# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 2 Background and Related Work
In this work, the authors emphasize that the elimination of random noise from seismic data significantly affects the accuracy of subsequent data processing. They achieve an enhancement in the signal-to-noise ratio of seismic data through the application of a convolutional neural network trained on noise. This not only results in a higher signal-to-noise ratio but also preserves more valuable information. Given the previously discussed importance of earthquake identification, and thus, early warning systems, the prohibitive cost of high-end ground motion sensors often leaves earthquake-prone areas unable to implement such systems for measuring seismic waves. Low-cost MEMS-based ground motion sensors present a promising solution for creating affordable, yet reliable and study, seismometers. Traditional high-end monitoring systems are highly dedicated measuring systems with high to very high precision of measurement, usually significantly above the monitoring scenario's requirements. The low-cost approach in such monitoring systems attempts to minimize the cost (usually at the level of one to, hopefully, two orders of magnitude) while preserving the precision of measurements within acceptable [21]. The lower cost allows for a higher number of deployed systems and a lower cost per system unit replacement, leading to a denser network of interconnected systems compared to high-end solutions, offering redundancy, expansive spatial measurements, and--utilizing AI methods--the capacity for collective extraction of information. This collective approach yields insights unattainable by unique systems, achieving significantly higher levels of precision compared to unique low-cost systems and rivaling those of non-low-cost systems. The advancements in utilizing low-cost sensors for detecting earthquakes and issuing warnings have shown remarkable progress in recent years. This progress is evident in the expansion of station coverage, the enhancement of data quality, and the broadening scope of applications related to earthquake detection [22]. Real-time seismic signal waves are available to be plotted using ShakeMaps, helping to assess the damage patterns and directivity of rupture. These ShakeMaps plots have proven [23] helpful in establishing the peak ground velocity indicator of damage, and the peak ground acceleration. Similar to our proposed work for earthquake identification, some research efforts have also been made for the event detection of earthquakes with machine learning algorithms, applying time wave series data analogous to those used for different vehicle types. The implementation of different machine learning algorithms determines the class of automobiles [24] for distinguishing between earthquake and non-earthquake, vandalism vibrations [25], even for event detection, phase identification, and the onset picking time [26]. In all such cases, the results indicate that the use of deep neural networks was superior in distinguishing and provided high classification accuracy during training, as well as in the event and phase detection of earthquakes.