3D stochastic joint inversion of gravity and magnetic data

Shamsipour, Pejman; Marcotte, Denis; Chouteau, Michel

doi:10.1016/j.jappgeo.2011.12.012

Cited by 41 publications

(15 citation statements)

References 18 publications

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“…Alternatively, when more external information is available, De Stefano et al (2011) offer the possibility of linking multiple domains during joint inversion using either structural constraints or empirical petrophysical laws. When probabilistic geological or petrophysical data are available, several authors developed approaches involving statistical tools that account for prior information (Shamsipour et al, 2012, Reid et al, 2013, McCalman et al, 2014, Lane and Guillen, 2005, Bosch, 2004, Jardani et al, 2011, Mahardika et al, 2012, Roberts et al, 2016, Gloaguen et al, 2004. In a similar fashion, Chen et al (2012) performs stochastic joint inversion to retrieve petrophysical properties.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

et al. 2017

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We introduce a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geological data. In this framework, probabilistic geological modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then utilizes petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-square framework to capture and integrate information related to geophysical, petrophysical and geological data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geological field data we have generated a set of geological models, which we used to derive a probabilistic geological model. In this synthetic case study, we show that the integration of geological information and petrophysical constraints in geophysical joint inversion can reduce uncertainty and improve imaging. In particular, the use of petrophysical constraints retrieves sharper boundaries and better reproduces the statistics of the observed petrophysical measurements. The integration of probabilistic geological modeling permits more accurate retrieval of model geometry, and better constrains the solution 2 while still satisfying the statistics derived from geological data. The analysis of statistical indicators at each step of the workflow shows that 1) the inversion methodology is effective when applied to complex geology, and 2) the integration of prior information and constraints from geology and petrophysics significantly improves the inversion results while decreasing uncertainty. Lastly, the analysis of uncertainty to the integration of the conditioned petrophysical constraints also shows that, for this example, the best results are obtained for joint inversion using petrophysical constraints spatially conditioned by geological modeling. * i-th geological model cell Conditioning of petrophysical constraints

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Section: Introductionmentioning

confidence: 99%

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

et al. 2017

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“…Another avenue of improvement would be the use of joint inversion of different types of signals in a unique estimation process (Zhdanov et al, 2012). This approach has been used with a certain degree of success to integrate different geophysical methods: DC resistivity and magnetotelluric (Vozoff & Jupp, 1975), gravity and magnetics (Shamsipour et al, 2012), gravity-gradient and borehole gravity (Meixia et al, 2015), and seismic and electromagnetic imaging (Hoversten et al, 2003). Joint inversion of GPR and ERT data sound especially promising for our problem (Doetsch et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Estimation of Specific Yield and its Variability by Electrical Resistivity Tomography

Dietrich

Carrera

Weinzettel

et al. 2018

Water Resources Research

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Specific yield (Sy) is defined as the average volume of water that can be drained, per unit surface of aquifer per unit drop of head. This parameter is of critical relevance for groundwater resources assessment, but its estimation suffers from numerous difficulties, including spatial variability, hysteresis, dependence on depth to water table, or delayed drainage. As a result, no widely accepted method is available for its estimation. Here we show that most of these difficulties can be overcome by using time‐lapse electrical resistivity tomography (ERT) to map water content variations in response to phreatic level fluctuations. We applied the method to a synthetic example and to a real site in Argentina. Results demonstrate that the approach is robust and provides a picture of how, how much and where water is being released from the soil when aquifer heads drop, which is the ultimate goal of Sy concept. ERT produced some oscillations in estimated water contents, which we attribute to inversion artifacts. Still, the error analysis and the synthetic example suggest that the impact of spurious oscillations tends to cancel out during estimation of Sy, which supports the robustness of the method. Estimated Sy is sensitive to the petrophysical law. If the slope of this law is inadequate, calculated values of Sy may be biased, but their variability patterns are well captured by the method. What is clear is that Sy is a spatially and temporally variable parameter, whose estimation is difficult. ERT represents a possible avenue to evaluate Sy and its variability.

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“…On the other hand, global search methods may provide an acceptable solution but are computationally time intensive. Several local and global inversion techniques have been developed to interpret gravity anomalies (Thanassoulas et al, 1987;Shamsipour et al, 2012;Montesinos et al, 2005;Qiu, 2009;Toushmalani, 2013). However, PSO has been successfully applied to many fields, such as model construction, biomedical images, electromagnetic optimization, hydrological problems, etc.…”

Section: Introductionmentioning

confidence: 99%

Inversion of residual gravity anomalies using tuned PSO

Roshan

Singh

2017

Geosci. Instrum. Method. Data Syst.

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Abstract. Many kinds of particle swarm optimization (PSO) techniques are now available and various efforts have been made to solve linear and non-linear problems as well as onedimensional and multi-dimensional problems of geophysical data. Particle swarm optimization is a metaheuristic optimization method that requires intelligent guesswork and a suitable selection of controlling parameters (i.e. inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique, tuned PSO, is an improved technique of PSO, in which efforts have been made to choose the controlling parameters, and these parameters have been selected after analysing the responses of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and validated using synthetic gravity anomalies over various source geometries. Finally, tuned PSO is applied over field residual gravity anomalies of two different geological terrains to find the model parameters, namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also that the strategy has enhanced the exploration capability of the proposed method. Thus tuned PSO is an efficient and more robust technique to achieve an optimal solution with minimal error.

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3D stochastic joint inversion of gravity and magnetic data

Cited by 41 publications

References 18 publications

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Estimation of Specific Yield and its Variability by Electrical Resistivity Tomography

Inversion of residual gravity anomalies using tuned PSO

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