In this paper, a real-time low-cost geophone-based Elephant Footstep Vibration Detection and Identification (EFVDI) system is proposed. The system design started with a real-time low-cost generalized Footstep Vibration Recording and Analyzing (FVRA) system. A series of field experiments to record elephant footstep vibration (target) signals and other possible interfering ground vibration (noise) sources are conducted using the FVRA system. System’s actual field performance was evaluated in terms of maximum detection range, signal amplitude, detection ratio, signal frequency, signal time span, etc. Variations of system’s performance with several input parameters are also investigated. The recorded signals from target as well as noise sources are analyzed to extract different Signal Parameters (SPs). All SPs are saved in a Ground Vibration Signal Pattern Library (GVSPL) which is then used to frame accurate indigenous Elephant Identification Algorithm (EIA). The EIA is embedded in FVRA system to reshape it as specific Elephant Footstep Vibration Detection and Identification (EFVDI) system. The EFVDI system has successfully segregated elephant footsteps from other noise vibrations with high accuracy under simulated field experiment. The results from the proposed system will provide important data to the ongoing research of developing the much needed highly accurate Elephant Early Warning System (EEWS) in future.

中文翻译：

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通过地面振动算法感知、记录和分析大象地震波的实时系统设计

在本文中，提出了一种实时低成本的基于检波器的大象足迹振动检测和识别（EFVDI）系统。系统设计从实时低成本通用足迹振动记录和分析 (FVRA) 系统开始。使用 FVRA 系统进行了一系列现场实验，以记录大象脚步振动（目标）信号和其他可能的干扰地面振动（噪声）源。从最大检测范围、信号幅度、检测比、信号频率、信号时间跨度等方面对系统的实际现场性能进行了评估。还研究了系统性能随几个输入参数的变化。分析来自目标和噪声源的记录信号以提取不同的信号参数 (SP)。所有 SP 都保存在地面振动信号模式库 (GVSPL) 中，然后用于构建准确的本地大象识别算法 (EIA)。EIA 嵌入在 FVRA 系统中，将其重塑为特定的大象脚步振动检测和识别 (EFVDI) 系统。EFVDI 系统在模拟现场实验中成功地将大象的脚步声与其他噪声振动隔离开来，并且精度很高。拟议系统的结果将为正在进行的研究提供重要数据，以开发未来急需的高度准确的大象早期预警系统（EEWS）。EIA 嵌入在 FVRA 系统中，将其重塑为特定的大象脚步振动检测和识别 (EFVDI) 系统。EFVDI 系统在模拟现场实验中成功地将大象的脚步声与其他噪声振动隔离开来，并且精度很高。拟议系统的结果将为正在进行的研究提供重要数据，以开发未来急需的高度准确的大象早期预警系统（EEWS）。EIA 嵌入在 FVRA 系统中，将其重塑为特定的大象脚步振动检测和识别 (EFVDI) 系统。EFVDI 系统在模拟现场实验中成功地将大象的脚步声与其他噪声振动隔离开来，并且精度很高。拟议系统的结果将为正在进行的研究提供重要数据，以开发未来急需的高度准确的大象早期预警系统（EEWS）。