Simulation of spatial and temporal properties of aftershocks by means of the fiber bundle model

Monterrubio-Velasco, Marisol; Zúñiga, F. Ramón; Márquez-Ramírez, Víctor Hugo; Figueroa-Soto, Á.

doi:10.1007/s10950-017-9687-8

Cited by 5 publications

(27 citation statements)

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“…In the present work, we use a dimensionless representation of quantities, so that we can use normalized stresses and Eq. (1) can be written as κ(σ ) = σ ρ , following Moreno et al (2001) and Monterrubio-Velasco et al (2017). Gómez et al (1998) introduced a probabilistic approach as an alternative formulation to the dynamic FBM which we follow here.…”

Section: Probabilistic Fbmmentioning

confidence: 99%

“…The fiber bundle model (FBM), a model based on cellular automata (Peirce, 1926;Daniels, 1945;Coleman, 1956), provides a conceptual and numerical description of the rupture process in heterogeneous media (Kun et al, 2006b). In this article, we present a model that improves over the base model presented in Monterrubio-Velasco et al (2017) by projecting the geometrical fault systems in a FBM algorithm. We developed a novel and powerful simulation model with simple and straightforward input parameters that is capable of providing simulation scenarios that are statistically consistent with real cases.…”

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

et al. 2019

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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: Probabilistic Fbmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…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 10 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):…”

Section: The Fiber Bundle Modelmentioning

confidence: 99%

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

Section: Pre-processing: Input Data and Initial Conditionsmentioning

confidence: 99%

“…The separation between both asperities remains constant. We chose a value of π asp = 0.90 to produce a large contrast between the asperity, and the rest of the fault plane (π = 0.67) (Monterrubio-Velasco et al, 2017). In order to analyze the influence of γ asp (and S a−Asp ), the asperity on the right side hand varying strength values, while the left asperity's strength remains constant.…”

Section: Strength Parameter γ Aspmentioning

confidence: 99%

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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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A Machine Learning Approach for Parameter Screening in Earthquake Simulation

Monterrubio-Velasco

Carrasco-Jiménez

Castillo-Reyes

et al. 2018

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Simulation of spatial and temporal properties of aftershocks by means of the fiber bundle model

Cited by 5 publications

References 93 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

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

A Machine Learning Approach for Parameter Screening in Earthquake Simulation

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