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.

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 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.

scholarly journals All Types of Large Earthquakes Produce Prompt Gravity Signals

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Terri Cook
Keyword(s):  
Early Warning ◽  
Seismic Waves ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
Warning Systems ◽  
Earthquake Early Warning Systems ◽  
Tectonic Settings ◽  
Large Earthquakes

New observations of recently discovered gravity perturbations that precede seismic waves have the potential to improve earthquake early-warning systems in California and other tectonic settings.

Comparison of Machine Learning Techniques Powering Flood Early Warning Systems. Application to a catchment located in the Tropical Andes of Ecuador.

2020 ◽  
Author(s):  
Paul Munoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Early Warning ◽  
Lead Time ◽  
Geometric Mean ◽  
Classification Problem ◽  
Early Warning Systems ◽  
Machine Learning Techniques ◽  
Warning Systems ◽  
Tropical Andes

<p>Flood Early Warning Systems have globally become an effective tool to mitigate the adverse effects of this natural hazard on society, economy and environment. A novel approach for such systems is to actually forecast flood events rather than merely monitoring the catchment hydrograph evolution on its way to an inundation site. A wide variety of modelling approaches, from fully-physical to data-driven, have been developed depending on the availability of information describing intrinsic catchment characteristics. However, during last decades, the use of Machine Learning techniques has remarkably gained popularity due to its power to forecast floods at a minimum of demanded data and computational cost. Here, we selected the algorithms most commonly employed for flood prediction (K-nearest Neighbors, Logistic Regression, Random Forest, Naïve Bayes and Neural Networks), and used them in a precipitation-runoff classification problem aimed to forecast the inundation state of a river at a decisive control station. These are “No-alert”, “Pre-alert”, and “Alert” of inundation with varying lead times of 1, 4, 8 and 12 hours. The study site is a 300-km2 catchment in the tropical Andes draining to Cuenca, the third most populated city of Ecuador. Cuenca is susceptible to annual floods, and thus, the generated alerts will be used by local authorities to inform the population on upcoming flood risks. For an integral comparison between forecasting models, we propose a scheme relying on the F1-score, the Geometric mean and the Log-loss score to account for the resulting data imbalance and the multiclass classification problem. Furthermore, we used the Chi-Squared test to ensure that differences in model results were due to the algorithm applied and not due to statistical chance. We reveal that the most effective model according to the F1-score is using the Neural Networks technique (0.78, 0.62, 0.51 and 0.46 for the test subsets of the 1, 4, 8 and 12-hour forecasting scenarios, respectively), followed by the Logistic Regression algorithm. For the remaining algorithms, we found F1-score differences between the best and the worse model inversely proportional to the lead time (i.e., differences between models were more pronounced for shorter lead times). Moreover, the Geometric mean and the Log-log score showed similar patterns of degradation of the forecast ability with lead time for all algorithms. The overall higher scores found for the Neural Networks technique suggest this algorithm as the engine for the best forecasting Early Warning Systems of the city. For future research, we recommend further analyses on the effect of input data composition and on the architecture of the algorithm for full exploitation of its capacity, which would lead to an improvement of model performance and an extension of the lead time. The usability and effectiveness of the developed systems will depend, however, on the speed of communication to the public after an inundation signal is indicated. We suggest to complement our systems with a website and/or mobile application as a tool to boost the preparedness against floods for both decision makers and the public.</p><p>Keywords: Flood; forecasting; Early Warning; Machine Learning; Tropical Andes; Ecuador.</p>

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
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