Calculation of microscopic nuclear level densities based on covariant density functional theory

In this study,a microscopic method for calculating the nuclear level density(NLD)based on the covariant density functional theory(CDFT)is developed.The particle-hole state density is calculated by a combinatorial method using single-particle level schemes obtained from the CDFT,and the level densities are then obtained by considering collective effects such as vibration and rotation.Our results are compared with those of other NLD models,including phenomenological,microstatisti-cal and nonrelativistic Hartree–Fock–Bogoliubov combinatorial models.This comparison suggests that the general trends among these models are essentially the same,except for some deviations among the different NLD models.In addition,the NLDs obtained using the CDFT combinatorial method with normalization are compared with experimental data,including the observed cumulative number of levels at low excitation energies and the measured NLDs.The CDFT combinatorial method yields results that are in reasonable agreement with the existing experimental data.

Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Grain yield and N uptake of maize in response to increased plant density under reduced water and nitrogen supply conditions

The development of modern agriculture requires the reduction of water and chemical N fertilizer inputs.Increasing the planting density can maintain higher yields,but also consumes more of these restrictive resources.However,whether an increased maize density can compensate for the negative effects of reduced water and N supply on grain yield and N uptake in the arid irrigated areas remains unknown.This study is part of a long-term positioning trial that started in 2016.A split-split plot field experiment of maize was implemented in the arid irrigated area of northwestern China in 2020 to 2021.The treatments included two irrigation levels:local conventional irrigation reduced by 20%(W1,3,240 m^(3)ha^(-1))and local conventional irrigation(W2,4,050 m^(3)ha^(-1));two N application rates:local conventional N reduced by 25%(N1,270 kg ha^(-1))and local conventional N(360 kg ha^(-1));and three planting densities:local conventional density(D1,75,000 plants ha^(-1)),density increased by 30%(D2,97,500 plants ha-1),and density increased by 60%(D3,120,000 plants ha^(-1)).Our results showed that the grain yield and aboveground N accumulation of maize were lower under the reduced water and N inputs,but increasing the maize density by 30% can compensate for the reductions of grain yield and aboveground N accumulation caused by the reduced water and N supply.When water was reduced while the N application rate remained unchanged,increasing the planting density by 30% enhanced grain yield by 13.9% and aboveground N accumulation by 15.3%.Under reduced water and N inputs,increasing the maize density by 30% enhanced N uptake efficiency and N partial factor productivity,and it also compensated for the N harvest index and N metabolic related enzyme activities.Compared with W2N2D1,the N uptake efficiency and N partial factor productivity increased by 28.6 and 17.6%under W1N1D2.W1N2D2 had 8.4% higher N uptake efficiency and 13.9% higher N partial factor productivity than W2N2D1.W1N2D2 improved urease activity and nitrate reductase activity by 5.4% at the R2(blister)stage and 19.6% at the V6(6th leaf)stage,and increased net income and the benefit:cost ratio by 22.1 and 16.7%,respectively.W1N1D2 and W1N2D2 reduced the nitrate nitrogen and ammoniacal nitrogen contents at the R6 stage in the 40-100 cm soil layer,compared with W2N2D1.In summary,increasing the planting density by 30% can compensate for the loss of grain yield and aboveground N accumulation under reduced water and N inputs.Meanwhile,increasing the maize density by 30% improved grain yield and aboveground N accumulation when water was reduced by 20% while the N application rate remained constant in arid irrigation areas.

Modeling of Micropores Drilling Force for Printed Circuit Board Micro-holes Based on Energy Method

The quality of printed circuit board(PCB)micro-hole processing directly determines the stability of the inner and outer circuit connections.Micro-hole drilling technology is a typical method for PCB micro-hole processing.The problem of optimal control of its drilling force is one of the main factors affecting the quality of micro-hole machining.To address this problem,the thrust forces and torques in PCB drilling were first modeled and analyzed,and the corresponding prediction models were established.The drilling force analysis was carried out through the micro-hole drilling experiment,the specific cutting energy under different feed rates was calculated,the influence of the size effect was clarified,and the accuracy of the prediction model was verified.The result shows that during the drilling of glass fiber cloth,changes in the material removal mechanism are induced as the feed per revolution is varied.When the feed per revolution is less than the tool edge radius,the glass fiber is not cut by the main cutting edge,but is crushed and broken.When the feed per revolution is greater than the radius of the tool edge,the glass fiber is cut by the main cutting edge.At the same time,the established analytical model can accurately reflect the influence of the size effect on the drilling torque in PCB micro-hole drilling,and the error is within 10%.This method has certain practical application value in controlling PCB micro hole processing quality.

A facile strategy for tuning the density of surface-grafted biomolecules for melt extrusion-based additive manufacturing applications

Melt extrusion-based additive manufacturing(ME-AM)is a promising technique to fabricate porous scaffolds for tissue engi-neering applications.However,most synthetic semicrystalline polymers do not possess the intrinsic biological activity required to control cell fate.Grafting of biomolecules on polymeric surfaces of AM scaffolds enhances the bioactivity of a construct;however,there are limited strategies available to control the surface density.Here,we report a strategy to tune the surface density of bioactive groups by blending a low molecular weight poly(ε-caprolactone)5k(PCL5k)containing orthogonally reactive azide groups with an unfunctionalized high molecular weight PCL75k at different ratios.Stable porous three-dimensional(3D)scaf-folds were then fabricated using a high weight percentage(75 wt.%)of the low molecular weight PCL 5k.As a proof-of-concept test,we prepared films of three different mass ratios of low and high molecular weight polymers with a thermopress and reacted with an alkynated fluorescent model compound on the surface,yielding a density of 201-561 pmol/cm^(2).Subsequently,a bone morphogenetic protein 2(BMP-2)-derived peptide was grafted onto the films comprising different blend compositions,and the effect of peptide surface density on the osteogenic differentiation of human mesenchymal stromal cells(hMSCs)was assessed.After two weeks of culturing in a basic medium,cells expressed higher levels of BMP receptor II(BMPRII)on films with the conjugated peptide.In addition,we found that alkaline phosphatase activity was only significantly enhanced on films contain-ing the highest peptide density(i.e.,561 pmol/cm^(2)),indicating the importance of the surface density.Taken together,these results emphasize that the density of surface peptides on cell differentiation must be considered at the cell-material interface.Moreover,we have presented a viable strategy for ME-AM community that desires to tune the bulk and surface functionality via blending of(modified)polymers.Furthermore,the use of alkyne-azide“click”chemistry enables spatial control over bioconjugation of many tissue-specific moieties,making this approach a versatile strategy for tissue engineering applications.

Flexible,high-density,laminated ECoG electrode array for high spatiotemporal resolution foci diagnostic localization of refractory epilepsy

High spatiotemporal resolution brain electrical signals are critical for basic neuroscience research and high-precision focus diagnostic localization,as the spatial scale of some pathologic signals is at the submillimeter or micrometer level.This entails connecting hundreds or thousands of electrode wires on a limited surface.This study reported a class of flexible,ultrathin,highdensity electrocorticogram(ECoG)electrode arrays.The challenge of a large number of wiring arrangements was overcome by a laminated structure design and processing technology improvement.The flexible,ultrathin,high-density ECoG electrode array was conformably attached to the cortex for reliable,high spatial resolution electrophysiologic recordings.The minimum spacing between electrodes was 15μm,comparable to the diameter of a single neuron.Eight hundred electrodes were prepared with an electrode density of 4444 mm^(-2).In focal epilepsy surgery,the flexible,high-density,laminated ECoG electrode array with 36 electrodes was applied to collect epileptic spike waves inrabbits,improving the positioning accuracy of epilepsy lesions from the centimeter to the submillimeter level.The flexible,high-density,laminated ECoG electrode array has potential clinical applications in intractable epilepsy and other neurologic diseases requiring high-precision electroencephalogram acquisition.

Method to measure tree-ring width,density,elemental composition,and stable carbon and oxygen isotopes using one sample

Tree-ring width(RW),density,elemental com-position,and stable carbon and oxygen isotope(δ^(13)C,δ^(18)O)are widely used as proxies to assess climate change,ecology,and environmental pollution;however,a specific pretreat-ment has been needed for each proxy.Here,we developed a method by which each proxy can be measured in the same sample.First,the sample is polished for ring width meas-urement.After obtaining the ring width data,the sample is cut to form a 1-mm-thick wood plate.The sample is then mounted in a vertical sample holder,and gradually scanned by an X-ray beam.Simultaneously,the count rates of the fluorescent photons of elements(for chemical characteriza-tion)and a radiographic grayscale image(for wood density)are obtained,i.e.the density and the element content are obtained.Then,cellulose is isolated from the 1-mm wood plate by removal of lignin,and hemicellulose.After producing this cellulose plate,cellulose subsamples are separated by knife under the microscope for inter-annual and intra-annual stable carbon and oxygen isotope(δ^(13)C,δ^(18)O)analysis.Based on this method,RW,density,elemental composition,δ^(13)C,and δ^(18)O can be measured from the same sample,which reduces sample amount and treatment time,and is helpful for multi-proxy comparison and combination research.

11.2 W/mm power density AlGaN/GaN high electron-mobility transistors on a GaN substrate

In this letter,high power density AlGaN/GaN high electron-mobility transistors(HEMTs)on a freestanding GaN substrate are reported.An asymmetricΓ-shaped 500-nm gate with a field plate of 650 nm is introduced to improve microwave power performance.The breakdown voltage(BV)is increased to more than 200 V for the fabricated device with gate-to-source and gate-to-drain distances of 1.08 and 2.92μm.A record continuous-wave power density of 11.2 W/mm@10 GHz is realized with a drain bias of 70 V.The maximum oscillation frequency(f_(max))and unity current gain cut-off frequency(f_(t))of the AlGaN/GaN HEMTs exceed 30 and 20 GHz,respectively.The results demonstrate the potential of AlGaN/GaN HEMTs on freestanding GaN substrates for microwave power applications.

Electron density measurement by the three boundary channels of HCOOH laser interferometer on the HL-3 tokamak

Far-infrared(FIR)interferometer is widely used to measure the electron density in the magnetically confined fusion plasma devices.A new FIR laser interferometer with a total of 13 channels(8 horizontal channels and 5 oblique channels)is under development on the HL-3tokamak by using the formic-acid laser(HCOOH,f=694 GHz).In order to investigate the boundary electron density activity during the divertor discharge,three horizontal interferometry channels located at Z=-97,-76,76.5 cm have been successfully developed on HL-3 in 2023,and put into operation in recent experimental campaign,with a time resolution of<1.0μs and lineintegrated electron density resolution of~7.0×10^(16) m^(-2).This paper mainly focuses on the optical design of the three-channel interferometry system,as well as optical elements and recent experimental result on HL-3.

The effect of topographic density variations on the geoid and orthometric heights in Hong Kong

Utilizing the adopted average topographic density of 2670 kg/m^(3)in the reduction of gravity anomalies introduces errors attributed to topographic density variations,which consequently affect geoid modeling accuracy.Furthermore,the mean gravity along the plumbline within the topography in the definition of Helmert orthometric heights is computed approximately by applying the Poincar e-Prey gravity reduction where the topographic density variations are disregarded.The Helmert orthometric heights of benchmarks are then affected by errors.These errors could be random or systematic depending on the specific geological setting of the region where the leveling network is physically established and/or the geoid model is determined.An example of systematic errors in orthometric heights can be given for large regions characterized by sediment or volcanic deposits,the density of which is substantially lower than the adopted topographic density used in Helmert's definition of heights.The same applies to geoid modeling errors.In this study,we investigate these errors in the Hong Kong territory,where topographic density is about 20%lower than the density of 2670 kg/m^(3).We use the digital rock density model to estimate the effect of topographic density variations on the geoid and orthometric heights.Our results show that this effect on the geoid and Helmert orthometric heights reach maxima of about 2.1 and 0.5 cm,respectively.Both results provide clear evidence that rock density models are essential in physical geodesy applications involving gravimetric geoid modeling and orthometric height determination despite some criticism that could be raised regarding the reliability of these density models.However,in regions dominated by sedimentary and igneous rocks,the geological information is essential in these applications because topographic densities are substantially lower than the average density of 2670 kg/m^(3),thus introducing large systematic errors in geoid and orthometric heights.

Local thermal conductivity of inhomogeneous nano-fluidic films:A density functional theory perspective

Combining the mean field Pozhar-Gubbins(PG)theory and the weighted density approximation,a novel method for local thermal conductivity of inhomogeneous fluids is proposed.The correlation effect that is beyond the mean field treatment is taken into account by the simulation-based empirical correlations.The application of this method to confined argon in slit pore shows that its prediction agrees well with the simulation results,and that it performs better than the original PG theory as well as the local averaged density model(LADM).In its further application to the nano-fluidic films,the influences of fluid parameters and pore parameters on the thermal conductivity are calculated and investigated.It is found that both the local thermal conductivity and the overall thermal conductivity can be significantly modulated by these parameters.Specifically,in the supercritical states,the thermal conductivity of the confined fluid shows positive correlation to the bulk density as well as the temperature.However,when the bulk density is small,the thermal conductivity exhibits a decrease-increase transition as the temperature is increased.This is also the case in which the temperature is low.In fact,the decrease-increase transition in both the small-bulk-density and low-temperature cases arises from the capillary condensation in the pore.Furthermore,smaller pore width and/or stronger adsorption potential can raise the critical temperature for condensation,and then are beneficial to the enhancement of the thermal conductivity.These modulation behaviors of the local thermal conductivity lead immediately to the significant difference of the overall thermal conductivity in different phase regions.

Evolutionary history shapes variation of wood density of tree species across the world

The effect of evolutionary history on wood density variation may play an important role in shaping variation in wood density,but this has largely not been tested.Using a comprehensive global dataset including 27,297 measurements of wood density from 2621 tree species worldwide,we test the hypothesis that the legacy of evolutionary history plays an important role in driving the variation of wood density among tree species.We assessed phylogenetic signal in different taxonomic(e.g.,angiosperms and gymnosperms)and ecological(e.g.,tropical,temperate,and boreal)groups of tree species,explored the biogeographical and phylogenetic patterns of wood density,and quantified the relative importance of current environmental factors(e.g.,climatic and soil variables)and evolutionary history(i.e.,phylogenetic relatedness among species and lineages)in driving global wood density variation.We found that wood density displayed a significant phylogenetic signal.Wood density differed among different biomes and climatic zones,with higher mean values of wood density in relatively drier regions(highest in subtropical desert).Our study revealed that at a global scale,for angiosperms and gymnosperms combined,phylogeny and species(representing the variance explained by taxonomy and not direct explained by long-term evolution process)explained 84.3%and 7.7%of total wood density variation,respectively,whereas current environment explained 2.7%of total wood density variation when phylogeny and species were taken into account.When angiosperms and gymnosperms were considered separately,the three proportions of explained variation are,respectively,84.2%,7.5%and 6.7%for angiosperms,and 45.7%,21.3%and 18.6%for gymnosperms.Our study shows that evolutionary history outpaced current environmental factors in shaping global variation in wood density.

A detailed rock density model of the Hong Kong territories

We used the geological map and published rock density measurements to compile the digital rock density model for the Hong Kong territories.We then estimated the average density for the whole territory.According to our result,the rock density values in Hong Kong vary from 2101 to 2681 kg·m^(-3).These density values are typically smaller than the average density of 2670 kg·m^(-3),often adopted to represent the average density of the upper continental crust in physical geodesy and gravimetric geophysics applications.This finding reflects that the geological configuration in Hong Kong is mainly formed by light volcanic formations and lava flows with overlying sedimentary deposits at many locations,while the percentage of heavier metamorphic rocks is very low(less than 1%).This product will improve the accuracy of a detailed geoid model and orthometric heights.

Density functional theory study of B- and Si-doped carbons and their adsorption interactions with sulfur compounds

Understanding the adsorption interactions between carbon materials and sulfur compounds has far-reaching impacts,in addition to their well-known important role in energy storage and conversion,such as lithium-ion batteries.In this paper,properties of intrinsic B or Si single-atom doped,and B-Si codoped graphene(GR)and graphdiyne(GDY)were investigated by using density functional theory-based calculations,in which the optimal doping configurations were explored for potential applications in adsorbing sulfur compounds.Results showed that both B or Si single-atom doping and B-Si codoping could substantially enhance the electron transport properties of GR and GDY,improving their surface activity.Notably,B and Si atoms displayed synergistic effects for the codoped configurations,where B-Si codoped GR/GDY exhibited much better performance in the adsorption of sulfurcontaining chemicals than single-atom doped systems.In addition,results demonstrated that,after B-Si codoping,the adsorption energy and charge transfer amounts of GDY with sulfur compounds were much larger than those of GR,indicating that B-Si codoped GDY might be a favorable material for more effectively interacting with sulfur reagents.

Correlation between DKK-1 Level and Bone Density Status in Children on Maintenance Haemodialysis

Background: Renal osteodystrophy (ROD) is a bone disorder resulting from chronic kidney disease (CKD) and related metabolic diseases. Dickkopf-related protein-1 (DKK-1) is critical in regulating bone biology. This study aimed to evaluate the serum DKK-1 level as a bone marker in children with CKD who undergo regular hemodialysis (HD). Subjects and Methods: This case-control study involved 40 children with CKD on HD and 40 healthy children as controls. The study measured serum DKK-1 levels and performed a dual-energy X-ray absorptiometry scan (DEXA) in line with routine laboratory investigations. Results: There was a significant increase in the serum level of DKK-1 in the patient group compared to the control group. The DKK-1 levels were 2540.65 (2215.4 - 2909.2) pg/ml and 1110.45 (885.45 - 1527.65) pg/ml, respectively, with a p-value of less than 0.001. In the hemodialysis group, 25 patients (62.5%) had low bone mineral density (BMD) with a Z-score of under -2.0. Eighteen of these patients had low BMD in both the neck of the femur and lumbar spines. Additionally, there was a significant increase in serum DKK-1 level in patients with low BMD (2567.35 (2303.8 - 3108.1) pg/ml) compared to patients with normal BMD (2454 (1859 - 2820) pg/ml) (p = 0.041). There was also a significant positive correlation between DKK1 level and phosphorus, alkaline phosphatase, and Parathormone serum levels. In conclusion, the study indicates a clear correlation between DKK-1 and BMD in children undergoing maintenance hemodialysis. DKK1 is a promising biomarker for CKD-MBD.

An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban planning.However,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction.To address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data.The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift.Further,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population predictions.Comparative experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction assignments.The performance of this model in tasks pertaining to population prediction exhibits commendable results.

Synergistic effects of planting density and nitrogen fertilization on chlorophyll degradation and leaf senescence after silking in maize

Regulating planting density and nitrogen(N)fertilization could delay chlorophyll(Chl)degradation and leaf senescence in maize cultivars.This study measured changes in ear leaf green area(GLA_(ear)),Chl content,the activities of Chl a-degrading enzymes after silking,and the post-silking dry matter accumulation and grain yield under multiple planting densities and N fertilization rates.The dynamic change of GLA_(ear)after silking fitted to the logistic model,and the GLA_(ear) duration and the GLAearat 42 d after silking were affected mainly by the duration of the initial senescence period(T_(1))which was a key factor of the leaf senescence.The average chlorophyllase(CLH)activity was 8.3 times higher than pheophytinase activity and contributed most to the Chl content,indicating that CLH is a key enzyme for degrading Chl a in maize.Increasing density increased the CLH activity and decreased the Chl content,T1,GLAear,and GLA_(ear) duration.Under high density,appropriate N application reduced CLH activity,increased Chl content,prolonged T1,alleviated high-density-induced leaf senescence,and increased post-silking dry matter accumulation and grain yield.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Probabilistic seismic inversion based on physics-guided deep mixture density network

Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.