Three-dimensional gravity inversion based on sparse recovery iteration using approximate zero norm

Meng, Zhaohai; Xu, Xuechun; Huang, Danian

doi:10.1007/s11770-018-0688-3

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…However, recovering the pattern of real Moho structures becomes even more difficult in some locations with extreme undulation, which is an inherent restriction of gravimetric inversion. At the moment, several studies have made progress on recovering the discontinuous interface geometry using the

{L}_{P}

inversion (Feng et al., 2019; Meng et al., 2018; Santos et al., 2015; Sun & Li, 2014).…”

Section: Synthetic Casesmentioning

confidence: 99%

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

Chen

2022

JGR Solid Earth

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The Mohorovĩcíc discontinuity (Moho) is the boundary between the Earth's crust and upper mantle. It is critical to determine accurate Moho depth for gaining insight into the deep structure of the Earth, material and energy exchange processes, and geodynamic problems (Ebbing et al., 2018;Gozzard et al., 2019;Xu et al., 2017). Currently, the seismic and gravimetric methods are the primary geophysical methods for determining the depth of Moho. Seismic methods are quite expensive, and seismic stations are sparse in many regions with extreme conditions (e.g., ocean, plateau, and polar). In addition, seismic methods could be ineffective for recovering three-dimensional (3D) Moho topography across vast regions. Along with seismic surveys, gravity observations can also be used to estimate the Moho depth. In recent decades, as satellite-gravity missions, the Gravity Recovery and Climate Experiment (GRACE) (Tapley et al., 2004), the Gravity field and steady-state Ocean Circulation Explorer (GOCE) (Floberghagen et al., 2011), GRACE Follow-On (GRACE-FO) (Kornfeld et al., 2019), and satellite altimetry (Sandwell et al., 2014) have advanced, it is now possible to obtain high resolution and accurate gravity data with almost global and homogeneous coverage. At the moment, several global gravity field models (GGMs) have been constructed with high accuracy and resolution by combining terrestrial, altimetric, and satellite gravity data, such as EGM2008 (Pavlis et al., 2012), EIGEN-6C4 (Förste et al., 2014), and XGM2019e_2159 (Zingerle et al., 2020. These GGMs with maximum degree/order of 2190 (Spatial resolution ∼10 km) have been

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{L}_{P}

inversion (Feng et al., 2019; Meng et al., 2018; Santos et al., 2015; Sun & Li, 2014).…”

Section: Synthetic Casesmentioning

confidence: 99%

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

Chen

2022

JGR Solid Earth

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“…Inversion methods that utilize a smooth stabilizer produce model typically characterized by smooth features, and hence have difficulties in recovering blocky structures or non-smooth distributions that have sharp boundaries or abrupt changes in physical properties (Farquharson, 2008). To overcome this problem, non-smooth stabilizers that help to produce compact and sharp models have been applied successfully (Zhdanov, 2009;Meng et al, 2018). Since we are interested in developing a gravity inversion method that can produce compact and sharp models, we use a non-smooth stabilizer through the L 0 -norm on the model parameters and will be discussed in the next subsection.…”

Section: Inverse Modelingmentioning

confidence: 99%

“…Many procedures such as gradient projection approach (Wang & Ma, 2007;Lelièvre et al, 2009), transform function approach (Pilkington, 2008) and logarithmic barrier approach (Li & Oldenburg, 2003) have been applied in different inversion schemes to implement this constraint. However, with regard to L 0norm stabilizer based gravity inversion methods an effective method is the direct utilization of lower and upper density constraint (Meng et al, 2018). Hence, in this work the direct density bound inequality constraint is used, that is at each iteration density contrast of each rectangular block is bounded by minimum and maximum density constraint function given by:…”

Section: Physical Parameter Inequality Constraintmentioning

confidence: 99%

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

Gebre

Lewi

2022

Preprint

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Abstract. We present a gravity inversion method that can produce compact and sharp images, to assist the modeling of non-smooth geologic features. The proposed iterative inversion approach makes use of L0-norm stabilizing functional, hard, and physical parameter inequality constraints, and depth weighting function. The method incorporates an auto-adaptive regularization technique, which automatically determines a suitable regularization parameter and error weighting function that helps to improve both the stability and convergence of the method. The auto-adaptive regularization and error weighting matrix are not dependent on the known noise level. Because of that, the method yields reasonable results even the noise level of the data is not known properly. The utilization of an effectively combined stopping rule to terminate the inversion process is another improvement that is introduced in this work. The capacity and the efficiency of the new inversion method were tested by inverting randomly chosen synthetic and measured data. The synthetic test models consist of multiple causative blocky bodies, with different geometries and density distributions that are vertically and horizontally distributed adjacent to each other. Inversion results of the synthetic data show that the developed method can recover models that adequately match the real geometry, location, and densities of the synthetic causative bodies. Furthermore, the testing of the improved approach using published real gravity data confirmed the potential, and practicality of the method in producing compact and sharp inverse images of the subsurface.

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“…Besides, considering that the original DE failed to obtain a smooth model, we designed a new mutation strategy to solve this problem. Although the inversion regularized with l p norm has been widely solved by applying the gradient-based methods [40][41][42][43][44], the application of adaptive DE in the physical property of magnetic data is studied for the first time.…”

Section: Introductionmentioning

confidence: 99%

lp Norm Smooth Inversion of Magnetic Anomaly Based on Improved Adaptive Differential Evolution

Cheng

2021

Applied Sciences

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Due to the approved applicability of differential evolution (DE) in geophysical problems, the algorithm has been widely concerned. The DE algorithms are mostly applied to solve the geophysical parametric estimation based on specific models, but they are rarely used in solving the physical property inverse problem of geophysical data. In this paper, an improved adaptive differential evolution is proposed to solve the lp norm magnetic inversion of 2D data, in which the perturbation direction in the mutation strategy is smoothed by using the moving average technique. Besides, a new way of updating the regularization coefficient is introduced to balance the effect of the model constraint adaptively. The inversion results of synthetic models demonstrate that the presented method can obtain a smoother solution and delineate the distributions of abnormal bodies better. In the field example of Zaohuoxi iron ore deposits in China, the reconstructed magnetic source distribution is in good agreement with the one inferred from drilling information. The result shows that the proposed method offers a valuable tool for magnetic anomaly inversion.

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Three-dimensional gravity inversion based on sparse recovery iteration using approximate zero norm

Cited by 16 publications

References 37 publications

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

lp Norm Smooth Inversion of Magnetic Anomaly Based on Improved Adaptive Differential Evolution

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Three-dimensional gravity inversion based on sparse recovery iteration using approximate zero norm

Cited by 16 publications

References 37 publications

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

Gravity Inversion Method to Produce Compact and Sharp Images Using L0-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

lp Norm Smooth Inversion of Magnetic Anomaly Based on Improved Adaptive Differential Evolution

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Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria