Earthquake trend prediction using long short-term memory RNN

Bhandarkar, Tanvi; Vardaan, K; Satish, Nikhil; Sridhar, Sharath Nittur; Sivakumar, R.; Ghosh, Snehasish

doi:10.11591/ijece.v9i2.pp1304-1312

Cited by 31 publications

(16 citation statements)

References 14 publications

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“…Kishore et al [116] compared the feed-forward NN and LSTM to predict the trend of an earthquake. From the Afghanistan and Tajikistan area, 5000 samples were taken for this study.…”

Section: ) Earthquake's Characteristics Studiesmentioning

confidence: 99%

“…LSTM is good method for finding pattern in data. Therefore, researchers are using it for earthquake prediction [42], [116]. Wang et al [42] proposed to use LSTM for earthquake prediction, which used spatio-temporal correlation.…”

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

confidence: 99%

“…Vardaan et al [116] used LSTM for predicting the future trend of earthquakes. The overall R 2 score of the model was -0.252.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

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Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

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“…Kishore et al [116] compared the feed-forward NN and LSTM to predict the trend of an earthquake. From the Afghanistan and Tajikistan area, 5000 samples were taken for this study.…”

Section: ) Earthquake's Characteristics Studiesmentioning

confidence: 99%

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

confidence: 99%

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Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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“…Recent applications of RcNN to earthquake data are given in [26,7] with the purpose of earthquake prediction. The common architecture of a RcNN presents a set of Long Short-Term Memory (LSTM) layers, combined to dropout layers to avoid overfitting, and supply an efficient framework for detection and inference of temporal patterns.…”

Section: Rcnnmentioning

confidence: 99%

Artificial Neural Networks as Emerging Tools for Earthquake Detection

Rojas

Otero

Alvarado

et al. 2019

CyS

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As seismic networks continue to spread and monitoring sensors become more efficient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identification, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recent advances are related to the application of Artificial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.

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“…The initial training relies on 170 events, including regional and teleseismic earthquakes, and the full operating network was tested during different time periods in 2009. Recent applications of RNNs to early warning systems and to earthquake prediction are given in Ibrahim et al (2018) and Bhandarkar et al (2019), respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Earthquake Detection and Source Region Estimation in North-Central Venezuela

Tous

Alvarado

Otero

et al. 2020

Bulletin of the Seismological Society of America

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Reliable earthquake detection algorithms are necessary to properly analyze and catalog the continuously growing seismic records. We report the results of applying a deep convolutional neural network, called UPC-UCV (Universitat Politecnica de Catalunya - Universidad Central de Venezuela), over single-station three-channel signal windows for P-wave earthquake detection and source region estimation in north-central Venezuela. The analysis is performed on a new dataset of handpicked arrivals of P waves from local events, named CARABOBO, built and made public for reproducibility and benchmarking purposes. The CARABOBO dataset consists of three-channel continuous data recorded by the broadband stations of the Venezuelan Foundation for Seismological Research in the region of 9.5°–11.5°N and 67.0°–69.0°W during the time period from April 2018 to April 2019. During this period, 949 earthquakes were recorded in that area, corresponding to earthquakes with magnitudes in the range from Mw 1.1 to 5.2. To estimate the epicentral source region of a detected event, the proposed network employs geographical distribution of the CARABOBO dataset into K clusters as a basis. This geographical partitioning is automatically performed by the k-means algorithm, and the optimality of the K-values for our dataset has been assessed using the elbow (K=5) and silhouette (K=3) methods. For target seismicity, the proposed network achieves 95.27% detection accuracy and 93.36% source region estimation accuracy, when using K=5 geographic clusters. The location accuracy slightly increases to 95.68% in the case of K=3 geographic partitions. The detection capability of this network has also been tested on the OKLAHOMA dataset, which compiles more than 2000 local earthquakes that occurred in this U.S. state. Without any modification, the proposed network yields excellent detection results when trained and evaluated on that dataset (98.21% accuracy; ConvNetQuake, fine-tuned for this dataset, achieves a 97.32% accuracy), corresponding to a totally different geographical region.

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Earthquake trend prediction using long short-term memory RNN

Cited by 31 publications

References 14 publications

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Artificial Neural Networks as Emerging Tools for Earthquake Detection

Deep Neural Networks for Earthquake Detection and Source Region Estimation in North-Central Venezuela

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