# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 2 Background and Related Work  
In seismology, the foundation of knowledge lies in data analysis, with significant breakthroughs often stemming from the examination of fresh datasets or the creation of novel data analysis techniques [12]. Seismology focuses on the study of earthquakes and associated phenomena, primarily applying the principles of continuous medium mechanics, specifically the theory of elasticity. In contrast, seismic engineering is an applied science concerned with understanding how earthquake-induced motion impacts man-made structures, including buildings and other constructions [13]. Earthquake impacts on both natural and human-made structures are primarily driven by the transfer of energy through seismic waves originating from the earthquake's source. Seismic waves propagate through the Earth and are detected at distant locations using sensitive seismographs. Interpreting seismic records requires an understanding of how seismic waves are generated and propagated, and how recording processes affect them. Advances in seismic instrumentation now allow for accurate digital representation of particle motion across a wide frequency range. However, this necessitates careful consideration of seismic noise, the background irregular ground motion caused by various factors, including human activities and natural phenomena. Occasionally, this background noise is interrupted by organized energy patterns generated by seismic waves from natural or artificial sources. These wave-trains, characterized by distinct arrivals associated with specific propagation paths, become more pronounced with increasing distance from the source. Following the initial body-wave phases, like P (compressional waves) and S (shear waves), there is an increase in record amplitude as surface-guided waves arrive [14]. Over the centuries, seismology has evolved significantly. From [PERSON]'s ancient seismograph in 132 AD to the late 1800s when scientific research on seismology began, progress was slow. It was not until around 1900 that precise measuring instruments, like geophones and seismometers, emerged. These early devices were large, costly, and had limited sensitivity. However, recent advances in micro-electro-mechanical system (MEMS) technology have drastically reduced size and cost while improving sensitivity, making MEMS-based seismic sensors highly promising for their ability to provide reliable measurements across a wide bandwidth [15]. Modern seismographs produce digitized information at varying regular time intervals sent to be analyzed on computers. Many concepts of time series analysis, including filtering and spectral methods, are valuable in seismic analysis [16]. The identification of noise (seismic included) depends on a plethora of parameters and usually requires data analysis while depending on the domain or application, a part of the information may be be treated as noise or useful signal [17]. Seismic noise monitoring systems have been proposed [18] that address continuous traffic noise utilizing raw noise records as well as shear-wave velocity profiles. Prior to seismic wave measurement and identification, noise removal is another important factor that has been addressed [19]. Periodic noise poses a well-documented challenge in the context of seismic wave removal, often originating from sources such as power lines, pump jacks, engine operations, or other forms of interference. It introduces contamination to seismic data and has a notable impact on subsequent data processing and interpretation. The proposed denoising approach hinges on the sparse representation of periodic noise, enabling its estimation without being influenced by seismic reflections. Consequently, this method effectively reduces periodic noise without compromising the integrity of seismic events. Similarly, the utilization of machine learning algorithms for eliminating random noise in seismic data has emerged as a crucial aspect of seismic analysis [20].