A Machine Learning Approach for Parameter Screening in Earthquake Simulation

Monterrubio-Velasco, Marisol; Carrasco-Jiménez, José C.; Castillo-Reyes, Octavio; Cucchietti, Fernando M.; Puente, Josep de la

doi:10.1109/cahpc.2018.8645865

Cited by 8 publications

(16 citation statements)

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“…TREMOL uses a local load sharing (LLS) rule considering the eight nearest neighbors. According to previous studies, such as Monterrubio-Velasco et al (2017), TREMOL redistributes the majority of the load to the four orthogonal neighbors. The load that is transferred to these orthogonal neighbors is called σ O , and it is defined according to Eq.…”

Section: Preprocessing: Input Data and Initial Conditionsmentioning

confidence: 99%

“…Over the years the FBM has been widely used to study failure in a wide range of heterogeneous materials (Hansen et al, 2015;Pradhan and Chakrabarti, 2003). Regardless of the specific FBM type, there are three basic assumptions that all FBMs have in common (Daniels, 1945;Andersen et al, 1997;Kloster et al, 1997;Vázquez-Prada et al, 1999;Phoenix and Beyerlein, 2000;Pradhan et al, 2010;Monterrubio-Velasco et al, 2017). 1.…”

Section: The Fiber Bundle Modelmentioning

confidence: 99%

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A stochastic rupture earthquake code based on the fiber bundle model (TREMOL v0.1): application to Mexican subduction earthquakes

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Abstract. In general terms, earthquakes are the result of brittle failure within the heterogeneous crust of the Earth. However, the rupture process of a heterogeneous material is a complex physical problem that is difficult to model deterministically due to numerous parameters and physical conditions, which are largely unknown. Considering the variability within the parameterization, it is necessary to analyze earthquakes by means of different approaches. Computational physics may offer alternative ways to study brittle rock failure by generating synthetic seismic data based on physical and statistical models and through the use of only few free parameters. The fiber bundle model (FBM) is a stochastic discrete model of material failure, which is able to describe complex rupture processes in heterogeneous materials. In this article, we present a computer code called the stochasTic Rupture Earthquake MOdeL, TREMOL. This code is based on the principle of the FBM to investigate the rupture process of asperities on the earthquake rupture surface. In order to validate TREMOL, we carried out a parametric study to identify the best parameter configuration while minimizing computational efforts. As test cases, we applied the final configuration to 10 Mexican subduction zone earthquakes in order to compare the synthetic results by TREMOL with seismological observations. According to our results, TREMOL is able to model the rupture of an asperity that is essentially defined by two basic dimensions: (1) the size of the fault plane and (2) the size of the maximum asperity within the fault plane. Based on these data and few additional parameters, TREMOL is able to generate numerous earthquakes as well as a maximum magnitude for different scenarios within a reasonable error range. The simulated earthquake magnitudes are of the same order as the real earthquakes. Thus, TREMOL can be used to analyze the behavior of a single asperity or a group of asperities since TREMOL considers the maximum magnitude occurring on a fault plane as a function of the size of the asperity. TREMOL is a simple and flexible model that allows its users to investigate the role of the initial stress configuration and the dimensions and material properties of seismic asperities. Although various assumptions and simplifications are included in the model, we show that TREMOL can be a powerful tool to deliver promising new insights into earthquake rupture processes.

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Section: Preprocessing: Input Data and Initial Conditionsmentioning

confidence: 99%

Section: The Fiber Bundle Modelmentioning

confidence: 99%

A stochastic rupture earthquake code based on the fiber bundle model (TREMOL v0.1): application to Mexican subduction earthquakes

et al. 2019

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“…TREMOL may be able to compensate some errors, but how far this possibility can be exploited needs to be investigated in the future. Further steps in the advance of the model just started to analyze a machine learning approach (Monterrubio-Velasco et al, 2018) which will exploit all the possibilities of this technique. 1.…”

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confidence: 99%

“…This limits the integration of a parallel domain, but a parallel distributed memory is a good approach to solve the problem of large domains. As a result high performance computing facilities are required when very large grid sizes are used (Monterrubio-Velasco et al, 2018). 15 Overall, the results of TREMOL are promising.…”

mentioning

confidence: 99%

“…However, various assumptions and 15 simplifications require further experiments and modifications of the algorithm to cover various tectonic settings. Likewise, the machine learning application by Monterrubio-Velasco et al (2018) needs to be incorporated into the model to determine the optimal parameter ranges for different fault types and tectonic regimes. Although many questions are still left to be answered due to the model's early development stage, TREMOL proved to be a powerful tool which can deliver promising new insights into earthquake triggering processes.…”

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TREMOL: A stochastic rupture earthquake code based on the fiber bundle model. Application to Mexican subduction earthquakes

Monterrubio-Velasco¹,

Rodríguez-Pérez²,

Zúñiga³

et al. 2019

Preprint

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In general terms, earthquakes are the result of brittle failure within the heterogeneous crust of the Earth. However, the rupture process of a heterogeneous material is a complex physical problem which is difficult to model deterministically due to the numerous parameters and physical conditions, which are largely unknown. Considering the variability within the parametrization, it is necessary to analyze earthquakes by means of different approaches. Computational physics may offer alternative ways to study brittle rock failure by generating synthetic seismic data based on physical and statistical models, 5 and by the use of only few free parameters. The Fiber Bundle model (FBM) is a discrete element model, which is able to describe complex rupture processes in heterogeneous materials. In this article, we present a computer code called stochasTic Rupture Earthquake MOdeL, TREMOL. This code is based on the principle of the FBM to investigate the rupture process of asperities on the earthquake rupture surface. In order to validate TREMOL, we carried out a parametric study at first to identify the best parameter configuration while minimizing computational efforts. As test cases, we applied the final configuration to 10 10 Mexican subduction zone earthquakes in order to compare the synthetic results by TREMOL with real data. According to our results, TREMOL is able to model the rupture of an asperity that is defined essentially by two basic dimensions: (1) the size of the fault plane, and (2) the size of the maximum asperity within the fault plane. Based on this data, and few additional parameters, TREMOL is able to generate numerous earthquakes as well as a maximum magnitude for different scenarios within a reasonable error range. The simulated earthquakes magnitudes are of the same order as the real earthquakes. Thus, TREMOL 15 can be used to analyze the behavior of a single asperity or a group of asperities since TREMOL considers the maximum magnitude occurring on a fault plane as a function of the size of the asperity. TREMOL is a simple, and flexible model which allows its users to investigate the role of the initial stress configuration, and the dimensions and material properties of seismic asperities. Although various assumptions and simplifications are included in the model, we show that TREMOL can be a powerful tool which can deliver promising new insights into earthquake rupture processes. 20Copyright statement. TEXT 1 Geosci. Model Dev. Discuss., https://doi.Rupture models of large earthquakes suggest significant heterogeneity in slip and moment release over the fault plane, (e.g., Aochi and Ide, 2011). In order to characterize the seismic source rupture complexity, two main models have been proposed: the asperity model (Kanamori and Stewart, 1978), and the barrier model (Das and Aki, 1977). Asperities are defined as regions on the fault rupture plane that have larger slip and strength in comparison to the average values on the fault plane (Somerville 5 et al., 1999). Asperities also have larger stress ...

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Random Forest Parameterization for Earthquake Catalog Generation

Llácer

Otero

Tous

et al. 2020

Machine Learning, Optimization, and Data Science

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An earthquake is the vibration pattern of the Earth's crust induced by the sliding of geological faults. They are usually recorded for later studies. However, strong earthquakes are rare, small-magnitude events may pass unnoticed and monitoring networks are limited in number and efficiency. Thus, earthquake catalog are incomplete and scarce, and researchers have developed simulators of such catalogs. In this work, we start from synthetic catalogs generated with the TREMOL-3D software. TREMOL-3D is a stochastic-based method to produce earthquake catalogs with different statistical patterns, depending on certain input parameters that mimics physical parameters. When an appropriate set of parameters are used, TREMOL-3D could generate synthetic catalogs with similar statistical properties observed in real catalogs. However, because of the size of the parameter space, a manual searching becomes unbearable. Therefore, aiming at increasing the efficiency of the parameter search, we here implement a Machine Learning approach based on Random Forest classification, for an automatic parameter screening. It has been implemented using the machine learning Python's library Sci-Kit Learn.

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A Machine Learning Approach for Parameter Screening in Earthquake Simulation

Cited by 8 publications

References 14 publications

A stochastic rupture earthquake code based on the fiber bundle model (TREMOL v0.1): application to Mexican subduction earthquakes

A stochastic rupture earthquake code based on the fiber bundle model (TREMOL v0.1): application to Mexican subduction earthquakes

TREMOL: A stochastic rupture earthquake code based on the fiber bundle model. Application to Mexican subduction earthquakes

Random Forest Parameterization for Earthquake Catalog Generation

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