovation Solution
A distributed multi-resolution evolving tomography system that decentralizes the computation load within sensor networks, using arrival times of seismic events to derive a multi-dimensional velocity model, allowing for real-time sub-surface imaging by processing data locally within the network rather than relying on centralized processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data collection and processing methods are used, then measurement precision can be maintained, but productivity deteriorates due to months-long processing times
Solution Approach 1:
The patent divides the centralized processing system into distributed sensor nodes that each perform local signal conditioning and feature extraction. This segmentation allows parallel processing across multiple nodes, dramatically improving productivity while maintaining measurement precision through distributed computation of the same seismic attributes.
Solution Approach 2:
The patent implements preliminary processing steps directly at the sensor nodes before data transmission, including noise filtering, wavelet deconvolution, and initial velocity analysis. This preliminary action reduces the data volume requiring centralized processing and enables faster overall turnaround while preserving the precision needed for subsurface imaging.
2Productivity
If real-time processing is implemented, then productivity improves, but device complexity worsens due to computational requirements at sensor nodes
Solution Approach 1:
The patent segments the computational workload by node type, with simple nodes performing only data acquisition and basic filtering, while more capable nodes handle advanced signal processing. This heterogeneous segmentation enables real-time processing without requiring every node to be computationally complex.
Solution Approach 2:
The patent introduces intermediate processing layers between raw data acquisition and final imaging, where cluster heads or gateway nodes aggregate and pre-process data from multiple sensor nodes. This intermediary approach reduces the computational burden on individual nodes while maintaining real-time processing capability through coordinated multi-node computation.
3Productivity
If distributed processing is used, then productivity improves through parallel computation, but loss of information worsens due to limited node memory and processing capacity
Solution Approach 1:
The patent performs preliminary data compression and feature extraction at each node before transmission, preserving only the most informative seismic attributes. This preliminary action reduces data volume for distributed storage and processing while minimizing information loss through selective retention of high-value signal characteristics.
Solution Approach 2:
The patent implements feedback mechanisms where processing results from distributed nodes are continuously monitored and used to adjust processing parameters, data sampling rates, and transmission frequencies. This feedback ensures that information fidelity is maintained by dynamically allocating resources to preserve critical seismic information while optimizing parallel processing efficiency.
Data Source
AI summary
Systems and methods of real-time in-situ sub-surface imaging are described herein.