# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 1 Introduction  
Earthquakes are sudden movements along fault lines that release stored elastic energy in rocks, generating seismic waves that propagate throughout the Earth [1]. Seismology is a field abundant with data and is heavily reliant on data analysis. Each day witnesses numerous earthquakes worldwide with magnitudes exceeding 2.5, which can be felt locally. Additionally, every few days, earthquakes capable of causing structural damage occur [2]. Moreover, there is a continuous occurrence of numerous smaller earthquakes, typically with magnitudes below 2.5, which are too weak to be perceptible but are consistently recorded by modern instruments [3]. These minor seismic events offer valuable insights into the mechanisms of earthquakes [4]. Monitoring earthquakes is important for disaster management, public safety, and scientific research. It enables determining preparatory activities such as timely warnings, evacuation plans, and response strategies in order to mitigate the impact of seismic events, in addition to studying earthquake patterns to accrue valuable insights into Earth's dynamics. To this end, sensor networks, comprising mostly of seismometers and accelerometers placed throughout the globe, play a pivotal role in this effort by continuously collecting seismic data. These networks facilitate real-time monitoring, data analysis, and the development of earthquake prediction models, ultimately enhancing the ability to protect lives and infrastructure, and advance the scientific understanding of earthquake behavior. The emergence of low-cost sensors represents a significant advancement in environmental monitoring [5], in general, and earthquake monitoring [6], specifically, as their affordability allows for widespread deployment. These sensors offer a plethora of advantages, including increased spatial coverage and dense monitoring networks that allow fora more comprehensive understanding of environmental conditions. Moreover, their cost-effectiveness allows for easier replacement in the event of malfunction or damage, ensuring continuous data collection without substantial financial burdens. This democratization of sensor technology not only enhances our ability to gather data across vast geographical areas but also empowers communities, researchers, and organizations to address critical environmental and societal challenges with greater precision and efficiency. Vehicular noise contamination poses a significant challenge in urban environments, where the deployment of low-cost sensors is common. This challenge stems from the ubiquitous presence of vehicles on roads and highways, generating a continuous stream of noise that can impact various aspects of urban life [7]. The deployment of low-cost sensors, while advantageous for monitoring purposes, can also exacerbate the problem by providing a platform for capturing and transmitting this noise. As urban areas continue to grow and traffic congestion increases, the issue of vehicular noise becomes more pronounced, affecting the well-being of residents, wildlife habitats, and overall quality of life [8]. The task of effectively using sensor data to monitor and analyze while accounting for noise contamination presents a complex problem that requires innovative solutions and advanced earthquake signal processing and deep learning techniques [9]. Earthquake identification is of paramount importance due to its significant impact on public safety, emergency response efforts, and disaster preparedness. Earthquakes are natural disasters that can cause widespread destruction, loss of life, and disruption to communities. The accurate and timely identification of earthquakes allows a variety of critical actions that can mitigate their effects and save lives [10]. It is thus fundamental to effective disaster management and empowers individuals, communities, and authorities with the information needed to make informed decisions, take swift actions, and allocate resources efficiently.