Deep Artificial Neural Networks as a Tool for the Analysis of Seismic Data

Кислов, К. В.; Гравиров, В. В.

doi:10.3103/s0747923918010073

Cited by 12 publications

(2 citation statements)

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“…Zöller et al (2007) present a strategy for estimating the recurrence times between large earthquakes and associated seismic hazard on a given fault section. The deep artificial neural networks (ann) used as a tool for the analysis of the seismic data (Kislov et al, 2018). Dhanya (2019) found that the majority of earthquake slip distributions follow truncated exponential or generalized Pareto distribution functions and the best-fitted distribution obtained by model fitting criteria like Akaike Information Criterion, mean squared error, correlation coefficient, and QQ plots.…”

Section: Introductionmentioning

confidence: 99%

Distribution of Earthquake Magnitude Levels in Bangladesh

Rahman¹

2019

JGG

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Earthquakes are one of the main natural hazards which seriously make threats the life and property of human beings. Different probability distributions of the earthquake magnitude levels in Bangladesh are fitted. In terms of graphical assessment and goodness-of-fit criterion, the log-normal distribution is found to be the best fit probability distributions for the earthquake magnitude levels in Bangladesh among the probability distribution considered in this study. The average earthquake magnitude level found 4.67 (in Richter scale) for log-normal distribution and the approximately forty-six percent chance is predicted to take place earthquake magnitude in the interval four to five.

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Section: Introductionmentioning

confidence: 99%

Distribution of Earthquake Magnitude Levels in Bangladesh

Rahman¹

2019

JGG

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“…Moreover, the expanding ML applications to seismology have reached areas such as earthquake early warnings, ground motion estimations, and seismic tomography and inversion, and we here also briefly comment on prominent works, for the sake of completeness. However, we refer the reader to the following complementary survey papers [18,23,29], Kong2019, for additional insights.…”

Section: Introductionmentioning

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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