## 1 Introduction  
Elephants use different vocalization patterns to communicate between herds and each other for food, water, mating, and warning. Among these patterns, the infrasonic rumbles have a very specific significance in long-range communication [1]. Elephant rumbles have acoustic and seismic components. The acoustic component propagates through the air as a three-dimensional wave and is attenuated through foliage fairly quickly. The seismic component, on the other hand, propagates through the ground as a two-dimensional Rayleigh wave and therefore travels a longer distance compared to the acoustic component. The elephant's foot is believed to have the capability of a seismic transponder to facilitate long-range seismic infrasonic communication. These seismic signals can be a powerful component of an elephant's communication system, serving crucial functions such as mate finding, prey detection, and interspecific and intraspecific warnings [2]. The attenuation of seismic waves during transmission increases monotonically with frequency, hence the range of 10 Hz to 40 Hz is considered the \"sweet zone\" for seismic signal propagation. Elephant rumbles, which have a fundamental frequency of around 20 Hz [3], fall within this sweet zone, allowing them to communicate through the ground. The elephant rumbles that propagate as Rayleigh waves through the ground can be captured using geophones [4]. Detecting and localizing wild elephants through airborne rumbles encounter certain challenges that impede its effectiveness. Airborne signals, though easy to detect, attenuate rapidly [5], limiting the range over which elephants can be identified. The amplitude of a Rayleigh wave during ground surface transmission is inversely proportional to the square root of the distance, with a loss of 3 dB for every doubling of the distance. In airborne signals, amplitude is inversely proportional to the distance, with a loss of 6 dB for every doubling of the distance. Factors such as weather, time of day, scattering, and reflections due to objects and trees also attenuate the airborne signal [2]. The seismic wave undergoes a lower attenuation compared to the airborne wave, hence the detectable range of the seismic component of a rumble is greater than that of the airborne acoustic component [6]. In [7], a method is proposed to detect elephant rumbles using the shape of the formant frequency tracks of a rumble. However, the assumption in this research that the first two formant frequencies remain stationary is controversial [8]. In [9], a template matching method is used for rumble identification in which a reference feature template is used to clarify whether or not a test sample contains a rumble. This method delivers subpar performance with a 78.6% detection rate with 78.2% false positives as reported in [10]. In [10], seismic elephant rumbles are identified using support vector machines (SVMs) and signal enhancement, which achieves an 88.2% detection rate with 13.7% false positives. In [11], elephant localization using an array of infrasonic seismic rumble detectors is demonstrated. Among the limited research conducted in detecting seismic elephant rumbles, signal acquisition and signal processing have not yet been fully investigated. There is a clear research gap and a need to develop the right signal acquisition and processing methods to improve seismic elephant rumble detection. This research intends to explore this gap and propose the most effective signal acquisition and signal processing methods for infrasonic seismic elephant rumble detection. After experimentally evaluating various feature extraction techniques, we observe that three machine learning (ML) algorithms, ridge classifier, decision tree classifier, and Light Gradient Boosting Machine (LCBM) classifier, provide the best results when Mel-frequency cepstral coefficients (MFCCs), Hjorth parameters, and spectral energy distributions, respectively, are used as features in identifying infrasonic seismic elephant rumbles.