A Comprehensive Analysis of Real-World Accelerometer Data Quality in a Global Smartphone-based Seismic Network

TL;DR

This study analyzes the quality of accelerometer data from a global smartphone seismic network, identifying key factors affecting data reliability and demonstrating improved earthquake magnitude estimation by filtering low-quality data.

Contribution

It provides a comprehensive evaluation of accelerometer data quality factors in a large-scale smartphone seismic network, highlighting the impact on earthquake analysis accuracy.

Findings

Accelerometer model and device specs significantly influence data quality.

Filtering low-quality data improves earthquake magnitude estimation.

Data quality varies across device types and geolocations.

Abstract

The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. Additionally, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.