研究目的
To envision and describe the framework and architecture of a Subsurface Camera (SAMERA) for real-time in situ 3D subsurface imaging using geophysical sensor networks.
研究成果
SAMERA is a transformative technology for subsurface imaging, with prototypes demonstrated for seismic methods. Future work should focus on automation, faster distributed computing, and integrating multiple geophysical methods for improved imaging.
研究不足
The paper mentions challenges such as the need for full automation, fast completion under bandwidth constraints, data fusion for joint inversion, and handling network disruptions in harsh environments. It also notes that some geophysical imaging methods (e.g., Full Waveform Inversion) may require significant computational resources that are not yet fully feasible in sensor networks.
1：Experimental Design and Method Selection:
The paper describes the design of SAMERA, a geophysical sensor network for subsurface imaging, using methods such as seismic imaging (e.g., travel-time tomography, migration-based imaging, ambient-noise imaging). It involves sensing, processing, computing, and control layers.
2：Sample Selection and Data Sources:
Data are collected from geophysical sensors (e.g., seismometers) deployed in networks, with examples from field deployments like the University of Georgia campus.
3：List of Experimental Equipment and Materials:
Includes geophones, GPS, computing boards (e.g., Raspberry Pi), wireless radios, solar panels, batteries, and specific hardware like the R1+ waterproof box.
4：Experimental Procedures and Operational Workflow:
Sensors are deployed to form networks; data are sensed, processed (e.g., filtering, cross-correlation), computed in a distributed manner (e.g., using iterative algorithms), and controlled via software for image visualization and parameter adjustment.
5：Data Analysis Methods:
Involves signal processing techniques (e.g., bandpass filtering, cross-correlation), distributed computing algorithms (e.g., decentralized optimization), and geophysical inversion methods (e.g., tomography, migration).
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geophone
Senses seismic waves for subsurface imaging.
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GPS
Provides precise timestamp and location information for sensor nodes.
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Raspberry Pi
3 Model B
Raspberry Pi
Computing board for data processing, storage, and communication in sensor nodes.
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wireless radio
Enables communication and data exchange between sensor nodes in the network.
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solar panel
Provides renewable energy for powering sensor nodes.
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battery
Powers sensor nodes, can be connected to solar panel.
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R1+
Waterproof box for protecting hardware components in harsh environments.
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Analog-to-Digital Converters
ADC chip
Digitizes analog signals from sensors.
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