Time-lapse gravity inversion with an active time constraint

Karaoulis, M.; Revil, A.; Minsley, Burke J.; Todesco, Micol; Zhang, J.; Werkema, D. Dale

doi:10.1093/gji/ggt408

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…The sensitivity of existing instrumentation (for instance, the CG-5 Autograv Gravity Meter) is approximately 1 μGal, which implies that very precise gravity measurements can be performed at small scales as well as in boreholes. These data can be inverted to recover the density distribution of the subsurface (e.g., Luo, 2010;Krahenbuhl and Li, 2012;Karaoulis et al, 2013). Various types of regularization and prior information can be added to the inverse problem, like for the resistivity problem, to obtain a density tomogram of the subsurface.…”

Section: Electric Resistivity and Gravity Inversionmentioning

confidence: 99%

“…A description of the forward algorithms used in this paper to model the gravity and resistivity distribution can be found in Zhou et al (2014) and Karaoulis et al (2013). The forward algorithms will be summarized below, and they have been benchmarked against an analytical solution to test the accuracy of the numerical techniques.…”

Section: Combining Petrophysical and Geologic Cross-section Informationmentioning

confidence: 99%

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2D joint inversion of geophysical data using petrophysical clustering and facies deformation

Zhang

Revil

2015

GEOPHYSICS

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Geologic expertise and petrophysical relationships can be brought together to provide prior information while inverting multiple geophysical data sets. The merging of such information can result in more realistic solution in the distribution of the model parameters. We have evaluated the geophysical inverse problem in terms of Gaussian random fields with mean functions controlled by petrophysical relationships and covariance functions controlled by a prior geologic cross section, including the definition of spatial boundaries for the geologic facies. The petrophysical relationship problem is formulated as a regression problem upon each facies. The inversion of the geophysical data is performed in a Bayesian framework. We have developed the usefulness of this strategy using a first synthetic case for which we have performed a joint inversion of gravity and galvanometric resistivity data with the stations located at the ground surface. The joint inversion was used to recover the density and resistivity distributions of the subsurface. In a second step, we considered the possibility that the facies boundaries were deformable and their shapes were inverted as well. We used the level-set approach to perform such deformation preserving a priori topological properties of the facies throughout the inversion. With the help of a priori facies petrophysical relationships and the topological characteristics of each facies, we made a posteriori inference about multiple geophysical tomograms based on their corresponding geophysical data misfits. We have applied this method to a second synthetic case showing that we can recover the heterogeneities inside the facies, the mean values for the petrophysical properties, and, to some extent, the facies boundaries using the 2D joint inversion of gravity and galvanometric resistivity data.

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Section: Electric Resistivity and Gravity Inversionmentioning

confidence: 99%

Section: Combining Petrophysical and Geologic Cross-section Informationmentioning

confidence: 99%

2D joint inversion of geophysical data using petrophysical clustering and facies deformation

Zhang

Revil

2015

GEOPHYSICS

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“…Interpretation of SP is particularly challenging due to the embedded non‐uniqueness present in all potential‐field methods (Pedersen ; Karaoulis et al . ). Traditional inversion coupled with regularizers can tackle non‐uniqueness (Tikhonov and Arsenin ) by constraining the solution and thus reducing the optimization space.…”

Section: Introductionmentioning

confidence: 97%

“…Recently, 4D inversion schemes, combined with additional regularizers have been applied to gravity data and numerical evidences shown promising results in time‐varying non‐static models (Karaoulis et al . ).…”

Section: Introductionmentioning

confidence: 97%

A hybrid optimization scheme for self‐potential measurements due to multiple sheet‐like bodies in arbitrary 2D resistivity distributions

Giannakis

Tsourlos

Papazachos

et al. 2019

Geophysical Prospecting

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Self‐potential is a passive geophysical method that can be applied in a straightforward manner with minimum requirements in the field. Nonetheless, interpretation of self‐potential data is particularly challenging due to the inherited non‐uniqueness present in all potential methods. Incorporating information regarding the target of interest can facilitate interpretation and increase the reliability of the final output. In the current paper, a novel method for detecting multiple sheet‐like targets is presented. A numerical framework is initially described that simulates sheet‐like bodies in an arbitrary 2D resistivity distribution. A scattered field formulation based on finite differences is employed that allows the edges of the sheet to be independent of the grid geometry. A novel analytical solution for two‐layered models is derived and subsequently used to validate the accuracy of the proposed numerical scheme. Lastly, a hybrid optimization is proposed that couples linear least‐squares with particle‐swarm optimization in order to effectively locate the edges of multiple sheet‐like bodies. Through numerical and real data, it is proven that the hybrid optimization overcomes local minimal that occurs in complex resistivity distributions and converges substantially faster compared to traditional particle‐swarm optimization.

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“…They showed that a simultaneous inversion of time-lapse ERT data gives a superior result compared to independent inversions, cascading inversions or differenced inversions. Their preferred technique was a special case of the 4D algorithm of Kim et al (2009), which has also successfully been applied to gravity (Karaoulis et al 2013a), induced polarisation (Karaoulis et al 2013b), and seismic tomography (Karaoulis et al 2015). This technique has not yet been presented for magnetotelluric inversion.…”

Section: Introductionmentioning

confidence: 99%

Time-lapse inversion of one-dimensional magnetotelluric data

Conway

Heinson

Rees

et al. 2018

Earth Planets Space

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We present a new tool for modelling time-lapse magnetotelluric (MT) data, an emerging technique for monitoring changes in subsurface electrical resistivity. Time-lapse MT data have been acquired in various settings, including sites of hydraulic fracturing, dewatering and sequestration. It has been shown in other geophysical techniques that the most effective way to model time-lapse data is with simultaneous inversion, which uses information from all timesteps to produce models with higher accuracy and fewer artefacts. We introduce this method to model time-lapse 1D MT data. As with a standard MT inversion, our routine penalises spatial roughness at each time-step, however we also introduce temporal regularisation. The inversion is simple to apply, requiring only the ratio between regularisation parameters and the desired level of misfit from the user. The algorithm is tested on both synthetic data, and a case study. We find that in the synthetic example our inversion successfully retrieves the main characteristics of the test model and introduces minimal artefacts, even in the presence of significant noise. We also test the effect of changing the ratio of regularisation parameters. In the case study, we produce an easily interpretable model that compares favourably with previous inversions of the synthetic data. We conclude that time-lapse modelling of 1D MT data can be a valuable tool for imaging subsurface change.

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Time-lapse gravity inversion with an active time constraint

Cited by 11 publications

References 31 publications

2D joint inversion of geophysical data using petrophysical clustering and facies deformation

2D joint inversion of geophysical data using petrophysical clustering and facies deformation

A hybrid optimization scheme for self‐potential measurements due to multiple sheet‐like bodies in arbitrary 2D resistivity distributions

Time-lapse inversion of one-dimensional magnetotelluric data

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