A Study of the Relationship between Air Pollutants and Inversion in the ABL over the City of Lanzhou

By analyzing the pollutant concentrations over the urban area and over the rural area of the city of Lanzhou, Gansu Province, China, the relationships between the daytime inversion intensity and the pollutant concentration in the atmospheric boundary layer （ABL） are studied with the consideration of wind speed and direction, potential temperature, specific humidity profiles, pollutant concentration in the ABL, the surface temperature, and global radiation on the ground. It was shown that the daytime inversion is a key factor in controlling air pollution concentration. A clear and positive feedback process between the daytime inversion intensity and the air pollutants over the city was found through the analysis of influences of climatic and environmental factors. The mechanisms by which the terrain and air pollutants affect the formation of the daytime inversion are discussed. The solar radiation as the essential energy source to maintain the inversion is analyzed, as are various out-forcing factors affecting the inversion and air pollutants. At last, a physical frame of relationships of air pollution with daytime inversion and the local and out-forcing factors over Lanzhou is built.

Linearized waveform inversion for vertical transversely isotropic elastic media:Methodology and multi-parameter crosstalk analysis

Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.

Development of multi-channel observation and inversion for IP electrical sounding method

In order to improve the exploration effect of deep non-ferrous mineral resources, multi-channel observation methods for induced polarization （IP） electrical sounding data and their inversion imaging technology are studied. First of all, four multi-channel observation methods are developed based on conventional IP electrical method, namely three-electrode and four-electrode arrays of unilateral and bilateral current transmitting. Then the maximum smoothness constrained inversion method of the least squares sense for IP electrical sounding data is proposed, and the inversion software is programmed. Finally, the simulation and inversion results of geo-electrical model for the proposed observation methods are analyzed. And the comparison results show that three-electrode array of bilateral current transmitting gives the best result, but the intensity in field work is larger than others; unilateral three-electrode and four-electrode arrays give the better results. Taking detection results and convenience of field exploration work into consideration, these two methods are more suitable for practical application; bilateral observation method of four-electrode array is not suitable for the detection of the steep ore bodies.

Tomographic velocity inversion for ADCIGs in areas with a rugged surface

Pre-stack depth migration velocity analysis is one of the key techniques influencing image quality. As for areas with a rugged surface and complex subsurface, conventional prestack depth migration velocity analysis corrects the rugged surface to a known datum or designed surface velocity model on which to perform migration and update the velocity. We propose a rugged surface tomographic velocity inversion method based on angle-domain common image gathers by which the velocity field can be updated directly from the rugged surface without static correction for pre-stack data and improve inversion precision and efficiency. First, we introduce a method to acquire angle-domain common image gathers （ADCIGs） in rugged surface areas and then perform rugged surface tornographic velocity inversion. Tests with model and field data prove the method to be correct and effective.

The Study on the Strong Ground Motion Attenuation Relationship in the Pishan Area,Xinjiang Uygur Autonomous Region

Small earthquake data from the Pishan MS6.5 aftershocks is collected by the Xinjiang Regional Digital Seismic Observation Network.Five parameters of the focal region are obtained by micro genetic inversion:stress dropΔσof 75.95 bars,quality factor parameters Q0of 186.33 andηof 0.26,geometric attenuation parameters R1of 72.18km and R2of 139.70km.We calculate the Fourier spectrum and combine it with the random phase spectrum to get the ground motion time history,and build the strong motion acceleration attenuation relationship.The strong ground motion acceleration attenuation of the Pishan area is thus obtained.Because of the insufficiency of strong ground motion records,we added the records from the Wuqia MS6.9 earthquake on October 5,2008,the Akto MS6.2 earthquake on October 6,2008,and the Lop MS6.0 earthquake on March 9,2012 to the data.The comparison of the calculation results and the empirical attenuation relationships with strong ground motion records reveal that the strong motion data of Pishan and Lop earthquakes is higher than the empirical attenuation relationships.The Wuqia MS6.9 earthquake strong motion data is consistent with Yu Yanxiangs(2013)short axis result,and lower than the present result.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Study on the amplitude inversion of internal waves at Wenchang area of the South China Sea

The field experiment is conducted from April 16, 2005 to July 20, 2005 at Wenchang area east of Hainan Island （19~35＇N, l12~E） of China. Internal wave packets are observed frequently with thermistor chains during the experiment. Meanwhile, internal waves are also detected from a synthetic aperture radar （SAR） image on June 19, 2005 and several other moderate-resolution imaging spectroradiometer （MODIS） images near a mooring position. The distance between the positive and negative peaks induced by the internal wave can be obtained from satellite images. Combined with remote sensing images and in situ data, a new method to inverse the amplitude of the internal wave is proposed based on a corrected nonlinear Schr6dinger （NLS） equation. Two relationships are given between the peak-to-peak distance and the characteristic wavelength of the internal wave for different nonlinear and dispersion coefficients. Based on the satellite images, the amplitude inversion of the internal waves are carried out with the NLS equation as well as the KdV equation. The calculated amplitudes of the NLS equation are close to the observation amplitude which promise the NLS equation a reliable method.

Cardiac and non-cardiac causes of T-wave inversion in the precordial leads in adult subjects: A Dutch case series and review of the literature

AIM: To describe the electrocardiographic(ECG) phenomena characterized by T-wave inversion in the precordial leads in adults and to highlight its differential diagnosis. METHODS: A retrospective chart review of 8 adult patients who were admitted with ECG T-wave inversion in the anterior chest leads with or without prolongation of corrected QT(QTc) interval. They had different clinical conditions. Each patient underwent appropriate clinical assessment including investigation for myocardial involvement. Single and multimodality noninvasive, semi-invasive and invasive diagnostic approach were used to ascertain the diagnosis. The diagnostic assessment included biochemical investigation, cardiac and abdominal ultrasound, cerebral and chest computed tomography, nuclear medicine and coronary angiography.RESULTS: Eight adult subjects(5 females) with a mean age of 66 years(range 51 to 82) are analyzed. The etiology of T-wave inversion in the precordial leads were diverse. On admission, all patients had normal blood pressure and the ECG showed sinus rhythm. Five patients showed marked prolongation of the QTc interval. The longest QTc interval(639 ms) was found in the patient with pheochromocytoma. Giant T-wave inversion(≥ 10 mm) was found in pheochromocytoma followed by electroconvulsive therapy and finally ischemic heart disease. The deepest T-wave was measured in lead V3(5 ×). In 3 patients presented with mild T-wave inversion(patients 1, 5 and 4 mm), the QTc interval was not prolonged(432, 409 and 424 msec), respectively.CONCLUSION: T-wave inversion associated with or without QTc prolongation requires meticulous history taking, physical examination and tailored diagnostic modalities to reach rapid and correct diagnosis to establish appropriate therapeutic intervention.

Predictors of short-term outcome in patients with acute middle cerebral artery occlusion: unsuitability of fluid-attenuated inversion recovery vascular hyperintensity scores

Fluid-attenuated inversion recovery （FLAIR） vascular hyperintensity （FVH） is used to assess leptomeningeal collateral circulation, but clinical outcomes of patients with FVH can be very different. The aim of the present study was to assess a FVH score and explore its relationship with clinical outcomes. Patients with acute ischemic stroke due to middle cerebral artery M1 occlusion underwent magnetic resonance imaging and were followed up at 10 days （National Institutes of Health Stroke Scale） and 90 days （modified Rankin Scale） to determine short-term clinical outcomes. Effective collateral circulation indirectly improved recovery of neurological function and short-term clinical outcome by extending the size of the pial penumbra and reducing infarct lesions. FVH score showed no correlation with 90-day functional clinical outcome and was not sufficient as an independent predictor of short-term clinical outcome.

Three-dimensional forward modeling and inversion of borehole-to-surface electrical imaging with different power sources

Borehole-to-surface electrical imaging （BSEI） uses a line source and a point source to generate a stable electric field in the ground. In order to study the surface potential of anomalies, three-dimensional forward modeling of point and line sources was conducted by using the finite-difference method and the incomplete Cholesky conjugate gradient （ICCG） method. Then, the damping least square method was used in the 3D inversion of the formation resistivity data. Several geological models were considered in the forward modeling and inversion. The forward modeling results suggest that the potentials generated by the two sources have different surface signatures. The inversion data suggest that the low- resistivity anomaly is outlined better than the high-resistivity anomaly. Moreover, when the point source is under the anomaly, the resistivity anomaly boundaries are better outlined than when using a line source.

Optimized cardiac magnetic resonance imaging inversion recovery sequence for metal artifact reduction and accurate myocardial scar assessment in patients with cardiac implantable electronic devices

Late gadolinium enhancement(LGE) cardiovascular magnetic resonance(CMR) is the gold standard for imaging myocardial viability.An important application of LGE CMR is the assessment of the location and extent of the myocardial scar in patients with ventricular tachycardia(VT), which allows for more accurate identification of the ablation targets.However, a large percentage of patients with VT have cardiac implantable electronic devices(CIEDs), which is a relative contraindication for cardiac magnetic resonance imaging due to safety and image artifact concerns.Previous studies showed that these patients can be safely scanned on 1.5 T scanners provided that an adequate imaging protocol is adopted.Nevertheless, imaging patients with a CIED result in metal artifacts due to the strong frequency off-resonance effects near the device; therefore, the spins in the surrounding myocardium are not completely inverted, and thus give rise to hyperintensity artifacts.These artifacts obscure the myocardial scar tissue and limit the ability to study the correlation between the myocardial scar structure and the electro-anatomical map during catheter ablation.In this study, we developed a modified inversion recovery technique to alleviate the CIED-induced metal artifacts and improve the diagnostic image quality of LGE images in patients with CIEDs without increasing scan time or requiring additional hardware.The developed technique was tested in phantom experiments and in vivo scans, which showed its capability for suppressing the hyperintensity artifacts without compromising myocardium nulling in the resulting LGE images.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Early Diagnosis of Recurrent Optic Neuritis Using Contrast-Enhanced T2 Fluid-attenuated Inversion Recovery Imaging：a Case Report

The diagnosis of the recurrent optic neuritis is commonly established clinically,and sometimes it could be challenging because the involved optic nerve does not always show significant enhancement on conventional contrast enhanced-T1 weighted imaging（CE-T1W1）.In this paper,we reported a middle-aged female with early diagnosis of recurrent optic neuritis using contrast-enhanced T2 fluid-attenuated inversion recovery imaging（CET2FLAIR）.The involved optic nerve presented evident enhancement on CE-T2FLAIR and no enhancement on CE-T1W1.This case suggested that the CE-T2FLAIR may be a useful diagnostic tool specifically for the recurrent optic neuritis in clinical practice.

Cone-beam local reconstruction based on a Radon inversion transformation

The local reconstruction from truncated projection data is one area of interest in image reconstruction for com- puted tomography （CT）, which creates the possibility for dose reduction. In this paper, a filtered-backprojection （FBP） algorithm based on the Radon inversion transform is presented to deal with the three-dimensional （3D） local recon- struction in the circular geometry. The algorithm achieves the data filtering in two steps. The first step is the derivative of projections, which acts locally on the data and can thus be carried out accurately even in the presence of data trun- cation. The second step is the nonlocal Hilbert filtering. The numerical simulations and the real data reconstructions have been conducted to validate the new reconstruction algorithm. Compared with the approximate truncation resistant algorithm for computed tomography （ATRACT）, not only it has a comparable ability to restrain truncation artifacts, but also its reconstruction efficiency is improved. It is about twice as fast as that of the ATRACT. Therefore, this work provides a simple and efficient approach for the approximate reconstruction from truncated projections in the circular cone-beam CT.

A new approach for inversion of receiver function for crustal structure in the depth domain

A method for reconstructing crustal velocity structure using the optimization of stacking receiver function amplitude in the depth domain,named common conversion amplitude(CCA)inversion,is presented.The conversion amplitude in the depth domain,which represents the impedance change in the medium,is obtained by assigning the receiver function amplitude to the corresponding conversion position where the P-to-S conversion occurred.Utilizing the conversion amplitude variation with depth as an optimization objective,imposing reliable prior constraints on the structural model frame and velocity range,and adopting a stepwise search inversion technique,this method efficiently weakens the tendency of easily falling into the local extremum in conventional receiver function inversion.Synthetic tests show that the CCA inversion can reconstruct complex crustal velocity structures well and is especially suitable for revealing crustal evolution by estimating diverse velocity distributions.Its performance in reconstructing crustal structure is superior to that of the conventional receiver function imaging method.

An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging

To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.

Contrast enhancement of medical ultrasound imaging with reversal phase-inversion pulse technology

A new ultrasound contrast imaging technique was proposed for eliminating the harmonic components from the emission signal transmitted by the broadband ultrasonic system.Reversal phase-inversion pulse was used for the first time to separate the contrast harmonics from the harmonics in the emission signal to improve the detection of contrast micro-bubbles.Based on the nonlinear acoustic theory of finite-amplitude effects and the associated distortion of the propagating wave,the Bessel-Fubini series model was applied to describe the nonlinear propagation effects of the reversal phase-inversion pulse,and the Church's equation for zero-thickness encapsulation model was used to produce the scattering-pulse of the bubble.For harmonic imaging,the experiment was performed using a 64-element linear array,which was simulated by Field II.The results show that the harmonic components from the emission signal can be completely cancelled,and the harmonics generated by the nonlinear propagation of the wave through the tissue,can be reduced by 15-30 dB.Compared with the short pulse,the reversal phase-inversion pulse can improve the contrast and definition of the harmonic image significantly.

High-resolution bone microstructure imaging based on ultrasonic frequency-domain full-waveform inversion

The main challenge in bone ultrasound imaging is the large acoustic impedance contrast and sound velocity differences between the bone and surrounding soft tissue. It is difficult for conventional pulse-echo modalities to give accurate ultrasound images for irregular bone boundaries and microstructures using uniform sound velocity assumption rather than getting a prior knowledge of sound speed. To overcome these limitations, this paper proposed a frequency-domain fullwaveform inversion(FDFWI) algorithm for bone quantitative imaging utilizing ultrasonic computed tomography(USCT).The forward model was calculated in the frequency domain by solving the full-wave equation. The inverse problem was solved iteratively from low to high discrete frequency components via minimizing a cost function between the modeled and measured data. A quasi-Newton method called the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm(L-BFGS) was utilized in the optimization process. Then, bone images were obtained based on the estimation of the velocity and density. The performance of the proposed method was verified by numerical examples, from tubular bone phantom to single distal fibula model, and finally with a distal tibia-fibula pair model. Compared with the high-resolution peripheral quantitative computed tomography(HR-p QCT), the proposed FDFWI can also clearly and accurately presented the wavelength scaled pores and trabeculae in bone images. The results proved that the FDFWI is capable of reconstructing high-resolution ultrasound bone images with sub-millimeter resolution. The parametric bone images may have the potential for the diagnosis of bone disease.

Visual Relationship Detection with Contextual Information

Understanding an image goes beyond recognizing and locating the objects in it,the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predictions of the relationships.But real-world image relationships often determined by the surrounding objects and other contextual information.In this work,we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.The core of the framework is a relationship inference network,which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the image.Experimental results on Stanford VRD and Visual Genome demonstrate that the proposed method achieves a good performance both in efficiency and accuracy.Finally,we demonstrate the value of visual relationship on two computer vision tasks:image retrieval and scene graph generation.

Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.