Modern seismic surveys are projected to employ more numerous geophones in denser topologies for high-quality and depth imaging applications. The cost of employing cables in such dense topologies is exacerbated by the need for a larger number of connectors and the associated maintenance costs. More recently, wireless systems have been proposed with the objective of eliminating cable and promoting denser topologies. However, there is a strong need for wireless systems that not only remove the dependency on cable but also offer real-time acquisition in highly dense geophone deployments with a large number of seismic channels. A novel geophone network architecture is proposed based on millimeter-wave multiple-input multiple-output (MIMO) technology using the IEEE 802.11ad and 802.11ay standards. The standards operate over unlicensed spectrum in the 57-64 GHz bands for mm-wave communication. There has been a global interest in promoting outdoor point-to-point links for mm-wave small-cell backhauls, allowing for gigabit rates over long transmission ranges. The proposed research will employ machine learning algorithms to achieve more efficient beamforming training between the gateway nodes and the geophones. The high-directivity and large channel bandwidth offered by these standards also promote more accurate radiolocation, and schemes will be investigated that can achieve decimeter-level accuracy. Furthermore, the above goals will be achieved in an energy-efficient manner so as to enhance the operational life of the geophones. In addition to the above aspects, channel measurements will be conducted, and channel models developed for near-ground propagation in the mm-wave bands. While current literature addresses scenarios employing tall base stations that are several meters high, there is a dearth of analysis for sub-meter ground-to-ground and air-to-ground 57-64 GHz channels. Finally, a mixed radio-frequency (RF)/free-space optical (FSO) wireless geophone network architecture is proposed for seismic surveys, its performance will be investigated, and the effects of atmospheric conditions and pointing error will be studied. Furthermore, energy harvesting (EH) nodes will be deployed to optimize the resources and extend the life span of the network.
|Effective start/end date||1/09/20 → 1/08/22|
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