Inversion Procedure Research Articles

Moment tensors for small earthquakes and the stress regime in the mid-Atlantic United States

Focal mechanisms for small magnitude earthquakes (M ~ 1.3–4.1) in the mid-Atlantic region of the United States have been determined using a double-couple moment tensor inversion procedure. The 26 new focal mechanisms obtained, when combined with previously published mechanisms, show a pattern of reverse faulting in the easternmost portion of the study area and strike-slip faulting in the west, consistent with previous studies. The change in focal mechanisms from east to west helps to constrain the geographic location of the east-west transition in the stress regime to a NE-SW area within central Pennsylvania within proximity of the Allegheny Front. Stress inversions performed to constrain variations in the stress state across the region show that the maximum compressive stress varies only slightly, but that the near-vertical stress is the minimum compressive stress in the east and transitions to the intermediate compressive stress in the west, as expected for an east-west transition in reverse to strike-slip faulting. Analysis of driving forces causing the stress change suggests that tectonic terrane structure, glacial isostatic adjustment, and changes in gravitational potential energy have little effect on the stress field in this region, leaving the interaction of sublithospheric mantle flow with the eastern edge of the Laurentian cratonic lithosphere beneath central Pennsylvania as a primary explanation. The cratonic lithospheric keel may cause a deflection in mantle flow, thereby changing the stress field enough so that the magnitude of the vertical stress in relation to the minimum horizontal stress results in strike-slip as opposed to reverse faulting.

Guided wave tomography for quantitative thickness mapping using non-dispersive SH0 mode through geometrical full waveform inversion (GFWI)

Guided wave tomography plays a significantly important role in quantitatively mapping thickness variations in plate-like structures and pipelines in the petrochemical industry for accurate measurement of corrosion, as guided waves enable rapid screening of large areas without needing direct access. Several inverse algorithms have been implemented in guided wave tomography; however, almost all of them use a primary assumption: the three-dimensional (3D) guided wave thickness reconstruction is simplified as a two-dimensional (2D) acoustic wave velocity inversion, which is then mapped to thickness variation using the dispersive nature of guided waves. Although this assumption simplifies the inverse procedure, it makes it impossible to use non-dispersive modes in reconstructions. Geometrical full waveform inversion (GFWI) is promising to overcome this limitation since it can reconstruct corrosion profiles through geometry optimization using a data-fitting procedure, where velocity-to-thickness mapping is not needed. In this work, GFWI-based guided wave tomography is developed in plate-like structures, and is applied to reconstruct thickness maps in a series of corrosion defects using the non-dispersive SH0 mode, demonstrating high performance and achieving an improved reconstruction resolution.

Longitudinal fibre/matrix debonds in cfrp ultra-thin plies: T-t cyclic testing and numerical prediction

The edge effect phenomenon in cross-ply laminates made of ultra-thin plies produces a relevant stress component through the laminate thickness that could lead to the generation of longitudinal debonds (parallel to the loading direction) in the 90° ply block. In this study, specimens from an ultrathin carbon-epoxy laminate are inspected after the curing process in order to find longitudinal debonds. The debonds growth is also monitored during a later T-T cyclic testing campaign. The experimental results are compared with the predictions obtained from a BEM model showing a good agreement. Finally, the use of an inverse procedure combining both experimental and numerical analyses leads to the estimation of GIc of the fibre/matrix interface.

Identification of position, size and dynamic heat power of chips embedded in a real electronic board

Temperature is a critical factor in the lifespan of electronic systems. Therefore, it is essential to control all thermal parameters. In certain applications, such as commercial off-the-shelf (COTS) components, manufacturers may not have precise knowledge of the composition of electronic boards. As a result, it is important for them to define the positions of heat-dissipating elements, and the power dissipated to better predict the final thermal behavior of the system. Some electronic boards today even have components integrated into their volume. Since components can now be located not only on the surface but also within the volume of electronic boards, manufacturers need to define volume-based methods for detecting these heat sources. In this article, we address the problem of detecting sources within a volume using surface temperature data. To achieve this goal, we develop a representative numerical model of the system that can be used for an inverse source identification procedure. In a first time, we present a numerical approach that demonstrates the feasibility of the identification procedure on a board containing several electronic chips. Secondly, the volume source identification method is applied to a real case, using surfaces temperatures measured by infrared cameras. The experimental results obtained are consistent and show that this technique is suitable for detecting volumetric sources within an electronic board.

COFs functionalized self-cleaning loose nanofiltration membranes for efficient dye/salt separation

In spite of the fact that loose nanofiltration membrane (LNFM) technology has certified inspiring prospects in the remediation of dyestuff-comprising wastewater, omnipresent membrane fouling deteriorates its separation competence and hinders its practical application. Herein, a neoteric LNFM with finely tuned anti-fouling and self-cleaning properties was manufactured via the reformative phase inversion procedure integrated with the correctional interfacial polymerization process. By altering the casting solution (i.e., adding nano additives) and coagulation bath (i.e., applying melamine aqueous solution rather than pure water) recipes, the porphyrin-based covalent organic framework (COF-366) and melamine monomers were introduced into the polymeric matrix ingeniously in one step, participating the subsequent interfacial polymerization reaction straightly. The COF-366 with photocatalytic degradation performance and hydrophilicity bestows the resultant LNFM with superior decomposing capacity for organic micro-pollutants under visible light irradiation and better operation durability. Moreover, this substrate-confined amine diffusion combined with the high polarity of melamine enables the formation of a thinner nanofilm for easy mass transport. The best-performing LNFM invented in this work exhibited a high water permeance up to 65 L m−2 h−1 bar−1, excellent dye retention (95.7% against methyl blue), and salt/dye discrimination (27.1 for methyl blue and Na2SO4 selectivity) abilities. Eventually, the membrane could recover to original separation efficacy after the cyclic degradation testing. Overall, this work demonstrates an extensive perspective to pioneer an advanced self-cleaning membrane for dye/salt fractionation.

Gradient distribution of cations in rhabdophane La0.27Y0.73PO4·nH2O nanoparticles

The structure of gradient rhabdophane nanoparticles of variable composition (La,Y)PO4·nH2O obtained by precipitation is considered. According to the data of HAADF TEM and EDX mapping using the Abel inversion procedure, it was shown that there is an inhomogeneous distribution of yttrium and lanthanum atoms in the particles from the central part to the periphery. At the same time, the nominal composition of both individual nanoparticles and the entire sample meets the value specified during synthesis within the error of the method – La0.27Y0.73PO4·nH2O. According to SAXS data, it was determined that in the particles of (La,Y)PO4·nH2O, there is a redistribution of scattering densities, which is caused by an increase in the fraction of YPO4 in the peripheral region of the particles. The observed effect of the distribution of cations over the particle made it possible to determine the size of the gradient layer, which reaches 50 % of the total particle size.

Regionalized multiple-point statistical simulation for calibrating process-based geologic models to seismic data

Calibrating process-based geologic models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geologic model is used as a training image for statistical pattern recognition. First, we define a rock-physics model from the process-based geologic model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geologic pattern recognition and regeneration under seismic data constraints. The method differs from the conventional MPS method in several ways: (1) the training image is a process-based geologic model of the reservoir of interest, thus defined on the same grid of the reservoir model; (2) the training image is generally nonstationary but there is no need to partition the nonstationary training image into pseudostationary ones; (3) the geologic facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; (4) multiple-point statistics are based on Bayes’ law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; and (5) the method does not involve an iterative optimization procedure. Therefore, it also differs from the neural-network-based machine-learning approach, where the data conditioning is achieved through an iterative optimization procedure. These differences make our method advantageous for calibrating process-based geologic models. The two examples with synthetic data illustrate the effectiveness of the method. The first one is derived from a satellite image of a river delta, and the second one from the Stanford VI reservoir model.

Defect characterization from magnetic field leakage signals in petroleum and natural gas pipelines

Pipelines transport invaluable energy resources such as crude oil and natural gas over long distances. The integrity of the piping system in terms of safety of the process is then of high importance. However, pipes are prone by time to defects that may degrade their properties and lead to failures. In this paper, we study the effect of defect parameters on the magnetic field leakage captured by Hall sensors operating along the pipe. In fact, the obtained results show that the defect parameters influence directly the MFL amplitude and shape. For this reason, the inversion problem allowing us to reconstruct the defect from the MFL signals became fast and easier in comparison to the deterministic and probabilistic algorithm inversion procedure. However, the simplified system cannot describe the real defects and the three-dimensional numerical study became necessary. In tank floor inspection domain, as our recent published work, we have studied the performance of defect shape reconstruction from MFL array sensor imaging and depth estimation while using an iterative inversion method. Indeed, the first stage consists of determining the defect width and length from magnetic flux leakage mapping reconstructed from the recorded signals of the micro-integrated magnetic sensors. Then, after coupling Comsol and Matlab software, the defect depth is obtained by coupling the 3D finite elements method and a fast iterative algorithm recently developed. Consequently, the defect shape and size are obtained after a few iterations with high precision. Furthermore, this method of defect reconstruction and seizing can be extended for irregular defect shapes encountered in pipeline such as cracks and corrosion.

Results from the further development of CGI inversion on simulation and field data systems

AbstractA new series expansion-based method, the Combined Geoelectric Weighted Inversion (CGWI) procedure is presented and tested by using synthetic and in-field measured datasets. The method is an improved version of the Combined Geoelectric Inversion (CGI) robustified by involving Cauchy-Steiner weights in an Iteratively Reweighted Least Squares technique. The new procedure is compared to the Fourier series expansion-based 1.5D and the CGI methods as well as to the broadly applied RES2DINV inversion procedure. The field measurements are performed during stone exploration in an active quarry on the south-western slopes of the Mátra mountains, in northern Hungary. It is shown that the CGWI method gives stable and robust parameter estimation with acceptable accuracy. The comparison with other inversion methods is based on data distances, estimation errors and correlation parameters calculated on the base of the parameter correlation matrix.

Comprehensive evaluation of rockburst risk by multiparameter characteristics based on microseismic signals: A case study

AbstractNowadays, the seismological monitoring system in China is a valuable tool in the rockburst risk evaluation for deep coal mines. In the past, only parameters, like source location and energy, are widely used to estimate the risk level of rockburst. Sometimes, it is effective; however, some other important physical parameters, such as apparent stress drop, static stress drop, P‐wave velocity, and moment tensor, should also be included in order to improve the accuracy of risk assessment. In this study, these parameters are calculated using mine tremor signals recorded in the LW35001 workface of Liangbaosi Coal Mine. These calculations provide an overall identification of periodical stress distribution and rock mass energy‐releasing type under high concentrated stress. Via linear moment tensor inversion procedure, the source mechanism of mine tremors and stress state of the rock mass is determined whether it is risk or not to underground roadway. This comprehensive analysis provides a specific guidance for rockburst prevention for coal mine management, that is, knowing when and where measures must be taken to decrease the risk level or induce the occurrence of rockburst under control.

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

Assessment of Hydrocarbon Contamination Using 2D Resistivity Imagining Method and Geochemical Analysis in Ibadan, Southwestern Nigeria

The study examined the use of 2D resistivity imaging survey and geochemical analysis to map and assess the level of soil contamination due to hydrocarbon spillage in Araromi Auto Spare Parts Market Ibadan, Southwestern Nigeria. The objectives are to provide 2D inverted resistivity sections beneath the study area, delineate anomalously low resistivity zones indicative of long-term hydrocarbon contamination in the sections, and determine concentrations of Polycyclic Aromatic Hydrocarbons (PAHs) in the suspected soils. Five traverses were run using Wenner array with electrode spacing varied from 5 m to 15 m and station increment of 5 m. The resistivity data were processed by using 2D inversion procedure to generate the 2D inverted resistivity sections. Geochemical analysis was conducted on five soil samples collected at 1 m depth in the low resistivity zones delineated on the 2D inversion sections, to determine the concentration of Polycyclic Aromatic Hydrocarbons (PAHs) to assess the level of contamination. The 2D inverted sections delineated low resistivity anomalies, ranging from 1.25 Ωm to 5.54 Ωm, characteristic of mature biodegraded hydrocarbon contamination. The results of geochemical analysis revealed total PAH concentration of 1390 µg/kg to 16140 µg/kg suggesting that the study area is heavily contaminated by the used motor oil. The combined use of 2D resistivity imaging and geochemical analysis has shown that soils beneath the study area has been heavily contaminated by the used motor oil, and may constitute potential health risk to the soil and groundwater systems in its vicinity. The results are expected to serve as useful guide in planning remediation programme.

THEPORE: A software package for modeling THErmo-PORo-elastic displacements

THEPORE (THErmo-POro-Elastic solutions) is an open source software to perform forward and inverse modeling of ground displacements induced by thermo-poro-elastic sources. The software, implemented in MATLAB, offers a library of analytical and semi-analytical solutions to compute ground displacements induced by thermo-poro-elastic deformation sources of different geometries, embedded in an elastic, homogeneous and isotropic half-space. The solutions have been verified against finite-element simulations. THEPORE includes also an inversion procedure of the deformation data to constrain the source parameters that better fit the observed signals.The software's functionality is showcased by inverting the GPS deformation data recorded on Vulcano Island at the onset of the 2021 unrest, in order to estimate the position and volume change of the source responsible for the observed deformations. The results encourage to consider THEPORE as a practical tool suitable for a fast preliminary estimation of the deformation source during a volcanic crisis.

Geoelectrical Evaluation of Contamination in and around Ori-Ile Battery Wastes Dumpsite in Ibadan, Southwestern Nigeria

Geoelectrical surveys involving vertical electrical sounding (VES) and 2D resistivity imaging were integrated to assess subsurface contamination by heavy metals in and around Orile Battery Waste Dumpsite, Kumapayi in Ibadan, Southwestern Nigeria. The objectives are to determine the lateral and vertical variations of resistivity/conductivity in and around the dumpsite, determine the nature and thickness of the overburden, determine the depth to bedrock, and delineate subsurface zones of anomalously high conductivity/low resistivity which may have resulted from contamination by heavy metals. Thirty-three VES stations were occupied using the Schlumberger electrode array with electrode spacing (AB/2) varied from 1 m to 100 m. The 2D resistivity survey was carried out along six East–West trending traverses established across and around the dumpsite using the Dipole-Dipole electrode array with station spacing of 10 m and expansion factor, n=1-5. The VES data were quantitatively interpreted using initial partial curve matching technique and 1D forward modelling while the dipole-dipole data were interpreted using 2D inversion procedures. The geoelctric sections delineated three layers comprising topsoil with layer resistivity ranging from 18 Ωm to 264 Ωm and thickness varying from 0.4 m to 1.4 m, weathered layer with resistivity and thickness ranging from 1 Ωm to 219 Ωm and 0.6 m to 9.4 m respectively, and weathered/fractured/fresh bedrock with resistivity ranging from 132 Ωm and 6500 Ωm. The 2D inverted resistivity sections revealed anomalous resistivity lows suggesting contamination at depths ranging from 5 m to 10 m beneath the traverses. The contamination zones are characterized by resistivity values less than 10 Ωm. The study revealed that the soil beneath the study area has been contaminated by the battery wastes. The suspected fractures and relatively shallow water table observed in the study area may have predisposed the groundwater to contamination which could constitute serious health risk to the inhabitants. It is recommended that geochemical analysis for heavy metals be conducted on the soil and groundwater from wells in the study area to assess the level of contamination.

Estimation of temperature-dependent piezoelectric material parameters using ring-shaped specimens

Abstract Simulation-driven design processes of piezoelectric actuators and sensors necessitate precise material parameter knowledge. Historically, the IEEE standard on piezoelectricity is used for this purpose, which proposes an analytical method for material parameter identification based on resonance frequency evaluations. This method requires different impedance measurements on differently shaped and processed samples, which yields inconsistent material parameter sets due to processing effects on material properties. Previous studies show that an inverse procedure using a single disc-shaped specimen with some adaptations can be used to identify a full set of parameters. As high-power actuators often use ring-shaped components, this method needs adaptation for this kind of specimen. Furthermore, the dependence of the material parameters on temperature needs to be considered to characterise the temperature effects and the associated non-linear effects of the specimens. For this purpose, samples with different geometrical dimensions at a range of different temperatures are examined in this study. Material parameters are then estimated using an inverse approach utilising the measurement data in conjunction with a numerical FEM simulation model. The observed temperature dependence of the estimated material parameters is examined and presented in this contribution.

Full-waveform ultrasonic imaging for bone

Abstract This paper suggests a full-waveform ultrasonic imaging strategy for bone. A novel forward wavefield propagation is performed in the frequency-domain. To improve the efficiency of full-waveform inversion bone imaging, all relative computations are also conducted in the frequency-domain. Additionally, a conjugate gradient algorithm is employed in the inversion procedure to accelerate the reconstruction of the bone acoustic velocity. Eventually, numerical experiments on different bone models are carried out to demonstrate the effectiveness of explored imaging strategy.

Experimental and analytical investigations on debonding response of embedded through-section ribbed GFRP bar-to-concrete joints at elevated temperatures

Embedded Through-Section (ETS) technique has gained a great deal of increasing interest in the shear strengthening of existing reinforced concrete members, which is based on using fibre-reinforced polymer (FRP) bars installed through predrilled holes into concrete via adhesive. One of the key issues associated with the bonding of ETS FRP bars to concrete is the debonding of FRP bar-to-concrete interfaces which leads to premature and brittle failure of strengthened members, particularly under elevated temperatures. However, little research has been conducted on the influence of elevated temperatures on the bond behavior between ETS FRP bars and concrete. This paper presents experimental and analytical evaluations of the bond response of ETS-ribbed glass FRP (GFRP) bar-to-concrete joints subjected to elevated temperatures. A total of 90 pullout specimens were tested to quantify the influences of the temperature, bar diameter, and concrete strength on the failure mode, load response and bond strength. An analytical model to reproduce the full-range debonding process of ETS-GFRP bar-to-concrete interfaces was presented, which allowed simultaneously considering both long and short embedded lengths, interfacial friction, and snap-back behavior. The load-slip response, stress field, and peak load were solved analytically, resulting in a closed-form solution for the critical length for snap-back and a non-closed-form solution for the effective embedment length. After an inverse analysis procedure was proposed for determining the bond parameters, the accuracy of the analytical solution was verified through comparisons with the self-conducted test results and existing experimental data. A new temperature-dependent bond strength model was also developed. The findings revealed a remarkable decline in the bond strength of up to 92.3 % as the temperature increased from 20 °C to 160 °C, with about 60–70 % of this degradation occurring close to the glass transition temperature (Tg) of the adhesive. At temperatures well above Tg, the bond strength decreased with the bar diameter and displayed insignificant improvement with higher concrete strength. The results also demonstrated that the proposed analytical approach and bond strength model can predict the bond response of ETS ribbed GFRP bars embedded in concrete with reasonable accuracy.

Decoding disorder signatures of AuCl3and vacancies in MoS2films: from synthetic to experimental inversion.

This study investigates the scope of application of a recently designed inversion methodology that is capable of obtaining structural information about disordered systems through the analysis of their conductivity response signals. Here we demonstrate that inversion tools of this type are capable of sensing the presence of disorderly distributed defects and impurities even in the case where the scattering properties of the device are only weakly affected. This is done by inverting the DC conductivity response of monolayered MoS2films containing a minute amount of AuCl3coordinated complexes. Remarkably, we have successfully extracted detailed information about the concentration of AuCl3by decoding its signatures on the transport features of simulated devices. In addition to the case of theoretically-generated Hamiltonians, we have also carried out a full inversion procedure from experimentally measured signals of similar structures. Based on experimental input signals of MoS2with naturally occurring vacancies, we were able to quantify the vacancy concentration contained in the samples, which indicates that the inversion methodology has experimental applicability as long as the input signal is able to resolve the characteristic contributions of the type of disorder in question. Being able to handle more complex, realistic scenarios unlocks the method's applicability for designing and engineering even more elaborate materials.

Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction

In the field of groundwater, accurate delineation of contaminant plumes is critical for designing effective remediation strategies. Typically, this identification poses a challenge as it involves solving an inverse problem with limited concentration data available. To improve the understanding of contaminant behavior within aquifers, hydrogeophysics emerges as a powerful tool by enabling the combination of non-invasive geophysical techniques (i.e., electrical resistivity tomography—ERT) and hydrological variables. This paper investigates the potential of the Ensemble Smoother with Multiple Data Assimilation method to address the inverse problem at hand by simultaneously assimilating observed ERT data and scattered concentration values from monitoring wells. A novelty aspect is the integration of a Convolutional Neural Network (CNN) to replace and expedite the expensive geophysical forward model. The proposed approach is applied to a synthetic case study, simulating a tracer test in an unconfined aquifer. Five scenarios are compared, allowing to explore the effects of combining multiple data sources and their abundance. The outcomes highlight the efficacy of the proposed approach in estimating the spatial distribution of a concentration plume. Notably, the scenario integrating apparent resistivity with concentration values emerges as the most promising, as long as there are enough concentration data. This underlines the importance of adopting a comprehensive approach to tracer plume mapping by leveraging different types of information. Additionally, a comparison was conducted between the inverse procedure solved using the full geophysical forward model and the CNN model, showcasing comparable performance in terms of results, but with a significant acceleration in computational time.

Seismic source analysis and directivity of the November 2021 Fin doublet earthquake in southern Iran: challenges and findings

Studying the source characteristics of doublet or multiple earthquake sequences presents significant challenges in seismology, especially with short time intervals between events. On November 14, 2021, a doublet earthquake (Mw 6.0 and Mw 6.1) occurred near Fin city, southern Iran, within a span of less than two minutes and 10 km apart. We employed the Kinematic Waveform Inversion (KIWI) procedure to determine the point and extended source parameters of these events, using a multistep inversion approach for stable solutions. Our analysis highlighted the directivity of the earthquakes: the first event exhibited bilateral directivity, causing a rupture area that reached the surface, while the second event showed unilateral westward directivity, supported by waveform amplitude differences observed at various stations. This directivity analysis plays an essential role in seismic hazard studies. Our findings regarding the source parameters of these recent doublet earthquakes in the Fin region align well with regional geological trends and fault patterns. However, retrieving the main fault plane for the second earthquake was challenging due to the complexities of the waveform. Moment tensor decomposition revealed significant non-double-couple components for the second event, indicating the complexity inherent in analyzing doublet events. This study underscores the critical role of precise waveform analysis and robust inversion techniques in understanding complex seismic events.