Earthquake Event Recognition on Smartphones Based on Neural Network Models
Abstract
:1. Introduction
- Making use of the broad distribution and vast amount of smartphones, we can increase the seismic station density, expand the early warning coverage, and reduce the investment in deploying and operating a traditional EEW system;
- With the Global Navigation Satellite System (GNSS) positioning function embedded in smartphones, the earthquake epicenter can be promptly located;
- APPs are more convenient to develop, update and maintain, and some EEW parameters can be customized according to user needs.
2. Datasets
2.1. Data Sources
2.2. Data Preprocessing
- Removing irrelevant information such as time and location to leave only three-component acceleration;
- Truncating each acceleration record into specified lengths according to different activities (Table 1).
- As to seismic data, the preprocessing steps are:
- Extracting the three-component acceleration data from original files, and merging them into one;
- Manually excluding those data with unclear P wave arrival;
- Detrending each acceleration;
- Adding the mobile phone self-noise to each record for simulating earthquake data recorded by a mobile phone.
3. Methods
3.1. Feature Selection
Algorithm 1 PCA code in Python |
From sklearn.decomposition import PCA |
estimator = PCA(n_components = 10) # Initialization, n_components is the dimension reduction dimension |
# Use the training features to determine the orientation of the 10 orthogonal dimensions and transform the original training features |
pca_X_train = estimator.fit_transform(data_train_x) |
# The test features are also transformed according to the above-mentioned 10 orthogonal dimension directions (transform) |
pca_X_test = estimator.transform(data_test_x) |
3.2. FCNN
3.2.1. Architecture
3.2.2. Performance
3.3. CNN
3.3.1. Architecture
3.3.2. Performance
3.4. Other Algorithms
4. Discussion
4.1. Comparison of the Results of Different Algorithms
4.2. Can We Apply the Trained Models to an Actual EEW APP?
4.3. Limitations and Corresponding Solutions of EEW on Mobile Phones
- The sensors in mobile phones are not dedicated to detecting earthquakes, so they cannot reach the accuracy of professional strong-motion accelerometers;
- The installed capacity of mobile APPs is determined by the results of user experience, such as power consumption, CPU load, and local storage space occupied. If the installed capacity is insufficient, we will not be able to achieve the goal of trading quantity for quality, making it difficult to carry out EEW.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phone Model | Data Type | Phone’s Location | Velocity | Data Length (Second) |
---|---|---|---|---|
Vivo S5 Huawei nova 5pro Xiaomi 8se | Walking | In hands/In bags | Low/Normal/Fast | 90 |
Running | In hands/In bags | Low/Fast | ||
Going up and down stairs | In hands/In bags | Normal | ||
Riding a bike | In bags | |||
Taking the bus | In hands/In bags | 30/35/60/90 | ||
Taking the subway | In hands/In bags |
Feature Name | Number of Features | Calculation Formula | Explanation |
---|---|---|---|
The peak acceleration in a frame | 1 | ) | x, y, z: EW, NS, UD at a certain moment i: index, the value is [1, N] : the third and the first quartile S: a component data, such as EW, NS, or UD N: data length average of UD |
The average acceleration in a frame | 1 | ||
The absolute median deviation | 3 | ||
Interquartile range (IQR) | 3 | ||
Variance | 3 | ||
Standard deviation | 3 | ||
Average time-domain energy | 3 | ||
Horizontal average time-domain energy | 1 |
Feature Dimension | Average Validation Accuracy | Best Test Accuracy |
---|---|---|
10 | 78.98% | 97.33% |
8 | 78.92% | 99.25% |
5 | 78.7% | 99.04% |
3 | 77.39% | 99.22% |
Hidden Layers | Number of Nodes | Average Validation Accuracy | Best Test Accuracy | Training Time (Second) |
---|---|---|---|---|
2 | 4, 3 | 98.77% | 98.4% | 150 |
3 | 6, 4, 3 | 99.06% | 98.83% | 196 |
4 | 8, 6, 4, 3 | 99.23% | 99.46% | 246 |
5 | 10, 8, 6, 4, 3 | 99.27% | 99.36% | 291 |
6 | 12, 10, 8, 6, 4, 3 | 99.29% | 99.37% | 360 |
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Chen, M.; Peng, C.; Cheng, Z. Earthquake Event Recognition on Smartphones Based on Neural Network Models. Sensors 2022, 22, 8769. https://doi.org/10.3390/s22228769
Chen M, Peng C, Cheng Z. Earthquake Event Recognition on Smartphones Based on Neural Network Models. Sensors. 2022; 22(22):8769. https://doi.org/10.3390/s22228769
Chicago/Turabian StyleChen, Meirong, Chaoyong Peng, and Zhenpeng Cheng. 2022. "Earthquake Event Recognition on Smartphones Based on Neural Network Models" Sensors 22, no. 22: 8769. https://doi.org/10.3390/s22228769