WEI Jia, HAO Xiaozhu, FENG Jing, YANG Rui, XING Lei, LIU Huaishan, WANG Wei, WU Zhiqiang, ZHANG Dong, LI Linwei. Spatial Characteristics of the Plasma Spark Source WaveletJ. Journal of Ocean University of China, 2025, 24(1): 103-112. DOI: 10.1007/s11802-025-5839-6
Citation: WEI Jia, HAO Xiaozhu, FENG Jing, YANG Rui, XING Lei, LIU Huaishan, WANG Wei, WU Zhiqiang, ZHANG Dong, LI Linwei. Spatial Characteristics of the Plasma Spark Source WaveletJ. Journal of Ocean University of China, 2025, 24(1): 103-112. DOI: 10.1007/s11802-025-5839-6

Spatial Characteristics of the Plasma Spark Source Wavelet

  • Plasma spark sources are widely used in high-resolution seismic exploration. However, research on the excitation mechanism and propagation characteristics of plasma spark sources is very limited. In this study, we elaborated on the excitation process of corona discharge plasma spark source based on indoor experimental data. The electrode spacing has a direct impact on the movement of bubbles. As the spacing between bubbles decreases, they collapsed and fused, thereby suppressing the secondary pulse process. Based on the premise of linear arrangement and equal energy synchronous excitation, the motion equation of multiple bubbles under these conditions was derived, and a calculation method for the near-field wavelet model of plasma spark source was established. We simulated the source signals received in different directions and constructed a spatial wavelet face spectrum. Compared with traditional far-field wavelets, the spatial wavelet facial feature representation method provides a more comprehensive display of the variation characteristics and propagation properties of source wavelets in three-dimensional space. The spatial wavelet variation process of the plasma spark source was analyzed, and the source depth and the virtual reflection path are the main factors affecting the wavelet. The high-frequency properties of plasma electric spark source wavelets lead to their sensitivity to factors such as wave fluctuations, position changes, and environmental noise. Minor changes in collection parameters may result in significant changes in the recorded waveform and final data resolution. So, the facial feature method provides more effective technical support for wavelet evaluation.
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