Generalised Series Expansion (GSE) used in DC geoelectric–seismic joint inversion

Kis, M.

doi:10.1016/s0926-9851(02)00167-2

Cited by 16 publications

(7 citation statements)

References 12 publications

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“…Examples of joint inversion of different combinations of geophysical data with different common model parameters have been presented earlier by Vozoff and Jupp (1975), Schmutz et al (2000), Hertrich and Yaramanci (2002) and Christiansen et al (2004). Examples of joint inversion combining seismic and DC resistivity methods include: Underground vertical seismic profiling (VSP) and DC resistivity (Dobroka et al, 1991); Continuous VES (CVES) and seismic travel times (Gallardo and Meju, 2004), and; Refraction seismic and VES (Kis, 2002). In Hering et al (1995) basic ideas for a joint inversion algorithm for VES and surface wave (SW) seismic data are presented, and in Misiek et al (1997) two applications of this inversion algorithm are presented.…”

Section: Introductionmentioning

confidence: 98%

Laterally and Mutually Constrained Inversion of Surface Wave Seismic Data and Resistivity Data

Wisén

Christiansen

2005

JEEG

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The laterally and mutually constrained inversion (LCI and MCI) techniques allow for the combined inversion of multiple geophysical datasets and provide a sensitivity analysis of all model parameters. The LCI and MCI work with few-layered models, and are restricted to quasi-layered geological environments. LCI is used successfully for inversion of surface wave (SW) seismic data and MCI for combined inversion of SW data and continuous vertical electrical sounding (CVES) data. The primary model parameters are resistivity or shear wave velocity and thickness, and depth to layer interfaces is included as a secondary model parameter.The advantages and limitations of LCI and MCI are evaluated on synthetic SW data. The main conclusions are: Depth to a high velocity halfspace is generally well-resolved even if thicknesses of overlaying layers and the velocity of the halfspace are unresolved; Applying lateral constraints (LCI) between individual SW soundings improves model resolution, particularly for velocities and depths, and; Adding mutual constraints (MCI) to resistivity data improves model resolution of all parameters in the shear wave velocity model. When applied to field data, model resolution improves significantly when LCI or MCI is used, and resistivity and velocity models correlate structurally with better correlation to lithological interfaces identified in drill logs.

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Section: Introductionmentioning

confidence: 98%

Laterally and Mutually Constrained Inversion of Surface Wave Seismic Data and Resistivity Data

Wisén

Christiansen

2005

JEEG

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“…This geoelectric inversion algorithm is further developed for 3D applications by Gyulai and Tolnai (2012). A series expansion-based joint inversion method for the interpretation of DC geoelectric and seismic refraction data measured above 2D geologic structures is introduced by Kis (2002). In gravity modeling, the series expansion approach opened the door to reconstruct the 3D potential function and the full Eötvös-tensor from a large number of astronomical, torsional pendulum, and gravity data by solving a greatly overdetermined inverse problem (Dobróka and Völgyesi, 2008).…”

Section: Introductionmentioning

confidence: 99%

Interval inversion approach for an improved interpretation of well logs

Dobróka

Szabó

Tóth³

et al. 2016

GEOPHYSICS

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The quality analysis of well-logging inversion results has always been an important part of formation evaluation. The precise calculation of hydrocarbon reserves requires the most accurate possible estimation of porosity, water saturation, and shale and rock-matrix volumes. The local inversion method conventionally used to predict the above model parameters depth by depth represents a marginally overdetermined inverse problem, which is rather sensitive to the uncertainty of observed data and limited in estimation accuracy. To reduce the harmful effect of data noise on the estimated model, we have suggested the interval inversion method, in which an increase of the overdetermination ratio allows a more accurate solution of the well-logging inverse problem. The interval inversion method inverts the data set of a longer depth interval to predict the vertical distributions of petrophysical parameters in a joint inversion procedure. In formulating the forward problem, we have extended the validity of probe response functions to a greater depth interval assuming the petrophysical parameters are depth dependent, and then we expanded the model parameters into a series using the Legendre polynomials as basis functions for modeling inhomogeneous formations. We solved the inverse problem for a much smaller number of expansion coefficients than data to derive the petrophysical parameters in a stable overdetermined inversion procedure. The added advantage of the interval inversion method is that the layer thicknesses and suitably chosen zone parameters can be estimated automatically by the inversion procedure to refine the results of inverse and forward modeling. We have defined depth-dependent model covariance and correlation matrices to compare the quality of the local and interval inversion results. A detailed study using well logs measured from a Hungarian gas-bearing unconsolidated formation revealed that the greatly overdetermined interval inversion procedure can be effectively used in reducing the estimation errors in shaly sand formations, which may refine significantly the results of reserve calculation.

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“…Numerous inversion methods, many of which are described by Yeh [1986], have been developed to address these issues. Joint inversion methods allow for the incorporation of secondary information and have been employed extensively in the exploration and recovery of oil and natural gas to incorporate geophysical information [ Kis , 2002; Bosch , 2004] and to estimate the extent of subsurface contamination [ de la Vega et al , 2003]. More recently, universal function approximators have been used to perform inversion of groundwater modeling parameters [ Morshed and Kaluarachchi , 1998; Garcia and Shigidi , 2006].…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulation and spatial estimation with multiple data types using artificial neural networks

Besaw

Rizzo

2007

Water Resources Research

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[1] A novel data-driven artificial neural network (ANN) that quantitatively combines large numbers of multiple types of soft data is presented for performing stochastic simulation and/or spatial estimation. A counterpropagation ANN is extended with a radial basis function to estimate parameter fields that reproduce the spatial structure exhibited in autocorrelated parameters. Applications involve using three geophysical properties measured on a slab of Berea sandstone and the delineation of landfill leachate at a site in the Netherlands using electrical formation conductivity as our primary variable and six types of secondary data (e.g., hydrochemistry, archaea, and bacteria). The ANN estimation fields are statistically similar to geostatistical methods (indicator simulation and cokriging) and reference fields (when available). The method is a nonparametric clustering/classification algorithm that can assimilate significant amounts of disparate data types with both continuous and categorical responses without the computational burden associated with the construction of positive definite covariance and cross-covariance matrices. The combination of simplicity and computational speed makes the method ideally suited for environmental subsurface characterization and other Earth science applications with spatially autocorrelated variables.Citation: Besaw, L. E., and D. M. Rizzo (2007), Stochastic simulation and spatial estimation with multiple data types using artificial neural networks, Water Resour.

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Generalised Series Expansion (GSE) used in DC geoelectric–seismic joint inversion

Cited by 16 publications

References 12 publications

Laterally and Mutually Constrained Inversion of Surface Wave Seismic Data and Resistivity Data

Laterally and Mutually Constrained Inversion of Surface Wave Seismic Data and Resistivity Data

Interval inversion approach for an improved interpretation of well logs

Stochastic simulation and spatial estimation with multiple data types using artificial neural networks

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