# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 5 Conclusions  
This paper underscores the profound significance of earthquake monitoring for the purposes of disaster management, public safety, and scientific research. Moreover, the pivotal role of sensor networks in amassing seismic data is presented, forming the cornerstone of current earthquake detection capabilities. In that context, the advent of low-cost sensors has unlocked the potential for their extensive deployment in the field, despite their susceptibility to vehicular noise pollution. Low-cost seismic sensors present a formidable challenge, necessitating innovative solutions for accurate signal extraction. Since the importance of precise earthquake identification cannot be overstated, as it holds the key to timely alerts and informed decision-making, herein, we propose the use of an amalgamated deep neural network (DNN) composed of (i) a DNN trained on earthquake signals from professional sensory equipment, as well as (ii) a DNN trained on vehicular signals from low-cost sensors, for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise.  
Our proposal includes a detailed presentation of a low-cost seismic sensory equipment, which is designed to be approximately two orders of magnitude less expensive than typical professional seismic measurement equipment. Still, the low-cost seismic sensory equipment was shown to be prone to vehicular noise contamination. Accordingly, the proposed amalgamated deep neural network underwent evaluation through experimentation and has manifested significant performance improvements compared to a generic stochastic differential model. The superiority of the proposed methodology addresses the need of effectiveness, as it identifies both the beginning and the end of a seismic event, as well as the need of efficiency, as indicated by the performance measures. Future plans will include customizing the generic DNNs deployed in this study for the task at hand, in order to address the necessities of the work's scenario and achieve even higher efficiency in the end identification process. Moreover, we plan to expand the two training datasets (vehicular noise and ground truth from professional seismometers) provided herein to ensure more generality and to better train the DNN, accordingly obtaining more general results. Finally, we plan to extend our low-cost network of sensors in locations near the professional seismometers in order to enhance the two-fold synchronized dataset and to test even more diverse scenarios.  
Figure 12: Results of the amalgamated DNN model’s performance.  
**Author Contributions:** Conceptualization, L.A., A.A., N.A., I.V., I.K. and M.A.; Methodology, L.A., A.A., N.A., I.V., I.K. and M.A.; Software, L.A., A.A., N.A., I.V., I.K. and M.A.; Validation, L.A., A.A., N.A., I.V., I.K. and M.A.; Formal analysis, L.A., A.A., N.A., I.V., I.K. and M.A.; Investigation, L.A., A.A., N.A., I.V., I.K. and M.A.; Resources, L.A., A.A., N.A., I.V., I.K. and M.A.; Data curation, L.A., A.A., N.A., I.V., I.K. and M.A.; Writing--original draft, L.A., A.A., N.A., I.V., I.K. and M.A.; Writing--review & editing, L.A., A.A., N.A., I.V., I.K. and M.A.; Visualization, L.A., A.A., N.A., I.V., I.K. and M.A.; Supervision, L.A., A.A., N.A., I.V., I.K. and M.A.; Project administration, L.A., A.A., N.A., I.V., I.K. and M.A.; Funding acquisition, L.A., A.A., N.A., I.V., I.K. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.