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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Compressive Data Gathering With Generative Adversarial Networks for Wireless Geophone Networks
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Compressive Data Gathering With Generative Adversarial Networks for Wireless Geophone Networks

机译：用于无线地震孔网络的生成对抗网络收集的压缩数据

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摘要

In modern seismic data acquisition, real-time data collection is a challenging task due to bandwidth limitations in wireless communications. In this letter, we propose a novel compressive data gathering scheme using generative adversarial networks, named GAN-CDG, to improve the efficiency of data gathering. Instead of collecting the originally acquired data, GAN-CDG gathers data projections in wireless geophone networks. Data compression and load-balanced relay transmission are utilized during the projection process. To speed up the formation of projections, the shortest path routing tree (SPRT) is constructed, which achieves the minimum end-to-end time delay. The sparse domain of seismic signals and its reconstruction mapping are learned by sparsity-constrained adversarial networks. The testing results demonstrate that projections with high compression ratios (e.g., 16) are gathered efficiently with the SPRT. Then, original seismic signals can be reconstructed accurately (over 30 dB) from the projections using the adversarial model, which outperforms the state-of-the-art method.
机译：在现代地震数据采集中，由于无线通信中的带宽限制，实时数据收集是一个具有挑战性的任务。在这封信中，我们提出了一种使用名为GaN-CDG的生成对冲网络的新型压缩数据收集方案，以提高数据收集的效率。 GaN-CDG在无线地理管网络中收集数据投影而不是收集最初获取的数据。在投影过程中使用数据压缩和负载平衡继电器传输。为了加速投影的形成，构造了最短路径路由树（SPRT），这实现了最小的端到端时间延迟。通过稀疏受限的对抗网络学习地震信号的稀疏领域及其重建映射。测试结果表明，具有高压缩比（例如，16）的突起有效地用SPRT收集。然后，可以使用对普发的模型从突起精确地（超过30dB）重建原始地震信号，这优于最先进的方法。

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