# Monitoring Zoo Elephant Rumble Activity Using Combined Seismic and Acoustic Data

## TL;DR

This study uses seismic and acoustic sensors to monitor elephant rumbles in a zoo, revealing communication patterns and the benefits of combining data types.

## Contribution

The study introduces a combined seismic and infrasound monitoring approach for zoo elephants, enabling automated classification with high accuracy.

## Key findings

- Over 1350 rumbles were recorded, showing temporal variability and nocturnal increases in vocal activity.
- Seismic data detected motion-related signals not captured by infrasound sensors, emphasizing the value of combined monitoring.
- CNN models trained on seismic data achieved 98% accuracy in classifying rumbles and noise.

## Abstract

The communication of African and Asian elephants based on seismic and acoustic waves has been studied for decades. However, research within anthropogenic zoo environments, particularly with respect to seismic signals, remains limited compared to studies in natural habitats. This study analyzes low‐frequency elephant rumbles recorded at the Opel‐Zoo near Frankfurt am Main, Germany, by comparing characteristics from datasets obtained using non‐invasive, co‐located seismic and infrasound sensors. Analysis of recordings from August 2024 revealed over 1350 rumbles, indicating significant temporal variability. These rumbles are characterized by signal durations of 1–8 s and fundamental frequencies between 10 and 25 Hz, with harmonics above. Due to high seismic background noise during zoo opening hours, infrasound detections are more abundant during the day, while seismic and infrasound detection rates are comparable at night. The systematic nocturnal housing schedule of the elephants leads to an increase in rumbling activity approximately every second night, with one pair showing substantially higher vocal communication than the other. Many rumbles occur in rapid sequences within minutes, suggesting elephant interaction or external triggers. Most rumbles are accompanied by motion‐induced signals associated with locomotion or trampling, phenomena not detectable with infrasound sensors measuring acoustic waves only. This highlights the value of combined seismic and infrasound data. To enable a robust automated classification of rumbles and noise for continuous monitoring, we train CNNs using spectrogram images of the hand‐picked seismic and infrasound rumbles as inputs. The models achieve up to 98% classification accuracy, while cross‐domain applications demonstrate better generalization and robustness of the CNN trained with seismic data. The seismo‐acoustic monitoring approach and resulting findings have the potential to enhance our understanding of zoo elephant behavior, social interactions, and welfare.

At the Opel‐Zoo in Germany, we studied elephant rumble activity by recording low‐frequency signals with seismic and infrasound sensors. We detected over 1350 rumbles within 4 weeks that show temporal patterns, demonstrating the benefits of combining both sensing methods. This study provides insights into a promising tool for monitoring elephant activity, behavior, and potential communication in zoo environments.