Nonlinear Inversion with Quadratic Function Approaching Method for Magnetotelluric Data

Liu, Yan; Wen-bao, HU

doi:10.1002/cjg2.585

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…(2017) proposed a new joint inversion strategy, which uses the existing inversion program for the single inversion of different models, and the model constraints are only introduced through cross‐gradient minimization between different models after each iteration. This strategy decouples the data fitting process and structural constraint process, which overcomes the problem of finding adequate relative scaling between different data sets (Bouchedda et al., 2012; L. Gan et al., 2022; Heincke et al., 2017; Zhu & Harris, 2015). The balance between data fitting and structural similarity is achieved by adding the model perturbations from single inversions to constrain the process of the cross‐gradient minimization.…”

Section: Methodsmentioning

confidence: 99%

Seismogenic Structure of the 2014 M6.5 Ludian Earthquake From Three‐Dimensional Joint Inversion of Magnetotelluric Data and Seismic Arrival Times

et al. 2023

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On 3 August 2014, a destructive earthquake with a magnitude of 6.5 occurred in the Ludian region in Yunan, China, causing heavy casualties. However, the seismogenic structure of the Ludian earthquake is still unclear. To address this issue, we have developed a three‐dimensional joint inversion algorithm for magnetotelluric data and seismic body wave arrival times based on the cross gradient structural similarity constraint, which utilizes the complementary advantages of the two different datasets. We use arrival times of P‐ and S‐waves from 6,369 local earthquakes recorded by 30 seismic stations and magnetotelluric impedances from 127 MT stations to perform the joint inversion to construct a combined 3‐D P‐wave velocity (Vp), S‐wave velocity (Vs), and resistivity models and the locations of relocated aftershocks in the source region of the Ludian earthquake. Our models show significant low Vp/Vs ratios and low resistivity in the upper crust of the main shock zone, and the relocated aftershocks form an L‐shaped zone. Based on petrophysical studies and analysis of the cross‐plots of different physical properties, we infer the existence of cracks/fractures and fluids as well as high quartz contents in the source region. These fluids may play a key role in promoting the Ludian earthquake by reducing the effective normal stress of the fault zone, and the existence of high quartz contents further makes the crustal rocks in the source region more brittle. Our study provides a new evidence how the properties of the crust in the focal area have some control on the genesis of moderate to large earthquakes.

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Section: Methodsmentioning

confidence: 99%

Seismogenic Structure of the 2014 M6.5 Ludian Earthquake From Three‐Dimensional Joint Inversion of Magnetotelluric Data and Seismic Arrival Times

et al. 2023

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“…Its resolution is the lowest. In the model solved by QFAM [32] , this low resistivity layer is imaged but the resolution is lower than OCCAM and ARIA. The inversion results of ARIA and OCCAM are good compared with the original model, and ARIA is better than OCCAM.…”

Section: One-dimensional Theoretical Modelmentioning

confidence: 99%

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Chen

Zhao

Tang

et al. 2005

Chinese J. Geophys.

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In this paper, a new inversion method, Adaptive Regularized Inversion Algorithm (ARIA), is presented to overcome the difficulty of the determination for the regularized factor. Firstly, a new data variance disposing method, data variance normalization method, is put forward. This method uses a new way to calculate the influence matrix of data variance in inversion. Thus, the data variance only influences data fitting, and has no influence on the weight between data object function and the model constraint object function. So the influencing factors of the determination of the regularized factor are reduced. Secondly, the definition of the roughness kernel matrix is presented in the course of constructing model constraint object function, and a concise equation of it is derived. Thus the construction of the model object function becomes very simple and direct. Thirdly, two adaptive methods of the regularized factor are put forward based on the relations of data object function, the model constraint object function, and the regularized factor. Finally, ARIA is used to solve an magnetotelluric (MT) one‐dimensional inversion problem by the constraint of the flattest model. Several examples are illustrated to exemplify the effect of ARIA.

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“…(2015) applied trans‐dimensional inversion for CSEM data in the German North Sea. Based on the Bayesian sampling method, Yang and Hu (2008) combined a weighted matrix with reliability in the data space to complement the probabilistic analysis of magnetotelluric data with noise. Minsley (2011) designed a trans‐dimensional Bayesian MCMC algorithm for frequency‐domain electromagnetic data inversion.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inversion and uncertainty analysis of transient electromagnetic data

Zhang,

Sun,

Liu

et al. 2024

Near Surface Geophysics

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Quantification of non‐uniqueness and uncertainty is important for transient electromagnetism (TEM). To address this issue, we develop a trans‐dimensional Bayesian inversion schema for TEM data interpretation. The trans‐dimensional posterior probability density (PPD) offers a solution to model selection and quantifies parameter uncertainty resulting from the model selection from all possible models rather than determining a single model. We use the reversible‐jump Markov chain Monte Carlo sampler to draw ensembles of models to approximate PPD. In addition to providing reasonable model selection, we address the reliability of the inversion results for uncertainty analysis. This strategy offers reasonable guidance when interpreting the inversion results. We make the following improvements in this paper. First, in terms of algorithmic acceleration, we use the nonlinear optimization inversion results as the initial model and implement the multi‐chain parallel method. Second, we develop double factors to control the sampling step size of the proposed distribution, so that the sampling models cover the high‐probability region of the parameter space as much as possible. Finally, we provide the potential scale reduction factor‐η convergence criteria to assess the convergence of the samples and ensure the rationality of the output models. The proposed methodology is first tested on synthetic data and subsequently applied to a field dataset. The TEM inversion results show that probability inversion can provide reliable references for data interpretation through uncertainty analysis.

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Nonlinear Inversion with Quadratic Function Approaching Method for Magnetotelluric Data

Cited by 6 publications

References 6 publications

Seismogenic Structure of the 2014 M6.5 Ludian Earthquake From Three‐Dimensional Joint Inversion of Magnetotelluric Data and Seismic Arrival Times

Seismogenic Structure of the 2014 M6.5 Ludian Earthquake From Three‐Dimensional Joint Inversion of Magnetotelluric Data and Seismic Arrival Times

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Bayesian inversion and uncertainty analysis of transient electromagnetic data

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