Distribution Of Elastic Properties Research Articles

Nondestructively identifying the mechanical behavior of soft tissues using surface deformation with an explicit inverse approach

Identifying the spatial variation of stiffness properties in soft tissues nondestructively, with limited surface measurements, poses significant challenges. In this paper, we present a novel explicit inverse approach designed to characterize the nonhomogeneous elastic property distribution of soft tissues using only surface displacement datasets. In contrast to the prevalent implicit inverse approach, which focuses on optimizing the elastic properties of individual pixels, our proposed method optimizes the geometric parameters of deformable and movable components, as well as shear moduli of each component. As a result, the proposed approach requires far fewer optimization variables, streamlining the process. Numerical tests conducted in this study demonstrate the superiority of the explicit inverse method over the implicit inverse method, providing much-improved reconstructed results. In particular for a ring structure, while the average relative error of reconstruction using the implicit inverse method can exceed 40 %, the explicit inverse method achieves a remarkable average relative error of only 5 %. Given that surface displacements are easily measurable, the integration of our proposed explicit inverse method with low-cost imaging techniques shows great potential in accurately mapping the elastic property distribution of biological tissues.

Optimization study of finite element simulation based on multi-layer structure and anisotropic CFRP

Introduction: Current research has mostly simplified CFRP components as isotropic media or homogenized them as anisotropic media, which fails to present the actual acoustic propagation behavior inside the CFRP components and restricts the accuracy of decoupling sound line tracking results and defect feature information.Method: Based on clarifying the influence of fiber direction on the spatial distribution of CFRP elastic properties, this paper proposes an acoustic modeling method for CFRP multi-directional plates considering the coupling effect of multi-layer structure and elastic anisotropy.Results: There are two main innovations: 1. The model is optimized using the Voigt representation method, which can quantitatively characterize the elastic anisotropy of any homogeneous material with a 6 × 6 elastic stiffness matrix containing 21 independent elastic constants. In this paper, the elastic stiffness matrix only includes five independent elastic constants; 2. Finite element simulation is used to analyze the propagation rules of ultrasonic waves inside CFRP and compare the detection methods with or without sound coupling layer.Discussion: The acoustic characteristics of the sound coupling layer are changed to find the optimal coupling method, reduce the refraction of ultrasonic waves in the specimen layer, and make the sound line path more accurate.

Cell nucleus elastography with the adjoint-based inverse solver

Background and objectivesThe mechanics of the nucleus depends on cellular structures and architecture, and impact a number of diseases. Nuclear mechanics is yet rather complex due to heterogeneous distribution of dense heterochromatin and loose euchromatin domains, giving rise to spatially variable stiffness properties. MethodsIn this study, we propose to use the adjoint-based inverse solver to identify for the first time the nonhomogeneous elastic property distribution of the nucleus. Inputs of the inverse solver are deformation fields measured with microscopic imaging in contracting cardiomyocytes. ResultsThe feasibility of the proposed method is first demonstrated using simulated data. Results indicate accurate identification of the assumed heterochromatin region, with a maximum relative error of less than 5%. We also investigate the influence of unknown Poisson's ratio on the reconstruction and find that variations of the Poisson's ratio in the range [0.3–0.5] result in uncertainties of less than 15% in the identified stiffness. Finally, we apply the inverse solver on actual deformation fields acquired within the nuclei of two cardiomyocytes. The obtained results are in good agreement with the density maps obtained from microscopy images. ConclusionsOverall, the proposed approach shows great potential for nuclear elastography, with promising value for emerging fields of mechanobiology and mechanogenetics.

Characterization of channelized turbiditic reservoirs using elastic properties modeling: An application in the Sergipe-Alagoas basin, Brazil

Sandy channelized reservoirs in deep water environments are significant for oil exploration and production. The 3D nature of the outcrops described in the literature and the local limitation of the information acquired through drilled wells are limiting factors for the reliable subsurface characterization of this type of sedimentary environment and its related heterogeneities. Aiming to overcome the information absence between the sampled well points in hydrocarbon reservoir studies, the 3D seismic data is a conventional approach adopted. However, due to the intrinsically low seismic resolution aspect, the usage of inverted seismic volumes enhances the confidence rock properties spatial distribution into the geological models, even though the uncertainties are not eliminated. This study presents a methodology of synthetic elastic properties modeling in association with measured physical properties, velocity, density and thickness to assess and optimize the geostatistical built model using real turbiditic reservoirs data of a Maastrichtian section of Calumbi Formation, in the Sergipe sub-basin, Brazil. It was successively tested geomorphological and geological features, as described in the previous work as well as similar Alagoas Sub-basin outcrops, associating their responses in terms of compressional and shear velocities, densities, and thicknesses, reducing uncertainties in the spatial distribution of them, which are present in high vertical-resolution well data. The obtained results show that compressional and shear waves variations are the main influences for the seismic signal and the related attributes rather than the intrinsically geometrical features. It was built a specific equation intending to model the effects of these attributes over the measured seismic data to identify paths to implement needed corrections in the geometric and faciological characteristics of the evaluated reservoir-rocks. They confirm the importance for better understanding of subsurface geological characterization using the seismic data as conditioning, even below the intrinsically data resolution, especially when dealing with seismic inversion. Additionally, the 1D synthetic model acts to do deal with tuning effect and to control the elastic property distribution avoiding misinterpretations that may suggest unfavorable portions for new well locations, enhancing the hydrocarbon production because it provides feasible elements for project decision-making.

Effects of acute cervical stretching on arterial wall elastic properties.

Purpose: Acute (immediate) or regular (mid- or long-term) stretching increases arterial compliance and reduces arterial stiffness. Stretching is widely known to induce arterial functional factor changes, but it is unclear whether stretching alters arterial structural factors. Ultrasound shear wave elastography can quantify the distribution of tissue elastic properties as an index of arterial structural factors. This study thus aimed to examine the effects of acute cervical stretching on arterial wall tissue elastic properties. Methods: Seventeen healthy young adults participated in two different trials for 15min in random order on separate days: a resting and sitting trial (CON) and a supervised cervical stretching trial (CS). In CS, subjects performed 10 different stretches. At each site, the stretch was held for 30s followed by a 10-s relaxation period. In CON, subjects rested on a chair for 15min. Results: After the experiment, carotid arterial compliance, assessed by combined ultrasound imaging and applanation tonometry, was significantly increased in CS, but not in CON. However, there was no significant change in tissue elasticity properties of the arterial wall in either trial, as assessed by ultrasound shear wave elastography. Conclusion: Acute cervical stretching significantly increased carotid artery compliance in young participants, but did not reduce elastic tissue properties (i.e., arterial structural factors) of the carotid artery wall. These results strongly suggest that changes in structural factors have little relation to stretching-induced acute increases in arterial compliance.

Through-thickness distribution of bamboo tensile strength parallel to fibres

A tensile test program of Moso bamboo samples was carried out in order to obtain information on the radial distribution of elastic and strength properties. In accordance with previous studies, the elastic modulus and the tensile strength were found to increase outwards in the radial direction. This spatial dependence was compared with the distribution of the fibre content of the layers and found excellent correlation suggesting that the fibres are the main load bearing components. The fibre content was quantified using regression analysis in the radial direction as 0.1 (inner layer), 0.19 (middle-1 layer), 0.28 (middle-2 layer) and 0.44 (outer layer). The Rule of Mixture theory allowed the estimation of the elastic modulus and the tensile strength of the main individual components, the fibres and the parenchyma matrix.Article HighlightsRadial distribution of bamboo fibres across the culm wall and stiffness properties obtained from tensile test parallel to fibres were compared.The radial distribution of bamboo fibres was analysed in layers using image analysis software.The tensile failure mechanisms of bamboo samples were identified and found to depend on fibre content.

Stochastic inversion combining seismic data, facies properties, and advanced multiple-point geostatistics

We proposed a novel seismic inversion approach that integrates the physical properties of litho-facies, and geophysical data, within a multiple-point geostatistical framework to reduce the uncertainty in predictions of litho-facies spatial arrangement away from wells or control points. The litho-facies groups (rock-type) in the well locations are defined and conditioned to the distribution of elastic properties, including the values of P-wave velocity (Vp) and facies density (ρ) within the borehole. A conceptual geological model (training image) is utilized within a wavelet-based multiple-point geostatistical simulation (WAVESIM) algorithm to generate litho-facies realizations. In our inversion algorithm, the forward model is created by implementing the bivariate Kernel density estimation (KDE) technique of the litho-facies properties (Vp and ρ) that are distributed within the well locations. We utilize an iterative inversion framework, where a particular number of elastic properties (Vp and ρ) for each WAVESIM realization are drawn. For each generated set of the WAVESIM realizations, we simulate reflectivity series by KDE that are convolved with the seismic wavelet to create synthetic seismograms. Then, using a normalized, cross-correlation function we select the realization that provides the best-match between synthetic seismogram and the input seismic data. Our inversion technique is successfully applied to synthetic and field datasets. Our results demonstrate the efficiency of our inversion to characterize highly heterogeneous reservoirs in a reasonable computational time and control the connectivity between the channels.

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

ABSTRACTWe present a methodology for seismic inversion that generates high‐resolution models of facies and elastic properties from pre‐stack data. Our inversion algorithm uses a transdimensional approach where, in addition to the layer properties, the number of layers is treated as unknown. In other words, the data itself determine the correct model parameterization, that is, the number of layers. The reversible jump Markov Chain Monte Carlo method is an effective tool to solve such transdimensional problems as it generates models of reservoir properties along with uncertainty estimates. However, current implementations of the reversible jump Markov Chain Monte Carlo algorithms do not account for the non‐Gaussian and multimodal nature of model parameters. The target elastic reservoir properties generally have multimodal and non‐parametric distribution at each location of the model. The number of modes is equal to the number of facies. Taking these factors into account, we extend the reversible jump Markov Chain Monte Carlo algorithm to simultaneously invert for discrete facies and continuous elastic reservoir properties. The proposed extension to the algorithm iteratively samples the facies, by moving from one mode to another, and elastic properties by sampling within the same mode. The integration of facies classification within the inversion reduces non‐uniqueness, improves convergence speed and produces geologically consistent results. The workflow uses machine learning to generate probabilistic priors for the model parameters. We validate our approach by applying it to a synthetic dataset generated from a well log with two facies and then to a complex synthetic two‐dimensional model involving three facies having overlapping elastic property distribution. Finally, we apply our algorithm to a field dataset acquired over an unconventional reservoir. Our algorithm demonstrates the usefulness of incorporating facies information in seismic inversion and also the feasibility of inverting for facies from seismic data.

Full-Waveform Inversion of Fiber-Optic VSP Data From Deviated Wells

Distributed acoustic sensing (DAS) technology permits measuring the strain or strain rate along a fiber deployed in a well due to seismic wave propagation. In vertical seismic profiling (VSP) acquisitions where seismic waves are excited at the surface and measured by subsurface receivers in the well, the DAS serves as a fast alternative to a conventional geophone array. Like conventional multicomponent VSP velocity measurements, DAS VSP data contain information about the subsurface properties that are encoded in seismic events and attributes such as compressional and shear direct arrivals, reflections, converted modes, multiples, and amplitude and phase variations. These seismic data can be used to obtain elastic properties of the formation and characterize the reservoir. Full-waveform inversion (FWI) extracts the elastic properties of the formation from the seismic wavefield by minimizing the misfit between real measurements and synthetically modeled wavefields. The synthetically generated wavefields are obtained by solving partial differential equations that accurately model the physical phenomena of the real waveforms. For conventional geophone sensors, FWI is formulated for the particle velocity field. In this paper, we present a formulation of the modeling and inversion specifically for DAS measurements as an averaged strain along the fiber. The algorithm does not require converting the data to velocities. The gradient of the misfit function, by adjoint formulation, is a cross correlation in time of the forward-propagated wavefield and backward-propagated residuals injected from the receivers. For conventional sensors, the residuals are injected as force terms, but for DAS data, the residuals are averaged in space first and then injected as moment tensor sources with radiation patterns determined by the well deviation. For multi-offset in-plane and out-of-plane VSP acquisitions, a two-dimensional (2D) algorithm has been developed that inverts for a 2D distribution of elastic medium properties and includes three-dimensional (3D) well deviation effects. This paper will present the results of applying the inversion to real multi-offset, highly out-of-plane VSP fiber data acquired in a deviated well. Simulations confirm that the well trajectory has a considerable impact on the data amplitude and must be included in the modeling and inversion to reproduce amplitude variations. The inverted compressional and shear velocities agree well with the reference model based on sonic data. Another real data example is a multi-offset walk-above acquisition where a targeted domain is located below a deviated portion of the well. The inversion matches reflections and produces an image in the target area.

FEniCS implementation of the Virtual Fields Method (VFM) for nonhomogeneous hyperelastic identification

It is of great significance to identify the nonhomogeneous distribution of material properties in human tissues for different clinical and medical applications. This leads to the requirement of solving an inverse problem in elasticity. The Virtual Fields Method (VFM) is a rather recent inverse method with remarkable computational efficiency compared with the optimization-based methods. In this study, we aim to identify nonhomogeneous hyperelastic material properties using the VFM. We propose two novel algorithms, RE-VFM and NO-VFM. In RE-VFM, the solid is partitioned in different regions and the elastic properties of each region are determined. In NO-VFM, the distribution of elastic properties is completely reconstructed through the inverse problem without partitioning the solid. As the VFM requires to use virtual fields, we proposed an efficient way to construct them and implemented the approach in the FEniCS package. We validated the proposed methods on several examples, including a bilayer structure, a lamina cribosa (LC) model and a cube model embedded with a spherical inclusion. The numerical examples illustrate the feasibility of both RE-VFM and NO-VFM. Notably, the spatial variations of the Young's modulus distribution can be recovered accurately within only 5 iterations. The obtained results reveal the potential of the proposed methods for future clinical applications such as estimating the risk of vision loss related to glaucoma and detecting tumors.

Experimental and numerical investigation of strain inhomogeneity in zirconia during a Brazilian test

Zirconia-based ceramics is a material with a wide variety of applications. Problems of strength of such ceramics, being prepared by different methods, under different loading conditions, and with different porosity, are of practical importance. Inhomogeneous strain distributions in different-porosity zirconia ceramics during a Brazilian test are studied using the digital image correlation and numerical simulation techniques. Spatiotemporal patterns of strain localization across the compression axis are obtained. It is found that strain components are nonuniformly distributed over the specimen surface, and zirconia experiences macroscopically localized deformation. The fracture patterns reveal the triple-cleft mode. Numerical simulations performed both for a homogeneous material and with consideration for the material inhomogeneity show that different models yield different patterns of inhomogeneous strain distribution under diametral compression. Account for internal friction and dilatancy provides eight-shaped cracks. Random distributions of strength and elastic properties over the computational domain built with the use of pseudo-random number generators and/or Gaussian random fields allow one to control the size of strain inhomogeneities.

Optical coherence elastography of 3D bilayer soft solids using full-field and partial displacement measurements

Characterizing nonhomogeneous elastic property distribution of soft tissues plays a crucial role in disease diagnosis and treatment. In this paper, we will apply the optical coherence elastography to reconstruct the shear modulus elastic property distribution of a bilayer solid. In the computational aspect, we adopt a well-established inverse technique that solves for every nodal shear modulus in the problem domain (NO method). Additionally, we also propose a novel inverse method that assumes the shear moduli merely vary along the thickness of the bilayer solid (TO method). The inversion tests using simulated data demonstrate that TO method performs better in reconstructing the shear modulus distribution. Further, we utilize the experimental data obtained from the optical coherence tomography to reconstruct the shear modulus distribution of a bilayer phantom. We observe that the quality of the reconstructed shear modulus distribution obtained by the partial displacement measurement is better than that obtained by the full-field displacement measurement. Particularly, merely using the displacement component along the loading direction significantly improves the reconstructed results. This work is of great significance in applying optical coherence elastography (OCE) to characterize the elastic property distribution of layered soft tissues such as skins and corneas.

Evolution of co-seismic off-fault damage towards pulverisation

Pulverisation of rocks occurs through dynamic processes associated with earthquake ruptures. At the well-known pulverised rock outcrops along the San Andreas Fault (SAF), the intensity of pulverisation was shown to be highly heterogeneous on the outcrop (m–10s of m) scale, although detailed documentation of this heterogeneity is still lacking. Here we derive a standardized classification method for pulverisation intensity applicable to field observations of rocks that have been pulverised or dynamically shattered. We apply this classification to outcrops along the mature SAF and along a smaller displacement fault, the Borrego Fault (BF). Centimetre-scale mapping revealed damage heterogeneity at both locations, but while pulverisation intensity is heterogeneous at the SAF, it is mostly the meso-fracture damage that causes heterogeneity at the BF, although sporadic pulverisation is observed adjacent to the fault core. We postulate that the pattern of heterogeneity in pulverisation intensity along the SAF evolved from an originally patchy distribution of meso-fractures as observed on the BF. We develop a damage-evolution model, in which heterogeneous meso-fracture damage originating from damage focussed at subsidiary faults yields a heterogeneous distribution of elastic properties and therefore a heterogeneous strain-rate field of a future passing rupture. As pulverisation depends on strain rate, the heterogeneous strain-rate field is then responsible for the heterogeneous pulverisation intensity on the matured fault. This damage-evolution model integrates field observations on damage intensity and heterogeneity, results from laboratory experiments on pulverisation strain-rate thresholds, and theoretical considerations on the dynamic origin of pulverised fault zone rocks.

Numerical Algorithms for Simulation of a Fluid-Filed Fracture Evolution in a Poroelastic Medium

The paper deals with the computational framework for the numerical simulation of the three dimensional fluid-filled fracture evolution in a poroelastic medium. The model consists of several groups of equations including the Biot poroelastic model to describe a bulk medium behavior, Reynold’s lubrication equations to describe a flow inside fracture and corresponding bulk/fracture interface conditions. The geometric model of the fracture assumes that it is described as an arbitrary sufficiently smooth surface with a boundary. Main attention is paid to describing numerical algorithms for particular problems (poroelasticity, fracture fluid flow, fracture evolution) as well as an algorithm for the coupled problem solution. An implicit fracture mid-surface representation approach based on the closest point projection operator is a particular feature of the proposed algorithms. Such a representation is used to describe the fracture mid-surface in the poroelastic solver, Reynold’s lubrication equation solver and for simulation of fracture evolutions. The poroelastic solver is based on a special variant of X-FEM algorithms, which uses the closest point representation of the fracture. To solve Reynold’s lubrication equations, which model the fluid flow in fracture, a finite element version of the closet point projection method for PDEs surface is used. As a result, the algorithm for the coupled problem is purely Eulerian and uses the same finite element mesh to solve equations defined in the bulk and on the fracture mid-surface. Finally, we present results of the numerical simulations which demonstrate possibilities of the proposed numerical techniques, in particular, a problem in a media with a heterogeneous distribution of transport, elastic and toughness properties.

Model reduction in geostatistical seismic inversion with functional data analysis

In subsurface modeling and characterization, predicting the spatial distribution of subsurface elastic properties is commonly achieved by seismic inversion. Stochastic seismic inversion methods, such as iterative geostatistical seismic inversion (GSI), are widely applied to this end. Global iterative GSI methods are computationally expensive because they require, at a given iteration, the stochastic sequential simulation of the entire inversion grid at once multiple times. Functional data analysis (FDA) is a well-established statistical method suited to model long-term and noisy temporal series. This method allows us to summarize spatiotemporal series in a set of analytical functions with a low-dimension representation. FDA has been recently extended to problems related to geosciences, but its application to geophysics is still limited. We have developed the use of FDA as a model reduction technique during the model perturbation step in global iterative GSI. FDA is used to collapse the vertical dimension of the inversion grid. We illustrate our hybrid inversion method with its application to 3D synthetic and real data sets. The results indicate the ability of our inversion methodology to predict smooth inverted subsurface models that match the observed data at a similar convergence as obtained by a global iterative GSI, but with a considerable decrease in the computational cost. Although the resolution of the inverted models might not be enough for a detailed subsurface characterization, the inverted models can be used as a starting point of global iterative GSI to speed up the inversion or to test alternative geologic scenarios by changing the inversion parameterization and obtaining inverted models in a relatively short time.

SeReMpy: Seismic reservoir modeling Python library

Seismic reservoir characterization is a subfield of geophysics that combines seismic and rock-physics modeling with mathematical inverse theory to predict reservoir variables from the measured seismic data. An open-source comprehensive modeling library that includes the main concepts and tools is still missing. We have developed a Python library called SeReMpy with state-of-the-art of seismic reservoir modeling for reservoir properties characterization using seismic and rock-physics models and Bayesian inverse theory. The most innovative component of the library is the Bayesian seismic and rock-physics inversion to predict the spatial distribution of petrophysical and elastic properties from seismic data. The inversion algorithms include Bayesian analytical solutions of the linear-Gaussian inverse problem and Markov chain Monte Carlo (MCMC) numerical methods for nonlinear problems. The library includes four modules: geostatistics, rock physics, facies, and inversion, as well as several scripts with illustrative examples and applications. We illustrate the use of the functions of the module and develop codes for practical inversion problems using synthetic and real data. The applications include a rock-physics model for the prediction of elastic properties and facies using well-log data, a geostatistical simulation of continuous and discrete properties using well logs, a geostatistical interpolation and simulation of 2D maps of temperature, an elastic inversion of partial stacked seismograms with Bayesian linearized amplitude-variation-with-offset inversion, a rock-physics inversion of partial stacked seismograms with MCMC methods, and a 2D seismic inversion.

Metrological provision of measurement using NanoScan-3D microscopenanothercomer

A review of methods and instrumentation for the study of tribological and mechanical properties of the surface. It is established that the most common methods for studying these properties in the micro and nano-ranges are contact methods based on the interaction of the hard tip with the material under study. In scanning probe microscopy, a bimorph piezoceramic probe with a diamond tip is the main element of the measurement modules used for nanoindentation and sclerometry to comprehensively study the tribological and mechanical properties of the surface. The original design of the sensor allows to implement more than ten measuring techniques on one device. The operation of these devices in the semi-contact scanning probe-microscopic mode allows to obtain images of the surface relief and a map of the distribution of elastic properties. The indentation mode allows to measure hardness and modulus of elasticity, to estimate elastic
 recovery of material after indentation. The method allows to determine the abrasion resistance and hardness of the material, adhesion and thickness of thin coatings.

Thermal wave propagation in a functionally graded annular fin with fixed base

Thermal wave propagation and efficiency analysis of thin axially symmetric functionally graded annular fins with a fixed base boundary condition are examined. All material properties are graded along the radial direction using the Mori–Tanaka homogenization scheme to design a realistic and physically meaningful graded material model and create a smooth distribution of elastic and thermal material properties. The novelty of this research is handling a more realistic annular fin, which has sufficient strength in practice and significantly reduces weight, using two different metal materials with the elasticity-based Mori–Tanaka model. These conditions result in two-point linear boundary value problems that are numerically solved by the pseudospectral Chebyshev method. The temperature distributions, thermal stresses and the efficiency in the functionally graded annular fin are compared with fins made of steel only and aluminium only.

Introducing regularization into the virtual fields method (VFM) to identify nonhomogeneous elastic property distributions

The identification of nonhomogeneous elastic property distributions has been traditionally achieved with well acknowledged optimization based inverse approaches, but when full-field displacement measurements are available, the virtual fields method (VFM) can be computationally more efficient by converting the large-scale optimization problem into multiple small-scale optimization problems. A possible downside of the VFM so far was not to take into account prior knowledge, which is often available and needed when there is a very large number of unknowns and the inverse problem is ill-posed. In this work, different approaches are proposed for introducing regularization into the VFM, aiming to penalize the local variations of identified stiffness properties in order to reduce the effects of uncertainty in the inverse problem resolution. The feasibility and accuracy of the regularized VFM are tested through several numerical and experimental datasets. It is shown that the main advantage of the novel VFM approaches is the low computational cost, as large-scale inverse problems with 10,000 unknown parameters can be solved within several seconds using a standard personal computer. Although the regularized VFM can successfully detect a stiff inclusion in a soft solid with high accuracy, regularization also introduces unexpected spurious effects in the results, blurring the interface between soft and stiff regions. We also observed that the regularization did not improve the smoothness significantly due to local effects of the small-scale optimization problem introduced in the proposed VFM method. Therefore, traditional regularization, which penalizes local variations of identified stiffness properties, can be combined with the VFM to solve inverse problems with a high computational efficiency, but supplemental regularization conditions will need to be adapted in the future to better delineate soft-stiff interfaces with this methodology.

Low-Frequency Elastic Properties of a Polymineralic Carbonate: Laboratory Measurement and Digital Rock Physics

We demonstrate that the static elastic properties of a carbonate sample, comprised of dolomite and calcite, could be accurately predicted by Digital Rock Physics (DRP), a non-invasive testing method for simulating laboratory measurements. We present a state-of-the-art algorithm that uses X-ray Computed Tomography (CT) imagery to compute the elastic properties of a lacustrine rudstone sample. The high-resolution CT-images provide a digital sample that is used for analyzing microstructures and performing quasi-static compression numerical simulations. Here, we present the modified Segmentation-Less method withOut Targets method: a combination of segmentation-based and segmentation-less DRP. This new method assigns the spatial distribution of elastic properties of the sample based on homogenization theory and overcomes the monomineralic limitation of the previous work, allowing the algorithm to be used on polymineralic rocks. The method starts by partitioning CT-images of the sample into smaller sub-images, each of which contains only two phases: a mineral (calcite or dolomite) and air. Then, each sub-image is converted into elastic property arrays. Finally, the elastic property arrays from the sub-images are combined and fed into a finite element algorithm to compute the effective elastic properties of the sample. We compared the numerical results to the laboratory measurements of low-frequency elastic properties. We find that the Young’s moduli of both the dry and the fully saturated sample fall within 10% of the laboratory measurements. Our analysis also shows that segmentation-based DRP should be used cautiously to compute elastic properties of carbonate rocks similar to our sample.