scholarly journals Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 17-36
Author(s):  
Matteo Picozzi ◽  
Antonio Giovanni Iaccarino
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Identification Problem ◽  
Geothermal Field ◽  
Induced Earthquakes ◽  
Preparatory Phase ◽  
Seismic Networks ◽  
New Research ◽  
Natural Earthquakes ◽  
Large Earthquakes

Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.

scholarly journals Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

2021 ◽  
Author(s):  
Antonio Giovanni Iaccarino ◽  
Matteo Picozzi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Identification Problem ◽  
Geothermal Field ◽  
Induced Earthquakes ◽  
Preparatory Phase ◽  
Seismic Networks ◽  
New Research ◽  
Natural Earthquakes ◽  
Large Earthquakes

<p>Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a Recurrent Neural Network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.</p>

scholarly journals Evolution of Situated and Abstract Communication in Leader Selection and Borderline Identification Swarm Robotics Problems

2021 ◽  
Vol 11 (8) ◽  
pp. 3516
Author(s):  
Rafael Sendra-Arranz ◽  
Álvaro Gutiérrez
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Swarm Robotics ◽  
Direct Interaction ◽  
Identification Problem ◽  
Selection Task ◽  
Natural Evolution ◽  
Starting Conditions ◽  
Leader Selection

The design of robust yet simple communication mechanisms, that allow the cooperation through direct interaction among robots, is an important aspect of swarm robotics systems. In this paper, we analyze how an identical continuous-time recurrent neural network (CTRNN) controller can lead to the emergence of different kinds of communications within the swarm, either abstract or situated, depending on the problem to be faced. More precisely, we address two swarm robotics tasks that require, at some extent, communication to be solved: leader selection and borderline identification. The parameters of the CTRNN are evolved using separable natural evolution strategies. It is shown that, using the same starting conditions and robots’ controllers, the evolution process leads to the emergence of utterly diverging communications. Firstly, an abstract communication, in which the message carries all the information, results from evolution in the leader selection task. Alternatively, a purely situated communication, meaning that only the context is communicative, emerges when dealing with the borderline identification problem. Nonetheless, scalability and robustness properties are successfully validated.

scholarly journals Designing Forecasting Parameter Algorithm of Environmental Shrimp Using Recurrent Neural Network

2020 ◽  
Vol 18 (12.2) ◽  
pp. 8
Author(s):  
Phat Huu Nguyen ◽  
Quynh Diem Duong ◽  
Minh Van Luong ◽  
Hoang Duc Chu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Environmental Parameters ◽  
Identification Problem ◽  
Shrimp Farming ◽  
White Shrimp ◽  
Learning Factor ◽  
Small Branch ◽  
Algorithmic Construction

With the strong development of science and technology, the study of technologies related to environmental forecasting is important. In recent years, the application of smart technology in aquaculture has been widely applied. Based on the requirement, we focus on predicting the environmental parameters applied in shrimp farming, especially white shrimp, one of the seafood grown in our country. In the paper, we exploit a small branch of identification problem. This paper proposes an algorithmic construction method to predict changes in shrimp farm environmental parameters and simulate the next parameters based on current parameters. The goal of the paper is to reduce the parameter of Recurrent Neural Network (RNN) while ensuring data accuracy. Experimental results show that the proposal algorithm improves up to 85 percent when selecting suitable learning factor of neural networks.

Application of real time recurrent neural network for detection of small natural earthquakes in Poland

2013 ◽  
Vol 62 (3) ◽  
pp. 469-485 ◽  
Author(s):  
Jan Wiszniowski ◽  
Beata M. Plesiewicz ◽  
Jacek Trojanowski
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Natural Earthquakes

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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