scholarly journals Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

2018 ◽  
Vol 45 (10) ◽  
pp. 4773-4779 ◽  
Author(s):  
Zefeng Li ◽  
Men-Andrin Meier ◽  
Egill Hauksson ◽  
Zhongwen Zhan ◽  
Jennifer Andrews
Keyword(s):  
Machine Learning ◽  
Seismic Wave ◽  
Early Warning ◽  
Earthquake Early Warning

scholarly journals Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

2021 ◽  
Vol 9 ◽  
Author(s):  
Antonio Giovanni Iaccarino ◽  
Philippe Gueguen ◽  
Matteo Picozzi ◽  
Subash Ghimire
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Single Type ◽  
Gradient Boosting ◽  
Support Vector ◽  
Peak Displacement ◽  
Steel Reinforced Concrete

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models.

Generating Phone-quality Records to Train Machine Learning Models for Smartphone-based Earthquake Early Warning

2020 ◽  
Author(s):  
Zengwei Zheng ◽  
Lifei Shi ◽  
Sha Zhao ◽  
Jianmin Hou ◽  
Lin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Transformation Method ◽  
Earthquake Early Warning ◽  
Sensor Data ◽  
Superior Performance ◽  
Quality Data ◽  
Learning Models ◽  
Seismic Networks ◽  
Machine Learning Models

Abstract Earthquake Early Warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake damage. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary research method of smartphone-based earthquake early warning has emerged. Smartphones-based earthquake early warning system applies signal processing techniques and machine learning algorithms to the sensor data recorded by smartphones for better monitoring earthquakes. But it is challenging to collect abundant phone-recorded seismic data for training related machine learning models and selecting appropriate features for these models. One alternative way to solve this problem is to transform the data recorded by seismic networks into phone-quality data. In this paper, we propose such a transformation method by learning the differences between the data recorded by seismic networks and smartphones, in two scenarios: phone fixed and free located on tables, respectively. By doing this, we can easily generate abundant phone-quality earthquake data to train machine learning models used in EEW systems. We evaluate our transformation method by conducting various experiments, and our method performs much better than existing methods. Furthermore, we set up a case study where we use the transformed records to train machine learning models for earthquake intensity prediction. The results show that the model trained by using our transformed data produces superior performance, suggesting that our transformation method is useful for smartphone-based earthquake early warning.

scholarly journals A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning

2020 ◽  
Vol 34 (01) ◽  
pp. 403-411 ◽  
Author(s):  
Kevin Fauvel ◽  
Daniel Balouek-Thomert ◽  
Diego Melgar ◽  
Pedro Silva ◽  
Anthony Simonet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Response Time ◽  
Early Warning ◽  
Classification Problem ◽  
Relative Strength ◽  
Earthquake Early Warning ◽  
Sensor Data ◽  
Efficient Computation ◽  
Geographically Distributed ◽  
Large Earthquakes

Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data consequently, affecting the response time and the robustness of EEW systems.In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.

Development of an automatic hypocenter location system for the earthquake early warning in Japan

2007 ◽  
Vol 60 (5) ◽  
pp. 399-406
Author(s):  
Shigeki Horiuchi ◽  
Aya Kamimura ◽  
Hiromitsu Nakamura ◽  
Shunroku Yamamoto ◽  
Changjiang Wu
Keyword(s):  
Early Warning ◽  
Earthquake Early Warning ◽  
Location System ◽  
Hypocenter Location

Development of A Drug Early Warning System Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Warning System ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a Drug Early Warning System Model (DEWSM), it included drug injections and vital signs as this research important features. We use it to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose this new model for detecting cardiac arrest via drug classification and by using a sliding window; we apply learning-based algorithms to time-series data for a DEWSM. By treating drug features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model, we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits (intravenous therapy), and replenishers and regulators of water and electrolytes (fluid and electrolyte supplement). The best AUROC of bits is 85%, it means the medical expert suggest the drug features: bits, it will affect the vital signs, and then the evaluate this model correctly classified patients with CPR reach 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. It can be seen that the use of new AI technology will achieve better results, currently comparable to the accuracy of traditional common RF, and the LSTM model can be adjusted in the future to obtain better results. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. The National Early Warning Score (NEWS) only focuses on the score of vital signs, and does not include factors related to drug injections. In this study, the experimental results of adding the drug injections are better than only vital signs. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, we use traditional machine learning methods and deep learning (using LSTM method as the main processing time series data) as the basis for comparison of this research. The proposed DEWSM, which offers 4-hour predictions, is better than the NEWS in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

EARTHQUAKE EARLY WARNING DURING THE 2019 RIDGECREST EARTHQUAKE SEQUENCE: WHAT HAPPENED AND WHAT’S NEXT?

2019 ◽  
Author(s):  
Elizabeth S. Cochran ◽  
◽  
Sarah E. Minson ◽  
Annemarie S. Baltay ◽  
Julian Bunn ◽  
...  
Keyword(s):  
Early Warning ◽  
Earthquake Early Warning ◽  
Earthquake Sequence
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