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High-resolution and high-fidelity seismic inversion imaging are one of the core issues of lithologic reservoir exploration. As a linearization subproblem of full waveform inversion (FWI), least-squares migration (LSM) is the basic theoretical framework of high-resolution and high-fidelity inversion imaging that can be solved in either the data domain or image domain. For data domain LSM (DD-LSM), the calculation of the data domain iteration can be very compute-intensive. In practical situations, the convexity of the cost function of LSM could be poor, resulting in the slow or even non-convergence iteration, so the ideal inversion imaging results cannot be reached. The image domain LSM (ID-LSM) follows the logic of image deblurring, which realizes the effects of LSM from another way. The key problem of ID-LSM is to calculate a reliable Hessian matrix that can be effectively replaced by a set of point-spread-functions (PSFs). Affected by the unknown source wavelet, the accuracy of the background velocity and the accuracy of the forward/migration operator, the calculation results of the Hessian/PSF cannot be accurate. In this paper, we first illustrate the impact of incorrect Hessian/PSF on ID-LSM through numerical experiments. To eliminate the influence of inaccuracy of PSF, this paper proposes the ID-LSM problem to a high-dimensional image deconvolution problem. An alternate update strategy of PSF and imaging results is given under the framework of the Expectation-Maximization (EM) algorithm by additionally introducing prior information about PSF and inversion result. Numerical examples on both synthetic data and field data verify the proposed method significantly improves the image quality.

High-dimensional seismic data are inevitably affected by missing traces and random noise, both of which negatively affect subsequent processing and interpretation. Methods based on the low-rank matrix or tensor decomposition are used to reconstruct seismic data under the assumption that noise-free and complete data are low rank in the frequency-space ( f- x) domain. However, the presence of missing traces and random noise increases the rank of the data matrix or tensor. We use the Vandermonde structure of exponential bases of linear events in the f- x domain, which is in accordance with the plane-wave hypothesis, and develop the Vandermonde constrained tensor CANDECOMP/PARAFAC decomposition (VCPD) method for high-dimensional seismic data reconstruction. The modified alternating direction method of the multipliers algorithm is developed, and the rank-1 matrix approximation is adopted to tackle this constrained optimization problem efficiently. Numerical examples of the synthetic data and field data indicate that the proposed VCPD method is effective and efficient for reconstructing the seismic data in the case of randomly missing with a poor signal-to-noise ratio. In addition, the VCPD method is insensitive to the selection of the tensor rank and more efficient than the related state-of-the-art methods, which should lead to wide applicability.

First-arrival traveltime tomography is an essential method for obtaining near-surface velocity models. The adjoint-state first-arrival traveltime tomography is appealing due to its straightforward implementation, low computational cost, and low memory consumption. Because solving the point-source isotropic eikonal equation by either ray tracers or eikonal solvers intrinsically corresponds to emanating discrete rays from the source point, the resulting traveltime gradient is singular at the source point, and we denote such a singular pattern the imprint of ray-illumination. Because the adjoint-state equation propagates traveltime residuals back to the source point according to the negative traveltime gradient, the resulting adjoint state will inherit such an imprint of ray-illumination, leading to singular gradient-descent directions when updating the velocity model in the adjoint-state traveltime tomography. To mitigate this imprint, we solve the adjoint-state equation twice but with different boundary conditions: one being taken to be regular data residuals and the other taken to be ones uniformly, so that we are able to use the latter adjoint state to normalize the regular adjoint state and we further use the normalized quantity to serve as the gradient direction to update the velocity model; we call this process ray-illumination compensation. To overcome the issue of limited aperture, we have developed a spatially varying regularization method to stabilize the new gradient direction. A synthetic example demonstrates that our method is able to mitigate the imprint of ray-illumination, remove the footprint effect near source points, and provide uniform velocity updates along raypaths. A complex example extracted from the Marmousi2 model and a migration example illustrate that the new method accurately recovers the velocity model and that an offset-dependent inversion strategy can further improve the quality of recovered velocity models.

Seismic wave imaging in complex media requires an accurate wavefield simulation method that can accurately describethe wave propagation in realistic media. Reverse time‐depth migration is the preferred method for seismic wave imaging in complex media. Although it is relatively expensive, its imaging accuracy is usually better than migrations based on the ray method. Migration of primary reflection data requires a wave propagation simulation method that can accurately describe primary reflected/scattered wave energy and incorporate anisotropy. Accordingly, we propose the simulation of wave propagation in tilted transversely isotropic media using a 15° one‐way wave equation in a ray‐centred coordinate system, combining the flexibility of ray theory and the accuracy of wave theory. We use this equation to describe the propagation of body waves in a single ray tube, a ‘beam’. The wavefield along the beam, guided by its central raypath, has an angle limit defined only by the ray angle; therefore, wave propagation in complex and steeply dipping media can be simulated with a 15° one‐way wave equation. Numerical experiments show that the simulation results for beam propagation using the 15° equation in the ray‐centred coordinate system have good accuracy. For prestack depth migration in tilted transversely isotropic media, we built a beam imaging method using this propagator, and this migration method yielded accurate images with greater efficiency than reverse time‐depth migration.

Picking the first arrival is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate first-arrival picking as an intelligent Markov decision process in the multi-dimensional feature attribute space. By designing a reasonable model, the global optimization is carried out in the reward function space to obtain the path with the largest cumulative reward value, to achieve the purpose of automatically picking up the first arrival. The state-value function contains a distance-related discount factor γ, which enables the Markov decision process to pick up the first-arrival continuity to consider the lateral continuity of the seismic data and avoid the bad trace information in the seismic data. On this basis, the method of this paper further introduces the optimized model that is a fuzzy clustering-based multi-dimensional attribute reward function and structure-based Gaussian stochastic policy, thereby reducing the difficulty of model design, and making the seismic data pick up more accurately and automatically. Testing this approach in the field seismic data reveals its properties and shows it can automatically pick up more reasonable first arrivals and has a certain quality control ability, especially the first-arrival energy is weak (the signal-to-noise ratio is low) or there are adjacent complex waveforms in the shallow layer.

Seismic wave imaging in complex media requires a suitable wave field simulation method that can accurately describe real-media wave propagation. Reverse time migration is currently the preferred method; however, it is not optimal for simulating wave field propagation as it is based in solving the wave equation (qP-wave equation). The objective of this study is to develop a wave propagation simulation method to accurately describe the P-wave energy, which is less affected by complex surface conditions, and easily integrate anisotropy and attenuation by absorption media. Herein, qP-wave propagation is simulated in vertical transversely isotropic (VTI) media using the one-way wave equation in the ray-centred coordinate system (15°), which combines the flexibility of ray theory and accuracy of wave theory to describe wave propagation. Based on the qP-wave equation of VTI media, the wave equation in the ray-centred coordinate system and the one-way wave equation (15°) in the ray-centred coordinate system are derived. The 15° one-way wave equation can simulate the wave propagation process in complex media. Numerical experiments verify that the simulation results for the 15° equation in the ray-centred coordinate system exhibit high accuracy.

Conventional seismic migrations are often suitable for structural imaging of the subsurface, but have quantitative limitations. We proposed an imaging strategy for obtaining true-amplitude imaging results within the framework of least-squares migration. Gaussian beam migration and demigration operators are developed to produce two images, which are used to estimate the approximate inverse of the Hessian in the imaging domain by introducing a non-stationary filter operator. By applying an image correction with the estimated inverse Hessian to a conventional Gaussian beam migration result, the resulting image is much closer to the true-amplitude image. Benefiting from the Gaussian beam propagator, which is a one-way beam propagator, the proposed method has more flexibility than the method based on two-way wave equations, and it can be adapted to higher frequency imaging with less computational cost compared to a two-way wave equation method. Some numerical examples are shown to demonstrate the effectiveness and efficiency of the proposed method.

With the development of oil and gas exploration, the conventional seismic migration imaging technology based on the isotropic assumption no longer meets our current requirements for high-resolution images. Migration in anisotropic media has become an essential requirement for oil and gas exploration. Marine seismic exploration has gradually entered the wide-azimuth and high-density seismic data acquisition stage. However, even for current large high-performance computer clusters, it is still very difficult to implement pre-stack depth migration based on shot gathers. Thus, we present a double-square-root (DSR) equation based on three-dimensional (3D) pre-stack depth migration in midpoint-offset domain for a wide-azimuth dataset in transversely isotropic media with a vertical symmetry axis (VTI media). Considering VTI media, the DSR migration requires extensive memory and computation; we adopted the phase-shift plus interpolation approach to improve the computational efficiency. Then, we extract the angle-domain common-image gathers (ADCIGs) during DSR migration. For real large-scale seismic data, we designed an effective parallel implementation of 3D DSR migration with ADCIGs outputs. Finally, we applied the proposed angle-domain VTI DSR migration on wide-azimuth SEG/EAGE salt dome-based data and real data from the China South Sea. Numerical and practical data illustrate the effectiveness of the proposed method.

Most finite-frequency traveltime tomography methods are based on the Born approximation, which requires that the scale of the velocity heterogeneity and the magnitude of the velocity perturbation should be small enough to satisfy the Born approximation. On the contrary, the Rytov approximation works well for large-scale velocity heterogeneity. Typically, the Rytov-approximation-based finite-frequency traveltime sensitivity kernel (Rytov-FFTSK) can be obtained by integrating the phase-delay sensitivity kernels with a normalized weighting function, in which the calculation of sensitivity kernels requires the numerical solution of Green’s function. However, solving the Green’s function explicitly is quite computationally demanding, especially for 3D problems. To avoid explicit calculation of the Green’s function, we show that the Rytov-FFTSK can be obtained by crosscorrelating a forward-propagated incident wavefield and reverse-propagated adjoint wavefield in the time domain. In addition, we find that the action of the Rytov-FFTSK on a model-space vector, e.g., the product of the sensitivity kernel and a vector, can be computed by calculating the inner product of two time-domain fields. Consequently, the Hessian-vector product can be computed in a matrix-free fashion (i.e., first calculate the product of the sensitivity kernel and a model-space vector and then calculate the product of the transposed sensitivity kernel and a data-space vector), without forming the Hessian matrix or the sensitivity kernels explicitly. We solve the traveltime inverse problem with the Gauss-Newton method, in which the Gauss-Newton equation is approximately solved by the conjugate gradient using our matrix-free Hessian-vector product method. An example with a perfect acquisition geometry found that our Rytov-approximation-based traveltime inversion method can produce a high-quality inversion result with a very fast convergence rate. An overthrust synthetic data test demonstrates that large- to intermediate-scale model perturbations can be recovered by diving waves if long-offset acquisition is available.

Previous researches mainly focused on the cooling performance of the vortex tube. Here, the heating performance indicators such as hot temperature difference, heating rate per unit air, and coeffic...

SUMMARY Wave-equation-based traveltime tomography has been extensively applied in both global tomography and seismic exploration. Typically, the traveltime Fréchet derivative is obtained using the first-order Born approximation, which is only satisfied for weak velocity perturbations and small phase shifts (i.e. the weak-scattering assumption). Although the small phase-shift restriction can be handled with the Rytov approximation, the weak velocity-perturbation assumption is still a major limitation. The recently developed generalized Rytov approximation (GRA) method can achieve an improved phase accuracy of the forward-scattered wavefield, in the presence of large-scale and strong velocity perturbations. In this paper, we combine GRA with the classical finite-frequency theory and propose a GRA-based traveltime sensitivity kernel (GRA-TSK), which overcomes the weak-scattering limitation of the conventional finite-frequency methods. Numerical examples demonstrate that the accumulated time delay of forward-scattered waves caused by large-scale smooth perturbations can be correctly handled by the GRA-TSK, regardless of the magnitude of the velocity perturbations. Then, we apply the new sensitivity kernel to solve the traveltime inverse problem, and we propose a matrix-free Gauss–Newton method that has a faster convergence rate compared with the gradient-based method. Numerical tests show that, compared with the conventional adjoint traveltime tomography, the proposed GRA-based traveltime tomography can obtain a more accurate model with a faster convergence rate, making it more suited for recovering the large-intermediate scale of the velocity model, even for strong-perturbation and complex subsurface structures.

TaPGLP1, a chloroplast stromal 2-phosphoglycolate phosphatase of wheat, is an ATG8-interacting protein and undergoes autophagic degradation in starvation-treated wheat mesophyll protoplasts. Selective autophagy in plants has been shown to target diverse cellular cargoes including whole chloroplasts (Chlorophagy) and several chloroplast components (Piecemeal chlorophagy). Most cargoes of selective autophagy are captured by the autophagic machinery through their direct or indirect interactions with the autophagy-essential factor ATG8. Here, we reported a new ATG8-interacting cargo of piecemeal chlorophagy, the wheat photorespiratory 2-phosphoglycolate phosphatase TaPGLP1. The TaPGLP1-mCherry fusions expressed in wheat protoplasts located in the chloroplast stroma. Strikingly, these fusions are translocated into newly formed chloroplast surface protrusions after a long time incubation of protoplasts in a nutrition-free solution. Visualization of co-expressed TaPGLP1-mCherry and the autophagy marker GFP-TaATG8a revealed physical associations of TaPGLP1-mCherry-accumulating chloroplast protrusions with autophagic structures, implying the delivery of TaPGLP1-mCherry fusions from chloroplasts to the autophagic machinery. TaPGLP1-mCherry fusions were also detected in the GFP-TaATG8a-labelled autophagic bodies undergoing degradation in the vacuoles, which suggested the autophagic degradation of TaPGLP1. This autophagic degradation of TaPGLP1 was further demonstrated by the enhanced stability of TaPGLP1-mCherry in protoplasts with impaired autophagy. Expression of TaPGLP1-mCherry in protoplasts stimulated an enhanced autophagy level probably adopted by cells to degrade the over-produced TaPGLP1-mCherry fusions. Results from gene silencing assays showed the requirement of ATG2s and ATG7s in the autophagic degradation of TaPGLP1. Additionally, TaPGLP1 was shown to interact with ATG8 family members. Collectively, our data suggest that autophagy mediates the degradation of the chloroplast stromal protein TaPGLP1 in starvation-treated mesophyll protoplasts.

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

Assuming that an accurate background velocity is obtained, least-squares migration (LSM) can be used to estimate underground reflectivity. LSM can be implemented in either the data domain or image domain. The data domain LSM (DDLSM) is not very practical because of its huge computational cost and slow convergence rate. The image domain LSM (IDLSM) might be a flexible alternative if estimating the Hessian matrix using a cheap and accurate approach. It has practical potential to analyse convenient Hessian approximation methods because the Hessian matrix is too huge to compute and save. In this paper, the Hessian matrix is approximated with non-stationary matching filters. The filters are calculated to match the conventional migration image to the demigration/remigration image. The two images are linked by the Hessian matrix. An image deblurring problem is solved with the estimated filters for the IDLSM result. The combined sparse and total variation regularisations are used to produce accurate and reasonable inversion results. The numerical experiments based on part of Sigsbee model, Marmousi model and a 2D field data set illustrate that the non-stationary matching filters can give a good approximation for the Hessian matrix, and the results of the image deblurring problem with combined regularisations can provide high-resolution and true-amplitude reflectivity estimations.

Autophagy is a conserved self-degradation process in eukaryotic cells. The execution of autophagy relies on a number of AuTophaGy-related (ATG) factors including ATG8 which is encoded in higher eukaryotes by a family of genes. A protective role of autophagy against environmental stresses has been described in several plant species but not in wheat (Triticum aestivum L.), and information on the functional differentiation of ATG8 family is lacking. In this study, we investigated the responses of wheat autophagy and ATG8 family genes to different stresses. In wheat seedlings and within 48 h of salt/drought stress or 4 days of nitrogen starvation stress, the autophagic activity of leaf tissues was activated by these three abiotic stresses and that of root tissues enhanced by drought and nitrogen starvation. This activated or enhanced autophagic activity was supported by up-regulated expression levels of ATG8s. Wheat ATG8s also responded to infection by the powdery mildew causal fungus, and their expression was inhibited in susceptible seedlings while repeatedly up-regulated in resistant seedlings during the early stage of pathogen infection. A comparison among the ATG8 family showed that different ATG8s had similar stress-regulated expression patterns and that several ATG8s were more sensitive to specific stresses. Collectively, these results suggest that the ATG8-requiring autophagy process is involved in wheat responses to stresses and that different ATG8s are commonly involved but have unequal significance in wheat response to specific stresses.

Absolute acoustic impedance (AI) is generally divided into background AI and relative AI for linear inversion. In practice, the intermediate frequency components of the AI model are generally poorly reconstructed, so the estimated AI will suffer from an error caused by the frequency gap. To remedy this error, a priori information should be incorporated to narrow down the gap. With the knowledge that underground reflectivity was sparse, we solved an L1 norm constrained problem to extend the bandwidth of the reflectivity section, and an absolute AI model was then estimated with broadband reflectivity section and given background AI. Conventionally, the AI model is regularized with the total variation (TV) norm because of its blocky feature. However, the first-order TV norm that leads to piecewise-constant solutions will cause staircase errors in slanted and smooth regions in the inverted AI model. To better restore the smooth variation while preserving the sharp geological structure of the AI model, we introduced a second-order extension of the first-order TV norm and inverted the absolute AI model with combined first- and second-order TV regularizations. The algorithm used to solve the optimization problem with the combined TV constraints was derived based on split-Bregman iterations. Numerical experiments that were tested on the Marmousi AI model and 2D stacked field data illustrated the effectiveness of the sparse constraint with respect to shrinking the frequency gaps and proved that the proposed combined TV norms had better performances than those with conventional first-order TV norms.

SUMMARY In the case of long-range propagation of forward scattering, due to the accumulation of phase changes caused by the velocity perturbations, the validity of the Born approximation will be violated. In contrast, the phase-change accumulation can be handled by the Rytov approximation, which has been widely used for long-distance propagation with only forward scattering or small-angle scattering involved. However, the weak scattering assumption (i.e. small velocity perturbation) in the Rytov approximation limits its scope of application. To address this problem, we analyse the integral kernel of the Rytov transform using the Wentzel-Kramers-Brillouin-Jeffreys (WKBJ) approximation and we demonstrate that the integral kernel is a function of velocity perturbation and scattering angle. By applying a small scattering angle approximation, we show that the phase variation has a linear relationship with the slowness perturbation, no matter how strong the magnitude of perturbation is. Therefore, the new integral equation is then referred to as the generalized Rytov approximation (GRA) because it overcomes the weak scattering assumption of the Rytov approximation. To show the limitations of the Rytov approximation and the advantages of the proposed GRA method, first we design a two-layer model and we analytically calculate the errors introduced by the small scattering angle assumption using plane wave incidence. We show that the phase (traveltime) variations predicted by the GRA are always more accurate than the Rytov approximation. Particularly, the GRA produces accurate phase variations for the normal incident plane wave regardless of the magnitude of velocity perturbation. Numerical examples using Gaussian anomaly models demonstrate that the scattering angle has a crucial impact on the accuracy of the GRA. If the small scattering angle assumption holds, the GRA can produce an accurate phase approximation even if the velocity perturbation is very strong. On the contrary, both the first-order Rytov approximation and the GRA fail to get satisfying results when the scattering angle is large enough. The proposed GRA method has the potential to be used for traveltime modelling and inversion for large-scale strong perturbation media.

We have developed an automatic traveltime inversion (ATI) method to estimate the macrovelocity model from reflection seismic data. First, we extract the kinematic information (i.e., source/receiver ray parameters, traveltime, and source/receiver coordinates) of locally coherent events using a sparse-decomposition method. And then we evaluate a new strategy to calculate the reflection traveltime residual based on a ray-intersection criterion, eliminating the influence of seismic amplitude to the estimation of the traveltime residual. The velocity model can be updated iteratively by minimizing the traveltime residual functional with a gradient-based method. To obtain a smooth gradient free of artifacts, we first estimate the high-wavenumber components of the functional gradient with a total variation (TV) regularization method and then subtract it from the full gradient. Because the reflection traveltime residual calculation and velocity update are fully automated procedures, the proposed traveltime inversion method is referred to as ATI. We determine with 2D synthetic and field examples that ATI does not need a good starting model. Furthermore, it requires neither low-frequency seismic data nor long-offset acquisition. Nevertheless, the proposed traveltime residual calculation strategy is only valid for the 2D case, which limits its 3D applicability. We explore a possible solution for 3D extension.

For linear noise such as seismic ground roll, 3-D frequency-wavenumber-wavenumber (3-D FKK) domain filtering suppression is better than 2-D frequency-wavenumber (F-K) domain filtering. In recent years, with the continuous development of computer processing speed and memory capacity, high-density data acquisition in seismic exploration has been widely applied in the hydrocarbon industry, opening up the application of 3-D FKK filtering methods. We applied the 3-D FKK filtering software to a 3-D high-density onshore seismic field dataset from a coal mine in western China. The case study demonstrates that the linearity of the noise in the field data is better represented by constructing the single shot records as a minimum dataset. Both theoretical synthetic models and the 3-D high-density onshore seismic field data numerical filtering experiments demonstrate that the feasibility of 3-D FKK filtering.

Subsurface rocks (e.g. shale) may induce seismic anisotropy, such as transverse isotropy. Traveltime computation is an essential component of depth imaging and tomography in transversely isotropic media. It is natural to compute the traveltime using the wavefront marching method. However, tracking the 3D wavefront is expensive, especially in anisotropic media. Besides, the wavefront marching method usually computes the traveltime using the eikonal equation. However, the anisotropic eikonal equation is highly non-linear and it is challenging to solve. To address these issues, we present a layer-by-layer wavefront marching method to compute the P-wave traveltime in 3D transversely isotropic media. To simplify the wavefront tracking, it uses the traveltime of the previous depth as the boundary condition to compute that of the next depth based on the wavefront marching. A strategy of traveltime computation is designed to guarantee the causality of wave propagation. To avoid solving the non-linear eikonal equation, it updates traveltime along the expanding wavefront by Fermat’s principle. To compute the traveltime using Fermat’s principle, an approximate group velocity with high accuracy in transversely isotropic media is adopted to describe the ray propagation. Numerical examples on 3D vertical transverse isotropy and tilted transverse isotropy models show that the proposed method computes the traveltime with high accuracy. It can find applications in modelling and depth migration.

Cadmium (Cd) is a major abiotic stressor that affects plant growth and reduces the productivity of field crops. Here, we examined the ultrastructural, physiological, and molecular changes in three wheat cultivars [Sumai 3, Jingdong 8 (JD 8), and Nannong 9918 (9918)] in response to different concentrations of Cd (0, 10, 50, and 100 μM) in 1/4 Hoagland nutrient solution. The results showed that JD 8 contained the lowest shoot Cd content and the highest root Cd content among the three cultivars at higher Cd concentrations and so JD 8 was proposed to be a relatively Cd-tolerant cultivar. Next, the stress responses of JD 8 and 9918 were compared. Cadmium reduced root growth and size and number of the leaves, inhibited root hair development, and promoted leaf cell death. The result of trypan blue staining showed that the dead leaf cells induced by Cd stress gradually emerged in the xylem, supporting the hypothesis that cell death could restrict Cd transport. The Cd-induced deterioration of the leaf ultrastructure led to the complete disorganization of the chloroplasts, which had lower amounts of transitory starch and an increased number of osmiophilic granules compared to those in the untreated controls. Autophagy-related genes and autophagy in the leaves were induced by Cd stress. At the same concentration and Cd treatment time, the Cd-tolerant genotype JD 8 exhibited less toxic symptoms compared to the Cd-sensitive genotype 9918. The results of this study provide insights into the ultrastructural and physiological damages induced by Cd stress, which may help in selecting Cd-tolerant wheat cultivars.

Three-dimensional seismic survey is widely applied, but 3-D filtering technology has yet to be fully utilized for the analysis of seismic field data. The common approach is to first filter inline and then crossline. However, an effective 3-D filtering method is expected to eliminate coherent noise, such as the ground roll. We propose a 3-D Butterworth filtering method in the time-space domain. Firstly, a Butterworth-type filter in the frequency-wavenumber-domain is designed to suppress the linear noise with a specific apparent velocity. Secondly, transforming this filter to the time and space domain yields 3-D partial differential equations (PDEs), which are applied to suppress the linear noise. Factorizing the finite-difference equations in a different direction other than decreasing the 3-D PDEs to 2-D PDEs produces a highly accurate and efficient algorithm. Designing the 3-D Butterworth filter, selecting the filtering parameters, and showing its application to synthetic data and a 3-D high-density onshore seismic field data from a region in western China are discussed in detail. Numerical experiments with 3-D high-density onshore seismic field data demonstrate that it is more effective than the 3-D frequency-wavenumber-wavenumber (FKK) filtering method.

Autophagy is a highly conserved eukaryotic degradation process during which bulk cytoplasmic materials are transported by double-membrane autophagosomes into the vacuole for degradation. Methods of monitoring autophagy are indispensable in studying the mechanism and functions of autophagy. AuTophaGy-related protein 8 (ATG8) functions in autophagosome assembly by decorating on autophagic membranes, and the inner membrane-bound ATG8 proteins enter the vacuole via active autophagy flux. Fluorescence protein (FP)-tagged forms of ATG8 have been explored as visual markers to monitor autophagy in animals and several plant species. Here, we evaluated and modified this FP-ATG8-based autophagy monitoring method in wheat (Triticum aestivum L.) by fluorescence observation of green fluorescence protein (GFP)-tagged and Discosoma red fluorescent protein (DsRED)-tagged forms of one wheat ATG8, TaATG8h, in wheat mesophyll protoplasts. Under a nutrient-starvation condition, punctate GFP/DsRED- TaATG8h fluorescence representing autophagosomes was clearly observed in the cytoplasm. The accumulation of GFP-TaATG8h-labeled autophagosomes was impaired by the autophagosome biogenesis inhibitor 3-methyladenine but enhanced by the vacuolar degradation inhibitor concanamycin A. In addition, accumulated spreading fluorescence was observed in the vacuole, indicating active autophagy fluxes which led to continuous degradation of GFP/DsRED-TaATG8h fusions and release of protease-tolerant free GFP/DsRED proteins in the vacuole. To observe FP-tagged TaATG8h in other types of wheat cell, we also expressed GFP-TaATG8h in leaf epidermal cells. Consistent with its performance in protoplasts, GFP-TaATG8h showed punctate fluorescence representing autophagosomes in leaf epidermal cells. Taken together, our results proved the feasibility of using FP-tagged ATG8 to monitor both autophagosome accumulation and autophagy flux in living wheat cells.

A fractured area, such as a fault area, usually induces orthorhombic anisotropy. Ignoring orthorhombic anisotropy may degrade the subsurface image by creating a well mistie and blurring the image. Traveltime computation is essential for many processing techniques, such as depth imaging and tomography. Solving the ray-tracing system and eikonal equation are two popular methods for traveltime computation in isotropic media. However, because the ray-tracing system becomes complex and the eikonal equation becomes highly nonlinear, their applications in orthorhombic media become complex. We have developed an alternative 3D traveltime computation method in orthorhombic media based on dynamic programming. To avoid solving the complex ray-tracing system and nonlinear eikonal equation, it adopts an explicitly expressed group velocity from the moveout approximation to describe the propagation of the wavepath and computes the traveltime by Fermat’s principle. Similar to depth extrapolation, it computes the traveltime from one depth to the next depth and does not suffer from a shadow zone. Besides, three strategies of traveltime computation are proposed to deal with different geologic scenarios. Because classic dynamic programming (i.e., the first strategy) computes all possible wavepaths (i.e., 24 wavepaths) across one spatial location, it may be computationally intensive. Based on the idea of wavefield decomposition (e.g., upgoing and downgoing), the second and third strategies simplify the traveltime computation and reduce the computational cost. Numerical examples on the vertical and tilted orthorhombic models indicate that the traveltime contour obtained by our method matches well with the wavefront extrapolated from the wave equation. Our method can be applied in depth imaging and tomography.