Design and Evaluate a Low-Cost Seismic Monitoring Network in Urban Areas

Thesis Abstract

Urban areas worldwide are increasingly vulnerable to seismic hazards, yet existing seismic monitoring systems often remain prohibitively expensive and geographically sparse, limiting real-time seismic data collection essential for early warning and disaster mitigation. This study aims to design, implement, and evaluate a low-cost seismic monitoring network specifically tailored for dense urban environments, with an emphasis on affordability, scalability, and operational reliability. The specific objectives are to identify suitable low-cost seismic sensors, develop an optimized network configuration suitable for urban topography, evaluate the network’s technical performance, and assess its integration potential within existing disaster management frameworks. Employing a mixed-methods research design, the study integrates quantitative technical evaluation with qualitative stakeholder analysis. The population targeted comprises 150 urban residents, 50 civil engineering professionals, and 30 emergency management officials across a metropolitan city with a history of seismic activity. A purposive sampling technique is applied to select participants for stakeholder interviews, while sensor testing involves a sample of 20 low-cost seismic units deployed across diverse urban microzones. Data collection instruments include field test logs, seismic signal recordings, survey questionnaires, and semi-structured interviews, supplemented by secondary data from existing seismic hazard maps and urban infrastructure databases. Sensor performance is assessed through analytical methods such as regression analysis to examine signal fidelity, ANOVA to compare sensor sensitivity across different micro-environments, and receiver operating characteristic (ROC) curves to evaluate detection accuracy. The network’s spatial configuration is optimized using geographic information system (GIS)-based spatial analysis and network simulation models, guided by the theoretical framework of complex adaptive systems theory, to ensure resilience and robustness in dynamic urban settings. Validity and reliability of the instruments are established through calibration against high-precision reference seismometers and pilot testing, adhering to international standards for seismic instrumentation. Key expected findings include the identification of cost-effective seismic sensors with acceptable performance levels, optimal network layouts that maximize detection coverage in dense urban settings, and evidence of the network’s capacity to provide reliable early warning signals during seismic events. Analysis of stakeholder insights is anticipated to reveal infrastructural and institutional factors influencing system adoption and sustainability. The study also aims to delineate the technical and operational limitations of low-cost sensors relative to traditional systems, providing a comprehensive evaluation of their practicality for widespread deployment. This research contributes to knowledge by demonstrating the viability of affordable seismic monitoring in resource-constrained urban contexts, integrating sensor technology with urban spatial planning for disaster resilience, and establishing a replicable model for developing countries seeking cost-effective seismic hazard assessment tools. It advances the theoretical understanding of system adaptability and resilience within urban seismic monitoring networks, grounded in complex systems theory. The main conclusion underscores that strategically deployed low-cost seismic sensors can significantly enhance urban seismic monitoring capabilities, particularly when integrated with GIS-based analysis and stakeholder engagement. Recommendations include adopting modular network designs for phased implementation, fostering collaborations among government agencies, academia, and private stakeholders to sustain operations, and further research into sensor technological innovations. Future studies should focus on long-term performance assessments, integration with emerging Internet of Things (IoT) platforms, and scalability to other hazard monitoring applications, thereby contributing to comprehensive urban disaster risk reduction strategies.