scholarly journals Machine learning of source spectra for large earthquakes

2021 ◽  
Author(s):  
Shang Ma ◽  
Zefeng Li ◽  
Wei Wang
Keyword(s):  
Machine Learning ◽  
Source Spectra ◽  
Large Earthquakes

scholarly journals Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field

2018 ◽  
Vol 4 (5) ◽  
pp. eaao2929 ◽  
Author(s):  
Benjamin K. Holtzman ◽  
Arthur Paté ◽  
John Paisley ◽  
Felix Waldhauser ◽  
Douglas Repetto
Keyword(s):  
Machine Learning ◽  
Seismic Source ◽  
Geothermal Field ◽  
Source Spectra ◽  
Cyclic Changes

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.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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