A method for determining the regularization parameter and the relative weight ratio of the seismic slip distribution with multi-source data

Wang, Leyang; Zhao, Xiong; Gao, Hua

doi:10.1016/j.jog.2018.04.005

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…Krawczyk-Stańdo & Rudnicki, 2007;Wang et al, 2018); that search for a balance between over-fitting and over-smoothing based on data and model resolution, such as jRi(Barnhart & …”

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confidence: 99%

An EPIC Tikhonov Regularization: Application to Quasi‐Static Fault Slip Inversion

et al. 2021

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“…Krawczyk-Stańdo & Rudnicki, 2007;Wang et al, 2018); that search for a balance between over-fitting and over-smoothing based on data and model resolution, such as jRi(Barnhart & …”

mentioning

confidence: 99%

An EPIC Tikhonov Regularization: Application to Quasi‐Static Fault Slip Inversion

et al. 2021

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“…In order to suppress unrealistic scattered solutions and guarantee a consistency of contiguity, a spatial Laplacian constraint was introduced in the inverse problem formulation 72 . The Lagrange multiplier was obtained by the U-curve method 73 .…”

Section: Methodsmentioning

confidence: 99%

Repeating earthquakes and ground deformation reveal the structure and triggering mechanisms of the Pernicana fault, Mt. Etna

Cannata

Iozzia

Alparone

et al. 2021

Commun Earth Environ

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Structure and dynamics of fault systems can be investigated using repeating earthquakes as repeatable seismic sources, alongside ground deformation measurements. Here we utilise a dataset of repeating earthquakes which occurred between 2000 and 2019 along the transtensive Pernicana fault system on the northeast flank of Mount Etna, Italy, to investigate the fault structure, as well as the triggering mechanisms of the seismicity. By grouping the repeating earthquakes into families and integrating the seismic data with GPS measurements of ground deformation, we identify four distinct portions of the fault. Each portion shows a different behaviour in terms of seismicity, repeating earthquakes and ground deformation, which we attribute to structural differences including a segmentation of the fault plane at depth. The recurrence intervals of repeating earthquake families display a low degree of regularity which suggests an episodic triggering mechanism, such as magma intrusion, rather than displacement under a constant stress.

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“…e shape of the curve is similar to the letter "L." e optimum regularization parameter is the value of the position where the curvature of the curve is the largest (that is, the inflection point of the letter L). e U-curve method can be defined as follows [34]:…”

Section: Determination Of Regularization Parameters By U-curvementioning

confidence: 99%

“…In order to solve ill-posed problem for load identification, the choice of regularization parameters plays a key role in regularization, and there are many methods to select regularization parameters, such as the L-curve method, the generalized cross validation (GCV), the U-curve method, and the discrepancy principle [24][25][26][27]. To mitigate error propagation and ill-posed problem during the process of identification, Jia et al [28,29] proposed a weighted regularization approach based on the proper orthogonal decomposition (POD), in which the regularization parameter was selected by the GCV method.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Load Identification Based on Explicit Wilson‐θ and Improved Regularization Algorithm

Yuchuan

Zhao

2019

Shock and Vibration

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In the research of dynamic load identification, the method of obtaining kernel function matrix is usually rather cumbersome. To solve this problem, an explicit dynamic load identification algorithm based on the Wilson-θ (DLIAEW) method is proposed to easily obtain the kernel function matrix as long as the parameters of the system are known. To aim at the ill-posed problem in the inverse problem, this paper improves the Tikhonov regularization, proposes an improved regularization algorithm (IRA), and introduces the U-curve method to determine the regularization parameters. In the numeric simulation investigation of a four dofs vibrating system, effects of the sampling frequency and the noise level on the regularization parameters and the identification errors of impact and harmonic loads for the IRA are discussed in comparison with the Tikhonov regularization. Finally, the experiments of a cantilever beam excited by impact and harmonic loads are carried out to verify the advantages of the IRA.

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A method for determining the regularization parameter and the relative weight ratio of the seismic slip distribution with multi-source data

Cited by 17 publications

References 18 publications

An EPIC Tikhonov Regularization: Application to Quasi‐Static Fault Slip Inversion

An EPIC Tikhonov Regularization: Application to Quasi‐Static Fault Slip Inversion

Repeating earthquakes and ground deformation reveal the structure and triggering mechanisms of the Pernicana fault, Mt. Etna

Research on Dynamic Load Identification Based on Explicit Wilson‐θ and Improved Regularization Algorithm

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