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Towards Long Range Detection of Elephants Using Seismic Signals; A Geophone-Sensor Interface for Embedded Systems

Jaliya L. Wijayaraja, Janaka L. Wijekoon, Malitha Wijesundara, L. J. Mendis Wickramasinghe

TL;DR

This work addresses the human-elephant conflict by enabling non-invasive long-range detection of elephants through seismic signals. It introduces a geophone-sensor interface built around an instrumentation amplifier, a Butterworth low-pass filter, and a signal amplifier, all feeding an embedded ATmega328p system. Demonstrated results show detection of elephant footfalls at up to 155.6 m with 99.5% accuracy, and robust performance across 1 Hz to 1 kHz and 10–40°C in field tests with Sri Lankan elephants. The approach offers a cost-effective, deployable solution for early warning and monitoring in HEC-prone regions, with future work focusing on real-time ML-based detection and autonomous gain adaptation.

Abstract

The long-distance detection of the presence of elephants is pivotal to addressing the human-elephant conflict. IoT-based solutions utilizing seismic signals originating from the movement of elephants are a novel approach to solving this problem. This study introduces an instrumentation system comprising a specially designed geophone-sensor interface for non-invasive, long-range elephant detection using seismic waves while minimizing the vulnerability of seismic signals to noise. The geophone-sensor interface involves a cascade array of an instrumentation amplifier, a second-order Butterworth filter for signal filtering, and a signal amplifier. The introduced geophone-sensor interface was tested under laboratory conditions, and then real-world experiments were carried out for tamed, partly tamed, and untamed elephants. The experimental results reveal that the system remains stable within the tested frequency range from 1 Hz to 1 kHz and the temperature range of 10C to 40C. The system successfully captured the seismic signals generated by the footfalls of elephants within a maximum detection range of 155.6 m, with an overall detection accuracy of 99.5%.

Towards Long Range Detection of Elephants Using Seismic Signals; A Geophone-Sensor Interface for Embedded Systems

TL;DR

This work addresses the human-elephant conflict by enabling non-invasive long-range detection of elephants through seismic signals. It introduces a geophone-sensor interface built around an instrumentation amplifier, a Butterworth low-pass filter, and a signal amplifier, all feeding an embedded ATmega328p system. Demonstrated results show detection of elephant footfalls at up to 155.6 m with 99.5% accuracy, and robust performance across 1 Hz to 1 kHz and 10–40°C in field tests with Sri Lankan elephants. The approach offers a cost-effective, deployable solution for early warning and monitoring in HEC-prone regions, with future work focusing on real-time ML-based detection and autonomous gain adaptation.

Abstract

The long-distance detection of the presence of elephants is pivotal to addressing the human-elephant conflict. IoT-based solutions utilizing seismic signals originating from the movement of elephants are a novel approach to solving this problem. This study introduces an instrumentation system comprising a specially designed geophone-sensor interface for non-invasive, long-range elephant detection using seismic waves while minimizing the vulnerability of seismic signals to noise. The geophone-sensor interface involves a cascade array of an instrumentation amplifier, a second-order Butterworth filter for signal filtering, and a signal amplifier. The introduced geophone-sensor interface was tested under laboratory conditions, and then real-world experiments were carried out for tamed, partly tamed, and untamed elephants. The experimental results reveal that the system remains stable within the tested frequency range from 1 Hz to 1 kHz and the temperature range of 10C to 40C. The system successfully captured the seismic signals generated by the footfalls of elephants within a maximum detection range of 155.6 m, with an overall detection accuracy of 99.5%.
Paper Structure (18 sections, 7 equations, 27 figures, 8 tables)

This paper contains 18 sections, 7 equations, 27 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Background Survey
  3. Methodology
  4. Geophone selection and embedded system implementation
  5. Geophone-Sensor Interface Design
  6. Instrumentation amplifier
  7. Noise Filter
  8. Signal Amplifier
  9. Prototype Implementation
  10. Simulations, Field Experiments, Results and Discussion
  11. Software Simulation
  12. Field Experiments
  13. Experiment A
  14. Experiment B
  15. Experiment C
  16. ...and 3 more sections

Figures (27)

  • Figure 1: Overall architecture of the geophone-sensor interface system
  • Figure 2: Propagation of Rayleigh waves aicher1990vibrational
  • Figure 3: RGI-HS10 Geophone
  • Figure 4: Geophone-sensor interface circuit
  • Figure 5: Frequency response of geophone (RGI-HS10) RacotechGeophysicalInstrument
  • ...and 22 more figures