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

Monterrubio-Velasco, Marisol; Rodríguez-Pérez, Quetzalcóatl; Zúñiga, F. Ramón; Scholz, Doreen; Aguilar-Meléndez, Armando; Puente, Josep de la

doi:10.5194/gmd-12-1809-2019

Cited by 8 publications

(21 citation statements)

References 52 publications

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“…This relation is specific for events in a crustal-plate-boundary tectonic regime (Stirling and Goded, 2012). Careful attention has been given to minimum magnitudes which depend on size of the cell A cell , i.e., are proportional to N −2 Monterrubio-Velasco et al (2018). In order to make results comparable for different grid sizes, we filter out events rupturing less than a minimum amount of cells for the finer grids.…”

Section: Methodsmentioning

confidence: 99%

“…where δ is the time interval (or inter-event time) until the next rupture occurs, valid for any load-sharing rule (Moral et al, 2001a), computed as (Moreno et al, 2001;Moral et al, 2001a)…”

Section: Probabilistic Fbmmentioning

confidence: 99%

“…The load increase in a cell is due to internal load transfer processes. In a companion study (Monterrubio-Velasco et al, 2019), we present the TREMOL v0.1 code which studies the case of asperities. That model was developed as a mainshock simulator based on the FBM to analyze the case of Mexican subduction earthquakes.…”

Section: Algorithmmentioning

confidence: 99%

“…In this work, we choose the values proposed in Monterrubio-Velasco et al (2017, 2019 in order to reduce the degrees of freedom. The only two parameters that change during the execution of the model are σ (x,y) and F (x,y) .…”

Section: Algorithmmentioning

confidence: 99%

“…Even though we focus on one aftershock case, the applicability of our model can be extended to analyze other aftershock sequences using particular fault system as input configuration. For example, in Monterrubio-Velasco et al (2018), this model was applied to classify, via machine learning algorithms, three different aftershock sequences. Lastly, we study the event productivity as function of on-fault (or weakling areas) and off-fault parameters.…”

Section: Introductionmentioning

confidence: 99%

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Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence

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Abstract. Earthquake aftershocks display spatiotemporal correlations arising from their self-organized critical behavior. Dynamic deterministic modeling of aftershock series is challenging to carry out due to both the physical complexity and uncertainties related to the different parameters which govern the system. Nevertheless, numerical simulations with the help of stochastic models such as the fiber bundle model (FBM) allow the use of an analog of the physical model that produces a statistical behavior with many similarities to real series. FBMs are simple discrete element models that can be characterized by using few parameters. In this work, the aim is to present a new model based on FBM that includes geometrical characteristics of fault systems. In our model, the faults are not described with typical geometric measures such as dip, strike, and slip, but they are incorporated as weak regions in the model domain that could increase the likelihood to generate earthquakes. In order to analyze the sensitivity of the model to input parameters, a parametric study is carried out. Our analysis focuses on aftershock statistics in space, time, and magnitude domains. Moreover, we analyzed the synthetic aftershock sequences properties assuming initial load configurations and suitable conditions to propagate the rupture. As an example case, we have modeled a set of real active faults related to the Northridge, California, earthquake sequence. We compare the simulation results to statistical characteristics from the Northridge sequence determining which range of parameters in our FBM version reproduces the main features observed in real aftershock series. From the results obtained, we observe that two parameters related to the initial load configuration are determinant in obtaining realistic seismicity characteristics: (1) parameter P, which represents the initial probability order, and (2) parameter π, which is the percentage of load distributed to the neighboring cells. The results show that in order to reproduce statistical characteristics of the real sequence, larger πfrac values (0.85<πfrac<0.95) and very low values of P (0.0<P≤0.08) are needed. This implies the important corollary that a very small departure from an initial random load configuration (computed by P), and also a large difference between the load transfer from on-fault segments than by off-faults (computed by πfrac), is required to initiate a rupture sequence which conforms to observed statistical properties such as the Gutenberg–Richter law, Omori law, and fractal dimension.

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Section: Methodsmentioning

confidence: 99%

Section: Probabilistic Fbmmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence

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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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Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning

Chandra

Cripps

Butterworth

et al. 2021

Environmental Modelling & Software

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

Cited by 8 publications

References 52 publications

Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence

Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence

Random Forest Parameterization for Earthquake Catalog Generation

Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning

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