Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques

Gutiérrez, L.; Jiménez, César Eduardo; Vasquez, Luis Fernando Niño

doi:10.15446/esrj.v23n2.70581

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The proposed method needs to meet the requirements of step by step identification of the large range and many seismic collapse problems of seismic collapse, so as to determine the slope that does not need further prediction and analysis. Next, a stepby-step forecast and analysis can be carried out (Li et al 2015, Zhang et al 2016, Azzam et al 2018, Schultz et al 2018, Ochoa Gutiérrez et al 2019, Leventeli et al 2020). This can not only meet the requirements of rapid prediction, but also save time and the cost of investigation, experiment and calculation, with greater economic and social benefits.…”

Section: The Stability Discrimination Methods Of Highway Slope Under ...mentioning

confidence: 99%

Design of highway pavement earthquake damage prediction method considering horizontal earthquake force

Peng¹

2023

Rev. Int. Contam. Ambie.

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Due to the influence of geographical environment and horizontal seismic force, it is difficult to predict the seismic damage of urban highway pavement. In addition, poor engineering characteristics will cause damage to the stability of the subgrade and extremely easy to deformation. The above situation has become an important problem in highway construction. To improve the accuracy of earthquake damage prediction of highway pavement, a method of earthquake damage prediction considering horizontal seismic force is proposed. This method is based on the fuzzy comprehensive evaluation, taking the highway bridge, subgrade and pavement, roadside environment and highway tunnel as the main factors, and establishing the post-earthquake damage evaluation model of the highway system. Based on the analysis of the relevant data, the power polynomial prediction model is proposed, and the feasibility and parameter determination method of the power polynomial are analyzed. The stability of highway pavement under earthquake is predicted. The results show that the design method can predict the seismic damage of highway pavement. In addition, the proposed surface model can also simulate the settlement value and predict the settlement law.

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Section: The Stability Discrimination Methods Of Highway Slope Under ...mentioning

confidence: 99%

Design of highway pavement earthquake damage prediction method considering horizontal earthquake force

Peng¹

2023

Rev. Int. Contam. Ambie.

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“…ML approaches are expected to improve the performance using information discarded in the conventional method. Gutierrez et al (2019) estimated the azimuth direction of incident seismic waves using support vector machines. Mousavi and Beroza (2020) and Ristea and Radoi (2022) estimated hypocenter locations or related parameters using DL from a single station waveform.…”

Section: Earthquake Locationmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract

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“…Machine learning (ML) has recently delivered fascinating results in advancing accurate models for susceptibility mapping of geohazards, e.g., earthquakes, landslides, earth fissures, and rockfalls 57 – 62 . However, the application of machine learning methods in avalanche prediction has been minimal 63 , 64 .…”

Section: Introductionmentioning

confidence: 99%

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Choubin

Borji

Hosseini

et al. 2020

Sci Rep

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Snow avalanche is among the most harmful natural hazards with major socioeconomic and environmental destruction in the cold and mountainous regions. The devastating propagation and accumulation of the snow avalanche debris and mass wasting of surface rocks and vegetation particles threaten human life, transportation networks, built environments, ecosystems, and water resources. Susceptibility assessment of snow avalanche hazardous areas is of utmost importance for mitigation and development of land-use policies. This research evaluates the performance of the well-known machine learning methods, i.e., generalized additive model (GAM), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and support vector machine (SVM), in modeling the mass wasting hazard induced by snow avalanches. The key features are identified by the recursive feature elimination (RFE) method and used for the model calibration. The results indicated a good performance of the modeling process (Accuracy > 0.88, Kappa > 0.76, Precision > 0.84, Recall > 0.86, and AUC > 0.89), which the SVM model highlighted superior performance than others. Sensitivity analysis demonstrated that the topographic position index (TPI) and distance to stream (DTS) were the most important variables which had more contribution in producing the susceptibility maps.

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Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques

Cited by 4 publications

References 22 publications

Design of highway pavement earthquake damage prediction method considering horizontal earthquake force

Design of highway pavement earthquake damage prediction method considering horizontal earthquake force

Recent advances in earthquake seismology using machine learning

Mass wasting susceptibility assessment of snow avalanches using machine learning models

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