Journal articles: 'Inversion identification'

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Corbett-Detig, Russell B., Iskander Said, Maria Calzetta, Max Genetti, Jakob McBroome, Nicholas W. Maurer, Vincenzo Petrarca, Alessandra della Torre, and Nora J. Besansky. "Fine-Mapping Complex Inversion Breakpoints and Investigating Somatic Pairing in the Anopheles gambiae Species Complex Using Proximity-Ligation Sequencing." Genetics 213, no. 4 (October 30, 2019): 1495–511. http://dx.doi.org/10.1534/genetics.119.302385.

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Chromosomal inversions are fundamental drivers of genome evolution. In the main Afrotropical malaria vector species, belonging to the Anopheles gambiae species complex, inversions play an important role in local adaptation and have a rich history of cytological study. Despite the importance and ubiquity of some chromosomal inversions across the species complex, inversion breakpoints are often challenging to map molecularly due to the presence of large repetitive regions. Here, we develop an approach that uses Hi-C sequencing data to molecularly fine-map the breakpoints of inversions. We demonstrate that this approach is robust and likely to be widely applicable for both identification and fine-mapping inversion breakpoints in species whose inversions have heretofore been challenging to characterize. We apply our method to interrogate the previously unknown inversion breakpoints of 2Rbc and 2Rd in An. coluzzii. We found that inversion breakpoints occur in large repetitive regions, and, strikingly, among three inversions analyzed, two breakpoints appear to be reused in two separate inversions. These breakpoint-adjacent regions are strongly enriched for the presence of a 30 bp satellite repeat sequence. Because low frequency inversion breakpoints are not correlated with genomic regions containing this satellite, we suggest that interrupting this particular repeat may result in arrangements with higher relative fitness. Additionally, we use heterozygous individuals to quantitatively investigate the impacts of somatic pairing in the regions immediately surrounding inversion breakpoints. Finally, we discuss important considerations for possible applications of this approach for inversion breakpoint identification in a range of organisms.

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TAROUDAKIS, MICHAEL I. "IDENTIFYING MODAL ARRIVALS IN SHALLOW WATER FOR BOTTOM GEOACOUSTIC INVERSIONS." Journal of Computational Acoustics 08, no. 02 (June 2000): 307–24. http://dx.doi.org/10.1142/s0218396x00000224.

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A mode identification process that can be applied to broadband acoustic transmissions in the ocean is presented. The process is associated with a modal travel time inversion scheme for geoacoustic or tomographic inversions. The process is based on the assumption that a reference (background) environment is known and the identification process is based on information on the group (modal) velocities of the reference environment only. Using two characteristic examples corresponding to shallow water environments it is shown that the identification process works well at least for the lower order modes.

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Shukla, Sanjay K., Jennifer Kislow, Adam Briska, John Henkhaus, and Colin Dykes. "Optical Mapping Reveals a Large Genetic Inversion between Two Methicillin-Resistant Staphylococcus aureus Strains." Journal of Bacteriology 191, no. 18 (June 19, 2009): 5717–23. http://dx.doi.org/10.1128/jb.00325-09.

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ABSTRACT Staphylococcus aureus is a highly versatile and evolving bacterium of great clinical importance. S. aureus can evolve by acquiring single nucleotide polymorphisms and mobile genetic elements and by recombination events. Identification and location of novel genomic elements in a bacterial genome are not straightforward, unless the whole genome is sequenced. Optical mapping is a new tool that creates a high-resolution, in situ ordered restriction map of a bacterial genome. These maps can be used to determine genomic organization and perform comparative genomics to identify genomic rearrangements, such as insertions, deletions, duplications, and inversions, compared to an in silico (virtual) restriction map of a known genome sequence. Using this technology, we report here the identification, approximate location, and characterization of a genetic inversion of ∼500 kb of a DNA element between the NRS387 (USA800) and FPR3757 (USA300) strains. The presence of the inversion and location of its junction sites were confirmed by site-specific PCR and sequencing. At both the left and right junction sites in NRS387, an IS1181 element and a 73-bp sequence were identified as inverted repeats, which could explain the possible mechanism of the inversion event.

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Day, Tanya K., Guoxin Zeng, Antony M. Hooker, Madhava Bhat, David R. Turner, and Pamela J. Sykes. "Extremely Low Doses of X-Radiation can Induce Adaptive Responses in Mouse Prostate." Dose-Response 5, no. 4 (October 1, 2007): dose—response.0. http://dx.doi.org/10.2203/dose-response.07-019.day.

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The pKZ1 mouse chromosomal inversion assay is the only assay that has detected modulation of a mutagenic endpoint after single whole body X-irradiation with doses lower than 1 mGy. A non-linear dose response for chromosomal inversion has been observed in spleen and prostate between 0.001 mGy and 10 mGy, with doses between 0.005–0.01 mGy causing an increase in inversions and doses between 1–10 mGy causing a reduction below spontaneous inversion frequency. An adaptive response is a decreased biological effect induced by a low radiation dose. Adaptive responses contradict the linear-no-threshold model of risk estimation. We demonstrated that very low (0.001 mGy, 0.01 mGy, 1 mGy and 10 mGy) doses of X-radiation induced a chromosomal inversion adaptive response as measured by a reduction in the frequency of subsequent high dose (1000 mGy) induced inversions in prostate. These are the lowest X-radiation doses reported to induce an adaptive response for any endpoint. Adaptive response experiments were also performed where the high dose was administered four hours prior to a low dose of 0.01 mGy or 10 mGy. In both cases an adaptive response was observed. Identification of the modifying factors involved in the adaptive response may provide candidates for radioprotection.

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Yin, Peng-Yeng, Ray-I. Chang, Rong-Fuh Day, Yen-Cheng Lin, and Ching-Yuan Hu. "Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion." Applied Sciences 12, no. 1 (December 22, 2021): 71. http://dx.doi.org/10.3390/app12010071.

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The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with human respiratory diseases. It is critical to analyze, forecast, and mitigate PM2.5 concentrations. One of the typical meteorological phenomena seducing PM2.5 concentrations to accumulate is temperature inversion which forms a warm-air cap to blockade the surface pollutants from dissipating. This paper analyzes the meteorological patterns which coincide with temperature inversion and proposes two machine learning classifiers for temperature inversion classification. A separate multivariate regression model is trained for the class with or without manifesting temperature inversion phenomena, in order to improve PM2.5 forecasting performance. We chose Puli township as the studied site, which is a basin city easily trapping PM2.5 concentrations. The experimental results with the dataset spanning from 1 January 2016 to 31 December 2019 show that the proposed temperature inversion classifiers exhibit satisfactory performance in F1-Score, and the regression models trained from the classified datasets can significantly improve the PM2.5 concentration forecast as compared to the model using a single dataset without considering the temperature inversion factor.

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Huang, Li-Feng, Cheng-Guo Liu, Zhi-Peng Wu, Li-Jun Zhang, Hong-Guang Wang, Qing-Lin Zhu, Jie Han, and Ming-Chen Sun. "Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals." Remote Sensing 15, no. 14 (July 17, 2023): 3577. http://dx.doi.org/10.3390/rs15143577.

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Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters.

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Schulz, Jonas, Philipp Aziz, and Hans‐Jörg Bart. "Identification of Phase Inversion on Sieve Trays." Chemie Ingenieur Technik 93, no. 7 (March 30, 2021): 1080–87. http://dx.doi.org/10.1002/cite.202000140.

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Shenvi, Neil, J. M. Geremia, and Herschel Rabitz. "Nonlinear Kinetic Parameter Identification through Map Inversion." Journal of Physical Chemistry A 106, no. 51 (December 2002): 12315–23. http://dx.doi.org/10.1021/jp021762e.

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Li, Xiao Long, Jun Jing Zhang, Fu Ming Wang, and Bei Zhang. "Identification of Surrounding Rock Parameters Based on MSVR." Applied Mechanics and Materials 580-583 (July 2014): 1227–31. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.1227.

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An inversion method based on multi-output support vector regression (MSVR) is proposed for identifying the mechanical parameters of surrounding rock. This method considers the surrounding rock as a multi-output system during excavation, and the surveyed rock deformations of each monitoring section as its output. First, perform numerical experiments based on the principle of orthogonal test to obtain the calculated deformation values corresponding to different rock parameter combinations, and use them as the samples for training the model of MSVR as reflecting the nonlinear mapping relationship between rock and its deformations. Second, use the PSO to seek the optimal rock parameters based on measured deformations of rock mass. An example is employed to test the presented inversion method. The results showed that compared with the inversion method based on single-output support vector regression (SSVR), the proposed one is more inclined to reach the global optimization goals and achieve more reliable inversion results due to its consideration of the inherent correlativity among the measured deformations of each monitoring section.

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Zhou, Dapeng, Zeyu Jin, and Guoqiang Wu. "Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain." Applied Sciences 13, no. 12 (June 8, 2023): 6951. http://dx.doi.org/10.3390/app13126951.

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The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

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Zhang, Xin Chao, Sam Zan Dong Sun, Hai Yang Wang, Yuan Yin Zhang, Hong Liang Gao, and Bing Jing. "Application of Pre-Stack Inversion to a Complex Reservoir Prediction: a Case Study in LG34 Area, Tarim Basin." Advanced Materials Research 468-471 (February 2012): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.624.

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The Donghe Sandstone formation (DS fm. for simplicity) presents an excellent thin condensate gas reservoir in LG34 well area, Tarim Basin. Unfortunately, drilling results show that the reservoir is thin, deeply buried (below 5000m) and mix lithologies. Pre-stack inversion and fluid identification is implemented in this area. The feasibility and validity of pre-stack inversion for this kind of reservoir is tested, since that application of pre-stack inversion in this area is for the first time. A new rock physics model is used for estimating rock velocities and elastic attributes, and cross-plots of Poisson's ratio (σ) again LambdaRho (λρ) is found to be more favorable for lithofacies identification. Then, pre-stack inversion which uses seismic data with careful amplitude processing is performed to predict the reservoir. The inversion results agree and tie with most wells, suggesting that the pre-stack inversion is viable process for this complex lithological reservoir prediction in the area.

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Yang, Cheng Zhong, and Xian Da Xu. "Intelligent Identification of High Rock-Filled Embankment Constitutive Model Parameters." Advanced Materials Research 183-185 (January 2011): 2139–42. http://dx.doi.org/10.4028/www.scientific.net/amr.183-185.2139.

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Based on the orthogonal design method, the finite element method was combined with the artificial neural network to have established high rock-filled embankment constitutive model parameters inverse analysis method. According to orthogonal design requirements, the level of inversion parameters were selected and the numerical simulation program were determined. By ANSYS software calculated out the analysis samples of neural network and trained the BP neural network.Using the field monitoring displacement,the soil constitutive model parameters were identified and the inversion parameters were compared with the theoretical value.The results show that: the maximum relative error of the inversion value with the theoretical value is no more than 9%,which meets accuracy requirements.

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Zhang, Shuo, Fan Liang, Caixia Lei, Junping Wu, Jing Fu, Qi Yang, Xiao Luo, et al. "Long-read sequencing and haplotype linkage analysis enabled preimplantation genetic testing for patients carrying pathogenic inversions." Journal of Medical Genetics 56, no. 11 (August 22, 2019): 741–49. http://dx.doi.org/10.1136/jmedgenet-2018-105976.

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BackgroundPreimplantation genetic testing (PGT) has already been applied in patients known to carry chromosomal structural variants to improve the clinical outcome of assisted reproduction. However, conventional molecular techniques are not capable of reliably distinguishing embryos that carry balanced inversion from those with a normal karyotype. We aim to evaluate the use of long-read sequencing in combination with haplotype linkage analysis to address this challenge.MethodsLong-read sequencing on Oxford Nanopore platform was employed to identify the precise positions of inversion break points in four patients. Comprehensive chromosomal screening and genome-wide haplotype linkage analysis were performed based on SNP microarray. The haplotypes, including the break point regions, the whole chromosomes involved in the inversion and the corresponding homologous chromosomes, were established using informative SNPs.ResultsAll the inversion break points were successfully identified by long-read sequencing and validated by Sanger sequencing, and on average only 13 bp differences were observed between break points inferred by long-read sequencing and Sanger sequencing. Eighteen blastocysts were biopsied and tested, in which 10 were aneuploid or unbalanced and eight were diploid with normal or balanced inversion karyotypes. Diploid embryos were transferred back to patients, the predictive results of the current methodology were consistent with fetal karyotypes of amniotic fluid or cord blood.ConclusionsNanopore long-read sequencing is a powerful method to assay chromosomal inversions and identify exact break points. Identification of inversion break points combined with haplotype linkage analysis is an efficient strategy to distinguish embryos with normal or balanced inversion karyotypes, facilitating PGT applications.

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Wang, Xiang Jian, and Jie Cui. "Physical Parameter Identification of Horizontal Layered Soil without Ground Motion of Bedrock." Applied Mechanics and Materials 90-93 (September 2011): 1439–42. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.1439.

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For linear parametric system, like mass proportional damping case, physical parameter identification of horizontal layered soil and inversion of ground motion of bedrock are done by using the Least-squares-based hybrid inversion algorithm. For nonlinear parametric system, such as Rayleigh proportional damping case, the modified Levenberg-Marquardt (mLM) method is introduced. Since the mLM method is sensitive to the initial values of parameter, a SVD-mLM method is proposed with combination of singular value decomposition (SVD). Parameter identification of nonlinear parametric system and inversion of ground motion are performed by using the SVD-mLM-based hybrid inversion algorithm. The numerical simulation results show that the identified parameters of horizontal soil layers and inversed ground motion of bedrock have high precision, and the proposed method is useful for geotechnical engineering and robust on noise.

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Wang, Kai Yan, Dan Liu, and Shi Long Wang. "The Application of Spectrum Inversion Based on Genetic Algorithm to Recognition of Thin Layer." Applied Mechanics and Materials 738-739 (March 2015): 405–8. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.405.

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Spectrum inversion method based on genetic algorithm (GA) is a kind of optimization methods for improving seismic data resolution, compared with the spectrum inversion based on conjugate gradient method,GA does not require a good starting model but rather a search space.In this paper, this algorithm is first proposed, and applied to thin layer identification,base on the resolution of odd and even component of reflection coefficient in thin layer,derive the spectrum inversion objective function, briefly describe the principle of genetic algorithm, finally in the wedge model and actual data for trial. According to the result, spectrum inversion method based on genetic algorithm can improve the ability of thin layer identification to a certain extent, improve the resolution of the seismic exploration.

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SONG, Yan. "One-More Paillier Inversion and Concurrent Secure Identification." Journal of Software 19, no. 7 (October 21, 2008): 1758–65. http://dx.doi.org/10.3724/sp.j.1001.2008.01758.

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Finsterle, Stefan, and Michael B. Kowalsky. "Joint Hydrological-Geophysical Inversion for Soil Structure Identification." Vadose Zone Journal 7, no. 1 (February 2008): 287–93. http://dx.doi.org/10.2136/vzj2006.0078.

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Ma, Xiaoqun, and Zoi‐Heleni Michalopoulou. "Acoustic inversion via linearization and Bayesian multipath identification." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2384. http://dx.doi.org/10.1121/1.4744403.

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Bailey, Brad, Frazer Barclay, Robert Nesbit, and Andrea Paxton. "Prospect Identification using AVO Inversion and Lithology Prediction." ASEG Extended Abstracts 2010, no. 1 (December 2010): 1–4. http://dx.doi.org/10.1081/22020586.2010.12041850.

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Mukherjee, Himadri, Ankita Dhar, Mridul Ghosh, Sk Md Obaidullah, K. C. Santosh, Santanu Phadikar, and Kaushik Roy. "Music chord inversion shape identification with LSTM-RNN." Procedia Computer Science 167 (2020): 607–15. http://dx.doi.org/10.1016/j.procs.2020.03.327.

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Hu, Xiao San, Guang Xun Chen, and Pan Ke Qin. "Research of Imaging and Inversion of Seismic Attribute." Advanced Materials Research 774-776 (September 2013): 1591–98. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1591.

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In order to improve the accuracy of inversion and quality of imaging of seismic attribute in some targeted area, a new inversion solution is proposed in this paper. By introducing instantaneous seismic inversion and hybrid inversion, the new solution can establish the geological model commendably and thus can finely depict the detailed characteristics of the structure of the stratum. Empirical results show that interpreters can obtain more precise inversion result and clearer seismic profile when adopt this new inversion solution. Hybrid inversion can deduce more reliable wave impedance, Poisson ratio, elasticity value and other important parameters, which is meaningful to the identification of oil, gas, or gas hydrate.

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Li, Chun Lei, Wen Qi Zhang, Zhao Hui Xia, Ming Zhang, Liang Chao Qu, Li Jiang Duan, Bin Ren, and Ling Li Liu. "Application of Seismic Inversion Technique Base on Geostatistics in Prediction of Thin Sand Body Reservoir." Advanced Materials Research 1030-1032 (September 2014): 724–27. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.724.

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Seismic inversion methods include constrained sparse pulse inversion and band limit inversion, etc. Although resolution of the seismic inversion results is higher than seismic data, it does not identify thin interbedding sand body and confirm the development of reservoirs. In this paper, in A block of Indonesia adopted geostatistical inversion in reservoir prediction, which is a method of seismic inversion combining geological statistics simulation and seismic inversion. This inversion method can establish various 3D geological model with the same probability of rock properties and lithology and it obey all seismic, logging and geological data. Using statistical regularity and seismic inversion technique we can obtain more fine reservoir model and finally reach the purpose of identification of single thin sand layer.

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Sinha, Supriya, Arthur Walmsley, Nigel Clegg, Brigido Vicuña, Hsu-Hsiang (Mark) Wu, Andrew McGill, Téo Paiva dos Reis, et al. "Past, Present, and Future Applications of Ultradeep Directional Resistivity Measurements: A Case History From the Norwegian Continental Shelf." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 63, no. 6 (December 1, 2022): 604–33. http://dx.doi.org/10.30632/pjv63n6-2022a3.

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With the introduction of ultradeep azimuthal resistivity (UDAR) logging-while-drilling (LWD) tools toward the beginning of the last decade, the oil and gas industry went from real-time mapping of formation boundaries a few meters from the wellbore to tens of meters away. This innovation allowed early identification of resistivity boundaries and promoted proactive geosteering, allowing for optimization of the wellbore position. Additionally, boundaries and secondary targets that may never be intersected are mapped, allowing for improved well planning for sidetracks, multilaterals, and future wells. Modern tool design and inversion algorithms allow mapping the reservoir in 3D and exploring the sensitivity of these tools to the electromagnetic field ahead of the measure point for look-ahead resistivity. Improvements in the technology over the past decade have changed the way wellbores are planned, drilled, and completed, and reservoir models are updated. This paper presents a case study summarizing the advances in UDAR measurements and inversions over the last decade. The case study presents the whole workflow from prejob planning, service design, and execution of one-dimensional (1D) and three-dimensional (3D) inversion in addition to the future potential of look ahead in horizontal wells. Prewell simulations provide a guide to expected real-time tool responses in highly heterogeneous formations. This identifies how far from the wellbore 1D inversions can map major boundaries above and below the well. A fault was expected toward the toe of the well, and UDAR was used as a safeguard to avoid exiting the reservoir. Standard 1D inversion approaches are too simplistic in this complex geologic setting. Thus, 3D inversion around the wellbore and ahead of the transmitter is also explored to demonstrate the improvements this understanding can bring regarding geostopping toward the fault and reservoir understanding in general. Successful geosteering requires personnel trained to handle complex scenarios. Geosteering training simulators (GTS) could be efficient tools for training to interpret inversions where the “truth” is known from realistic 3D model scenarios. The team can learn how to best exploit UDAR technology and inversion results within its limits and not extend the interpretation beyond acceptable uncertainty levels. It will also be addressed how the understanding of inversion uncertainty could be updated in real time in the future. The continued future success of UDAR technology and 1D to 3D inversion results for look-ahead and look-around applications will depend heavily on uncertainty management of the inversions to avoid wrong decisions and potentially reduced well economy.

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Lian, Fei Yu, and Qing Li. "A New Underground Target Identification Method Based on Ground Penetrating Radar Map." Applied Mechanics and Materials 58-60 (June 2011): 1926–31. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1926.

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In this paper, we proposed a punctuated target identification method for underground homogeneous medium based on dielectric constant inversion algorithm. Its main idea is that, initially, Hough transform is used to locate the target characterized by hyperbola in a radar profile map, then a layer-by-layer waveform inversion algorithm is used to invert the dielectric constant on the location where the target lies. To guarantee the correctness of inversion, the transmission beam method is adopted to obtain initial parameters and calibration factors required by inversion. Through numerical analysis of the dielectric constant of the target, the classification of multiple targets in an underground medium can be confirmed. This method not only overcomes the failure of the traditional phase-comparison method to distinguish different kinds of targets in the same region, but also overcomes the limitation of the image processing method, in which it only classifies the target in a coarse-grained manner. Experimental results show that this method has many advantages, such as fine-grained classification, high precision, and multiple target identification, in the identification of underground targets.

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Horino, Atsuko, Tsuyoshi Kenri, Yuko Sasaki, Noboru Okamura, and Tsuguo Sasaki. "Identification of a site-specific tyrosine recombinase that mediates promoter inversions of phase-variable mpl lipoprotein genes in Mycoplasma penetrans." Microbiology 155, no. 4 (April 1, 2009): 1241–49. http://dx.doi.org/10.1099/mic.0.025437-0.

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Mycoplasma penetrans has the ability to change its surface lipoprotein profiles frequently. The P35 family lipoproteins encoded by the mpl genes are key players in this profile variation. The M. penetrans HF-2 genome has 38 mpl genes that form three gene clusters. Most of these mpl genes have an invertible promoter sequence that is responsible for the ON/OFF switching of individual mpl gene expression. Here, we identified the recombinase that catalyses inversions of the mpl gene promoters. We focused on two open reading frames of the M. penetrans HF-2 genome, namely MYPE2900 and MYPE8180, which show significant homology to the tyrosine site-specific recombinase (Tsr) family proteins. Since genetic tools for M. penetrans are still not developed, we cloned the MYPE2900 and MYPE8180 genes and expressed them in Mycoplasma pneumoniae and Escherichia coli. The promoter regions of the mpl genes [p35 (MYPE6810) or p42 (MYPE6630) genes] were also introduced into M. pneumoniae and E. coli cells expressing MYPE2900 or MYPE8180. Inversion of these promoters occurred in the presence of the MYPE2900 gene but not in the presence of the MYPE8180 gene, indicating that the MYPE2900 gene product is the recombinase that catalyses mpl gene promoter inversions. We used a PCR-based method to detect mpl promoter inversion. This method also enabled us to detect inversions of 10 mpl gene promoters in M. penetrans HF-2 cells. All these promoter inversions occurred at the 12 bp inverted repeat (IR) sequences flanking the promoter sequence. The consensus sequence of these IRs was proposed as TAAYNNNDATTA (Y=C or T; D=A, G or T).

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Luo, Hongwen, Ying Li, Haitao Li, Xiaojiang Cui, and Zhangxin Chen. "Simulated Annealing Algorithm-Based Inversion Model To Interpret Flow Rate Profiles and Fracture Parameters for Horizontal Wells in Unconventional Gas Reservoirs." SPE Journal 26, no. 04 (January 21, 2021): 1679–99. http://dx.doi.org/10.2118/205010-pa.

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Summary With the increasing application of distributed temperature sensing (DTS) in downhole monitoring for multifractured horizontal wells (MFHWs), well performance interpretation by inversing DTS data has become a popular topic around the world. However, because of the lack of efficient inversion models, great challenges still exist in interpreting flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs from DTS data. In this paper, a robust inversion system is developed to interpret flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs by inversion of DTS data. A temperature prediction model serves as a forward model to simulate the temperature behaviors of MFHWs. A new inversion model based on a simulated annealing (SA) algorithm is proposed to find inversion solutions to flow rate profiles and fracture parameters. The simulated results of temperature behaviors indicate that the temperature profile of each MFHW is irregularly serrated, and the temperature drop in each serration is positively correlated with the inflow rate and fracture half-length. These results provide an excellent method to identify and locate effective hydraulic fractures for field MFHWs. Because of the far more significant influence of fracture half-length than conductivity on a temperature profile, fracture half-length was chosen as the inversion target parameter when performing the inversion of DTS data for MFHWs. Then a synthetic inversion task was accomplished using the SA algorithm-based inversion system, and it took only 110 iterations to reach the target inversion accuracy (10−6 level). Real-time inversion error distributions indicate that this novel inversion system shows great advantages in computational efficiency. Finally, a field application in a shale gas reservoir is presented to validate the reliability of the new inversion model. Based on accurate identification of effective fractures from DTS profiles, satisfactory inversion solutions (the maximum temperature deviation of less than 0.03 K) are obtained. The absolute error of the inversed gas production rate is less than 4 m3/d. The SA algorithm-based inversion system proves reliable to interpret flow rate profiles and fracture parameters, which is a great help to postfracturing evaluation and productivity improvement for MFHWs in unconventional gas reservoirs.

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Xu, Guang-Jing, Qiang Zu, Xiao-Hui Yang, Tao Tao, Peng Han, Qiang Luo, Sixu Han, and Zhan-Xiang He. "Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples." Applied Sciences 14, no. 23 (December 7, 2024): 11409. https://doi.org/10.3390/app142311409.

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Gravity data inversion is of critical importance in geophysics, encompassing a range of applications, such as the exploration of geological resources, the identification of geological structures, and the detection of groundwater resources. This study proposes a three-dimensional (3D) machine learning approach to enhance the efficiency of the aforementioned exploration tasks by leveraging gravity data. The mapping relationship between gravity data and subsurface density structures is modeled by the broad learning network, distinguished by its high training efficiency and robust modeling capability. Notably, the proposed inversion method obviates the constraints on the number of anomalies prior to the inversion process. This is achieved by setting one anomaly with varied locations for different training samples. Numerical and field data applications demonstrate the efficiency of the proposed 3D machine learning gravity data inversion method, especially in automatically determining the number of anomalies. In particular, the proposed method produced accurate density inversion results in the field application, aiding in the identification of potential oil and gas reservoirs in the target region and offering the potential for broader application in other resource exploration. The proposed inversion method can promote the construction of density structures of subsurfaces based on gravity data.

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Guo, Changfang, Zhen Yang, Shuai Chang, Ting Ren, and Wenli Yao. "Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm." Applied Sciences 9, no. 7 (April 10, 2019): 1493. http://dx.doi.org/10.3390/app9071493.

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Precise prediction of coal thickness is of the utmost importance in realizing intelligent and unmanned mining. As the channel wave is characterized by an easily recognizable waveform, a long propagation distance, and strong energy, it is widely used for coal thickness inversion. However, most traditional inversion methods are local in nature, and the inversion result is probably not optimal in the global scope. This paper introduces the GA-SIRT hybrid approach, which combines Genetic Algorithms (GA) and Simultaneous Iterative Reconstructive Techniques (SIRT) in order to deal with the above problem and to improve the accuracy of coal thickness inversion. The proposed model takes full advantage of the strong global search capability of GA and of the fast local convergence rate of the SIRT. Moreover, it inhibits the poor local search ability and the local optimal value effect of the GA and the SIRT respectively. The application of the GA-SIRT in the Guoerzhuang coal mine has significantly enhanced its accuracy, stability, and overall computational efficiency. Hence, the introduced novel hybrid model can precisely resolve and identify the coal thickness according to the channel wave. It can also be extended to other geophysical tomographic inversion problems towards the reduction of potential local optimal solutions.

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Zhang, Dali, Gongsheng Li, Guangsheng Chi, Xianzheng Jia, and Huiling Li. "Numerical Identification of Multiparameters in the Space Fractional Advection Dispersion Equation by Final Observations." Journal of Applied Mathematics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/740385.

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This paper deals with an inverse problem for identifying multiparameters in 1D space fractional advection dispersion equation (FADE) on a finite domain with final observations. The parameters to be identified are the fractional order, the diffusion coefficient, and the average velocity in the FADE. The forward problem is solved by a finite difference scheme, and then an optimal perturbation regularization algorithm is introduced to determine the three parameters simultaneously. Numerical inversions are performed both with the accurate data and noisy data, and several factors having influences on realization of the algorithm are discussed. The inversion solutions are in good approximations to the exact solutions demonstrating the efficiency of the proposed algorithm.

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Yang, Wenqiang, Zhifang Yang, Zhaoyun Zong, Qianhao Sun, and Xinfei Yan. "AVA inversion for novel fluid indicator considering consolidation parameter." Journal of Geophysics and Engineering 19, no. 6 (November 28, 2022): 1280–99. http://dx.doi.org/10.1093/jge/gxac083.

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Abstract Geofluid identification from seismic data are crucial for understanding reservoir characteristics. However, fluid indicators based on elastic parameter combinations show strong ambiguity in terms of geofluid identification. Although the effective pore-fluid bulk modulus proves to be the superior fluid indicator in geofluid discrimination, it is limited to empirical models such as the critical porosity model. The consolidation parameter model can evaluate the consolidation and compaction of sediments and is widely used due to its better applicability. Therefore, a novel fluid indicator considering consolidation parameter is proposed and a boundary-constrained inversion strategy for geofluid identification is developed. First, the novel fluid indicator is defined based on the poroelasticity theory. Then, the linearized AVA (amplitude variation with angle) approximation equation related to the new fluid indicator is derived to directly link seismic data and properties of pore-filling materials. The model analysis verifies the accuracy of the derived approximation equation at moderate incident angles, which can be used for parameter prediction by pre-stack seismic inversion. Furthermore, the contribution of each attribute of the novel equation to the reflectivity is analyzed to validate the feasibility of new fluid indicator inversion. Finally, a boundary-constrained AVA pre-stack inversion method is presented to enhance the robustness of the inversion results of the model parameter. The reliability of the improved method is proved by accurate inversion results of synthetic seismic records. After determining the consolidation parameter using well-log data combined with BGT (Biot–Gassmann Theory), the field data further demonstrated that the proposed approach is accurate and effective.

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Park, S. K., and D. W. Livelybrooks. "Quantitative interpretation of rotationally invariant parameters in magnetotellurics." GEOPHYSICS 54, no. 11 (November 1989): 1483–90. http://dx.doi.org/10.1190/1.1442612.

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Use of rotationally invariant parameters derived from the magnetotelluric impedance tensor avoids problems with identification of electrical strike. However, quantitative analysis of an invariant must be used with caution. Layered models from inversion of invariant sounding curves accurately estimate the structure, as does one‐dimensional inversion of an invariant parameter, if the site is away from conductive three‐dimensional (3‐D) heterogeneities. Inversions at sites both above and away from a resistive heterogeneity and above a conductive heterogeneity result in errors. The errors are most serious at sites above a finite conductor. A section created by interpolation of layered models derived from invariants should always be modeled with multidimensional programs to verify the section’s accuracy. The above conclusions hold regardless of the type of invariant parameter used. We found no advantage to using the determinant over the arithmetic mean of the impedance tensor, or vice versa.

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Han, Jie, Jiaji Wu, Lijun Zhang, Hongguang Wang, Qinglin Zhu, Chao Zhang, Hui Zhao, and Shoubao Zhang. "A Classifying-Inversion Method of Offshore Atmospheric Duct Parameters Using AIS Data Based on Artificial Intelligence." Remote Sensing 14, no. 13 (July 3, 2022): 3197. http://dx.doi.org/10.3390/rs14133197.

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Atmospheric duct parameters inversion is an important aspect of microwave-band radar and communication system performance evaluation. AIS (Automatic Identification System) is one of the signal sources used for atmospheric duct parameters inversion. Before the inversion of atmospheric duct parameters, determining the type of atmospheric duct plays an important role in the inversion results, but the current inversion methods ignore this point. We outlined a classifying-inversion method of atmospheric duct parameters using AIS signals combined with artificial intelligence. The method consists of an atmospheric duct classification model and a parameter inversion model. The classification model judges the type of atmospheric duct, and the inversion model inverts the atmospheric duct parameters according to the type of atmospheric duct. Our findings demonstrated that the accuracy of the atmospheric duct classification model based on deep neural network (DNN) even exceeds 97%, and the atmospheric duct parameters inversion model has better inversion accuracy than that of the traditional method, thereby illustrating the effectiveness and accuracy of this novel method.

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Ciutea, Alexandru, and Vasile Jitariu. "THERMAL INVERSIONS IDENTIFICATION THROUGH THE ANALYSIS OF THE VEGETATION INVERSIONS OCCURRED IN THE FOREST ECOSYSTEMS FROM THE EASTERN CARPATHIANS." Present Environment and Sustainable Development 14, no. 2 (October 14, 2020): 29–42. http://dx.doi.org/10.15551/pesd2020142002.

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The earth’s climate has a direct impact on the spatial distribution of the vegetation cover. Forests are one of the most prevalent ecosystems on earth. The forest’s structure, if unaltered, can be an indicator of some climate patterns. This paper proposes an algorithm for identifying vegetation inversions within mountain forest ecosystems in the eastern Carpathians of Romania, using freely available GIS and remote sensing data. Air temperature has a direct impact on the spatial distribution of tree species. Therefore, temperature-inversion spatial patterns can be estimated.

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Bawahab, Nabil, Udi Harmoko, Tony Yulianto, and Irvan Ramadhan. "Identification of low resistivity layers in the “N” geothermal field using 2D magnetotelluric inversion modelling." Journal of Physics and Its Applications 2, no. 2 (May 11, 2020): 85–89. http://dx.doi.org/10.14710/jpa.v2i2.7532.

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Magnetotelluric research in the “N” geothermal field has been carried out to see the subsurface detail in the “N” geothermal field. 2D inversion model is generated by secondary data from magnetotelluric data collection in the form of time series data to become 2D models. Magnetotellurics method is used to identify geothermal system components, especially identifying layers with low resistivity values (2 Ω.m - 10 Ω.m) or also called as the cap rock which is seen with a very contrasting color difference compared to the surrounding layers. There are manifestations on the “N” geothermal field which reinforce the assumption that there is a geothermal system in this area. This research begins by processing time series data to become apparent resistivity and phase data. Time series data processing in this study uses several processing methods to produce better apparent resistivity and phase data. The final result of this study is a 2D model that illustrates the contour of the resistivity value of rocks laterally or vertically. 2D model interpretation in this study identified the cap rock layer with low resistivity distribution (2 Ω.m - 10 Ω.m), the medium resistivity zone identified as the reservoir layer (11 Ω.m - 70 Ω.m), and the resistive zone which has high resistivity value (more than 70 Ω.m).

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Zhang, Lu, Dongliang Nan, Qi Zhao, Jinlong Tan, Chang Wang, and Yongqiang Liu. "Power grid accident inversion and risk identification based on adaptive fuzzy inference algorithm." Journal of Physics: Conference Series 2903, no. 1 (November 1, 2024): 012029. https://doi.org/10.1088/1742-6596/2903/1/012029.

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Abstract Based on power grid accident inversion and risk identification, a self-adaptive fuzzy inference algorithm has been designed. Using spatial reconstruction method to extract feature information from multi-channel risk data of power grid accidents, introducing the concept of risk accident hazard assessment value, and using it as a variable in the risk data perception model, a basic inversion model based on the selection and transformation of power grid accident risk variables is established. Fuzzy logic units are combined with neural networks to propose an adaptive neuro-fuzzy inference algorithm. Applying adaptive fuzzy reasoning algorithm to the integrated design of power grid accident inversion and risk identification, achieving risk identification. The experimental results show that the experimental data accuracy of this design method is the highest, reaching 0.9907, with an accuracy of 0.9336, a recall rate of 0.9214, a recognition time of 2.84 seconds, and a significant advantage in processing speed.

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Moshayedi, Faezeh, Hengameh Sadat Seidaei, and Amir Mohammad Salehi. "A Case Report of Non-puerperal Uterine Inversion due to Submucosa Leiomyoma in a Young Virgin Woman." Case Reports in Surgery 2022 (August 16, 2022): 1–4. http://dx.doi.org/10.1155/2022/5240830.

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The inversion of the uterus is a rare complication of the puerperium and much rarer during the non-puerperal period. The most common cause of non-puerperal inversion is a submucosa leiomyoma; however, identification can be difficult. Herein, we present a case of pedunculated uterine leiomyoma that causes uterus inversion in a 30-year-old virgin woman with a correct diagnosis; a hysterectomy was prevented for the patient.

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Ma, Jian Gang, Mei Ping Sheng, and Zhi Wei Guo. "The Inversion Approach of Power Flow in Load Identification." Applied Mechanics and Materials 577 (July 2014): 281–84. http://dx.doi.org/10.4028/www.scientific.net/amm.577.281.

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A study about the structure load identification was made in this paper, based on the SEA power flow method. An inversion formula was firstly developed for the case that the load position is known. Using the formula, an algorithm was proposed to locate the source when the load position is unknown. A reference source must be chosen to utilize the algorithm. To make full use of the surplus data and to further improve the identification accuracy, the least square method was applied. Experiments about both cases were taken to validate the approach obtained above and it was shown that the approach is correct and effective. It is proved that the power flow inversion approach has some practical value in engineering.

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Wang, Hui, Feng Qin, Qi Liu, Liu Ruan, Rui Wang, Zhanhong Ma, Xiaolong Li, Pei Cheng, and Haiguang Wang. "Identification and Disease Index Inversion of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data at Canopy Level." Journal of Spectroscopy 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/651810.

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Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built usingK-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.

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Gupta, Shivani, Michael Saulino, and Cory Brantley. "ID:15850 Identification of a Pump Inversion by Radiotelemetry." Neuromodulation: Technology at the Neural Interface 25, no. 4 (June 2022): S57. http://dx.doi.org/10.1016/j.neurom.2022.02.197.

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Tao, Weidong, Zhen Xu, Dongchi Zhao, Chao Wang, Xiaoli Tao, and Hong-jin Sun. "Hand Posture Inversion Effect: Inverted and Individual Identification Paradigm." International Journal of Psychophysiology 168 (October 2021): S171. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.474.

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Moghanloo, H. Ghanbarnejad, Mohammad Ali Riahi, and M. Bagheri. "Application of simultaneous prestack inversion in reservoir facies identification." Journal of Geophysics and Engineering 15, no. 4 (April 26, 2018): 1376–88. http://dx.doi.org/10.1088/1742-2140/aab249.

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Fabrizi, Federica, Pamela A. Thomas, Gareth Nisbet, and Stephen P. Collins. "Identification of inversion domains in KTiOPO4viaresonant X-ray diffraction." Acta Crystallographica Section A Foundations and Advances 71, no. 4 (May 14, 2015): 361–67. http://dx.doi.org/10.1107/s2053273315007238.

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A novel method is presented for the identification of the absolute crystallographic structure in multi-domain polar materials such as ferroelectric KTiOPO4. Resonant (or `anomalous') X-ray diffraction spectra collected across the absorptionKedge of Ti (4.966 keV) on a single Bragg reflection demonstrate a huge intensity ratio above and below the edge, providing a polar domain contrast of ∼270. This allows one to map the spatial domain distribution in a periodically inverted sample, with a resolution of ∼1 µm achieved with a microfocused beam. This non-contact, non-destructive technique is well suited for samples of large dimensions (in contrast with traditional resonant X-ray methods based on diffraction from Friedel pairs), and its potential is particularly relevant in the context of physical phenomena connected with an absence of inversion symmetry, which require characterization of the underlying absolute atomic structure (such as in the case of magnetoelectric coupling and multiferroics).

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Jalali, H., B. T. Bonab, and H. Ahmadian. "Identification of Weakly Nonlinear Systems Using Describing Function Inversion." Experimental Mechanics 51, no. 5 (July 23, 2010): 739–47. http://dx.doi.org/10.1007/s11340-010-9375-x.

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Day‐Lewis, Frederick D., Jerry M. Harris, and Steven M. Gorelick. "Time‐lapse inversion of crosswell radar data." GEOPHYSICS 67, no. 6 (November 2002): 1740–52. http://dx.doi.org/10.1190/1.1527075.

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The combination of differential radar tomography with conventional tracer and/or hydraulic tests facilitates high‐resolution characterization of subsurface heterogeneity and enables the identification of preferential flow paths. In dynamic imaging, each tomogram is typically inverted independently, under the assumption that data sets are collected quickly relative to changes in the imaged property (e.g., attenuation or velocity); however, such “snapshot” tomograms may contain large errors if the imaged property changes significantly during data collection. Acquisition of less data over a shorter time interval could ameliorate the problem, but the resulting decrease in ray density and angular coverage could degrade model resolution. To address these problems, we propose a new sequential approach for time‐lapse tomographic inversion. The method uses space‐time parameterization and regularization to combine data collected at multiple times and to account for temporal variation. The inverse algorithm minimizes the sum of weighted squared residuals and a measure of solution complexity based on an a priori space‐time covariance function and a spatiotemporally variable mean. We demonstrate our approach using a synthetic 2‐D time‐lapse (x,z,t) data set based loosely on a field experiment in which difference‐attenuation radar tomography was used to monitor the migration of a saline tracer in fractured rock. We quantitatively show the benefits of space‐time inversion by comparing results for snapshot and time‐lapse inversion schemes. Inversion over both space and time results in superior estimation error, model resolution, and data reproduction compared to conventional snapshot inversion. Finally, we suggest strategies to improve time‐lapse cross‐hole inversions using ray‐based inversion constraints and a modified survey design in which different sets of rays are collected in alternating time steps.

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Hafidah, Annisa Dwi, Yunus Daud, and Alfian Usman. "Reservoir Identification Based on Gravity Method at “AUN” Geothermal Field." E3S Web of Conferences 125 (2019): 14008. http://dx.doi.org/10.1051/e3sconf/201912514008.

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Sumatra Island has the largest geothermal potential in Indonesia spread along the subduction zone between the Indian-Australian plate and the Eurasian plate. “AUN” geothermal field located in Sumatra Island and considered to be one of the largest potential geothermal prospects in Indonesia. This study is focused on identifying the prospect of “AUN” geothermal field using gravity method. First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) analysis were applied in order to determine a more accurate boundary of the fault. 3D inversions of gravity data were used to reconstruct subsurface model. The result show that analysis of First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) can confirm southwest-northeast fault and caldera structure as a boundary of geothermal reservoir and 3D gravity inversion can show subsurface layers with density 2.5 gr/cc to 2.8 gr/cc inside the boundary which is determined as a heat source in “AUN” geothermal field.

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Zhang, Ruyi, and Huazhong Wang. "Viscoelastic Fluid Factor Inversion and Application in Luojia Oilfield Based on Broadband Impedance." Geofluids 2021 (June 21, 2021): 1–8. http://dx.doi.org/10.1155/2021/8989769.

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Based on the physical quantity of log data, the accurate identification of oil- and gas-bearing properties may be caused by the prestack inversion of fluid prediction, which will affect the success rate of exploration and development. Prestack data contain more information of amplitude and frequency. Using the frequency-dependent viscoelastic impedance equation and Bayesian inversion framework, the objective function of frequency-dependent elastic impedance inversion can be established to realize the frequency-dependent impedance inversion at different angles. According to the elastic impedance equation of the frequency-varying viscoelastic fluid factor, the relationship between elastic impedance and the frequency-dependent viscoelastic fluid factor is established, and the prestack seismic inversion method of the frequency-dependent viscoelastic fluid factor is studied. However, one of the important factors easily neglected is that we have been using logging data to establish fluid-sensitive parameters and the lithophysical version for fluid identification, so there are differences between logging and seismic frequency bands for fluid identification. The indicator factors with higher sensitivity to fluid can be selected by laboratory measurements. This article applies this method on Luojia oilfield data and verifies this method with log interpretation results, based on the sample of rock physics obtained in a low-frequency rock physics experiment; the technique of dispersion and fluid-sensitive parameters is studied, and the fluid prediction technology of a multifrequency band rock physics template is adopted, which can build the relationship between rock physical elastic parameters and fluid properties by the multifrequency broadband impedance method.

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Zou, Bo, Jingsheng Zhai, Jian Xu, Zhaoxing Li, and Sunpei Gao. "A Method for Estimating Dominant Acoustic Backscatter Mechanism of Water-Seabed Interface via Relative Entropy Estimation." Mathematical Problems in Engineering 2018 (December 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/4272436.

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It is important to distinguish the dominant mechanism of seabed acoustic scattering for the quantitative inversion of seabed parameters. An identification scheme is proposed based on Bayesian inversion with the relative entropy used to estimate dominant acoustic backscatter mechanism. DiffeRential Evolution Adaptive Metropolis is used to obtain samples from posterior probability density in Bayesian inversion. Three mechanisms for seabed scattering are considered: scattering from a rough water-seabed interface, scattering from volume heterogeneities, and mixed scattering from both interface roughness and volume heterogeneities. Roughness scattering and volume scattering are modelled based on Fluid Theories using Small-Slope Approximation and Small-Perturbation Fluid Approximation, respectively. The identification scheme is applied to three simulated observation data sets. The results indicate that the scheme is promising and appears capable of distinguishing sediment volume from interface roughness scattering and can correctly identify the dominant acoustic backscatter mechanism.

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Wang, Lingqian, Hui Zhou, Bo Yu, Yanxin Zhou, Wenling Liu, and Yukun Tian. "Inversion for Geofluid Discrimination Based on Poroelasticity and AVO Inversion." Geofluids 2019 (November 26, 2019): 1–17. http://dx.doi.org/10.1155/2019/2656747.

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Geofluid discrimination plays an important role in reservoir characterization and prospect identification. Compared with other fluid indicators, the effective pore-fluid bulk modulus is more sensitive to the property of fluid contained in reservoirs. We combine the empirical relations with deterministic models to form a new kind of linearized relationship between the mixed fluid/rock term and the fluid modulus. On the one hand, the linearized relationship can decouple the fluid bulk modulus from the mixed fluid/rock term; on the other hand, the decoupled terms are more stable especially in low-porosity situations compared with previous approaches. In terms of the new linearized equation of the fluid modulus, we derive a novel linearized amplitude variation with offset (AVO) approximation to avoid the complicated nonlinear relationship between the fluid modulus and the reflectivity series. Convoluting this linearized AVO approximation with seismic wavelets, the forward modeling is constructed to combine the prestack seismic records with the fluid modulus. Meanwhile, we introduce the Bayesian inference with multivariable Cauchy prior to the fluid modulus inversion for a stable and high-resolution solution. Model examples demonstrate the accuracy of the proposed linearized AVO approximation compared with the exact Zoeppritz equation and Aki-Richards approximate equation. The synthetic and field data tests illustrate the accuracy and feasibility of the proposed fluid modulus inversion approach for geofluid discrimination.

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Eken, Gokay, and Abdulhamit Misir. "Comparison of Computed Tomography, Traction, and Inverted Grayscale Radiographs for Understanding Pilon Fracture Morphology." Foot & Ankle International 43, no. 3 (October 12, 2021): 398–403. http://dx.doi.org/10.1177/10711007211049247.

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Background: There have been no studies evaluating the usefulness of grayscale radiographs in extremity fractures. We aimed to compare the ability and reliability of traction radiographs vs traction grayscale inversion radiographs to detect fracture fragment and comminution zones in comminuted tibia pilon fractures. Methods: Plain radiographs and grayscale inversion images of 60 patients with Orthopaedic Trauma Association/AO Foundation type C3 fracture were evaluated by 20 observers (15 orthopaedic surgeons and 5 radiologists) after traction had been applied. The anterolateral, posterolateral, and medial malleolar fragments, as well as the lateral, central, and medial column comminution zones, were identified by all physicians. Computed tomography scan images were used as the “gold standard” against which plain radiographs and grayscale inversion image interpretation were measured. Intra- and interobserver reliability and correct identification of fracture fragments and comminution zones were evaluated. Results: The interobserver reliability for 3 of the fracture fragments and comminution zones on the traction plain radiographs was moderate, whereas it was substantial on traction grayscale inversion radiographs. The lateral comminution zones ( P = .001) and presence or absence of posterolateral fragments ( P < .001) were significantly better identified in grayscale inversion radiographs compared to standard radiographs. Conclusion: After traction was applied, we found grayscale inversion radiographs are superior to plain radiographs in the identification of posterolateral fragment and lateral zone of comminution in comminuted intraarticular pilon fractures. Level of Evidence: Level III, retrospective case series.

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Norell, Jesper, Raphael M. Jay, Markus Hantschmann, Sebastian Eckert, Meiyuan Guo, Kelly J. Gaffney, Philippe Wernet, Marcus Lundberg, Alexander Föhlisch, and Michael Odelius. "Fingerprints of electronic, spin and structural dynamics from resonant inelastic soft X-ray scattering in transient photo-chemical species." Physical Chemistry Chemical Physics 20, no. 10 (2018): 7243–53. http://dx.doi.org/10.1039/c7cp08326b.

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Journal articles on the topic 'Inversion identification'

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