Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.

Niemann-Pick A/B Disease in a 13-Year-Old Child and Review of the Literature

Niemann-Pick disease (NPD) refers to a group of patients who have varying degrees of lipid storage and foam cell infiltration in tissues, as well as overlapping clinical features, including hepatosplenomegaly, insufficiency pulmonary and/or central nervous system (CNS). Thanks to the pioneering work of Roscoe Brady and colleagues, we now know that there are two distinct metabolic abnormalities that explain NPD. The first is due to the deficient activity of the acid sphingomyelinase enzyme (ASM;NPD “types A and B”), and the second is due to defective functioning in the transport of cholesterol (NPD “type C”). We report the case of a 13-year-old adolescent diagnosed with Niemann-Pick A/B disease.

A regression approach for seismic first-break picking

The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

First-Arrival Picking Method for Active Source Data with Ocean Bottom Seismometers Based on Spatial Waveform Variation Characteristics

The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.

矩阵值Caratheodory函数广义块Pick矩阵的秩不变性

Lasarow推导出矩阵值Caratheodory函数的第一、第二型广义块Pick矩阵及其变型的秩不变性.这些矩阵由同一个Caratheodory函数的值与它的直到某阶的导数值确定.利用文献中提出的块Toeplitz向量方法,该文断言,这些块矩阵的秩分别相关并重合于具有秩不变性的块Toeplitz矩阵的秩,从而改进了这两类广义块Pick矩阵的秩不变性结论的证明.

Study on the Optimum Picking Time of Callicarpa kwangtungensis

[Objective] This study was conducted to compare total contents of poliumoside and forsythoside B from Callicarpa kwangtungensis Chun in Qiandongnan Miao and Dong Autonomous Prefecture collected in different seasons, which could provide reference for its deep development and utilization. [Methods] Poliumoside and forsythoside B were measured according to the pharmacopoeia standard in the middle of each month in 2014, and the yield of C. kwangtungensis was simultaneously evaluated. All these results provided data reference for the determination of suitable picking time for C. kwangtungensis. [Results] The results showed the content of poliumoside and forsythoside B in C. kwangtungensis was the highest in November, and the content of the medicinal material in August was over eight times higher than the pharmacopoeia standard, besides at this month the yield was the highest during the year. Comprehensively, mid October is the optimum picking time for C. kwangtungensis in Taijiang. [Conclusion] Dynamic variations of content of poliumoside and forsythoside B in and yield of C. kwangtungensis were investigated, which would significantly benefit production of C. kwangtungensis in Qiandongnan Miao and Dong Autonomous Prefecture.

Research on the cutting force of a pick

Cutting tests were done using a test bed designed to measure pick cutting forces when cutting coal and rock.The test equipment has a drum with two starting helical vanes.Cutting forces on a pick were measured as a function of coal compressive strength,pick carbide tip diameter and the cutting depth per drum revolution.The results show that the cutting force is linearly related to the compressive strength.The relationship between the cutting force and both the carbide tip diameter and the cutting depth are exponential.Fluctuation in the cutting force does not increase with coal compressive strength but it has a linear relationship to tip diameter.A plot of cutting force fluctuations versus the cutting depth follows a sigmoidal curve.Based on the analysis of these test results a theoretical basis is supplied for design and effective use of shearer drums.

SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces

Manually picking regularly and densely distributed first breaks(FBs)are critical for shallow velocitymodel building in seismic data processing.However,it is time consuming.We employ the fullyconvolutional Seg Net to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces.Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs,we can obtain a welltrained Seg Net model.When any unseen gather including the one with irregular trace spacing is inputted,the Seg Net can output the probability distribution of different categories for waveform classification.Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background.Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer Seg Net can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones,even when the proportion of randomly missing traces reaches50%,21 traces are missing consecutively,or traces are missing regularly.

Active Source Seismic Identification and Automatic Picking of the P-wave First Arrival Using a Convolutional Neural Network

In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.

USTC-Pickers:a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.

Applying the Method for Solving Traveling Salesman Problem Based on Backtracking Algorithm to Order Picking

In the distribution center, the way of order picking personnel to pick goods has two kinds: single picking and batch picking. Based on the way of the single picking and assumed warehouse model, in order to reduce the walking path of order picking, the order picking problem is transformed into the traveling salesman problem in this paper. Based on backtracking algorithm, the order picking path gets optimized. Finally verifing the optimization method under the environment of VC++6.0, order picking path in the warehouse model get optimized, and compared with the traditional order picking walking paths. The results show that in small and medium-sized warehouse, the optimization method proposed in this paper can reduce order picking walking path and improve the work efficiency as well as reduce the time cost.

Pick-up strategies for and electrical characterization of ZnO nanowires with a SEM-based nanomanipulator

Because of their unique mechanical and electrical properties,zinc oxide(ZnO)nanowires are used widely in microscopic and nanoscopic devices and structures,but characterizing them remains challenging.In this paper,two pick-up strategies are proposed for characterizing the electrical properties of ZnO nanowires using SEM equipped with a nanomanipulator.To pick up nanowires efficiently,direct sampling is compared with electrification fusing,and experiments show that direct sampling is more stable while electrification fusing is more efficient.ZnO nanowires have cut-off properties,and good Schottky contact with the tungsten probes was established.In piezoelectric experiments,the maximum piezoelectric voltage generated by an individual ZnO nanowire was 0.07 V,and its impedance decreased with increasing input signal frequency until it became stable.This work offers a technical reference for the pick-up and construction of nanomaterials and nanogeneration technology.

MMP15、PICK1基因在山羊睾丸中的表达研究

MMP15、PICK1基因均是哺乳动物性腺组织中影响性腺生理功能正常发挥的重要影响因子,但它们在黔北麻羊睾丸组织中的表达规律尚不明晰。为研究黔北麻羊不同月龄的睾丸中MMP15、PICK1基因的表达情况,本试验以黔北麻羊为研究对象,通过qRT-PCR技术分别检测MMP15与PICK1基因在1、3、6、9、12月龄的黔北麻羊睾丸中的表达水平并建立基因表达的标准曲线。结果显示:MMP15、PICK1基因在黔北麻羊不同月龄睾丸组织中均有表达;MMP15基因在黔北麻羊不同月龄睾丸中的表达量从高到低排列为:12月龄>1月龄>9月龄>3月龄>6月龄;12月龄与1月龄的表达差异不显著(P>0.05),二者表达量均极显著高于3月龄、6月龄、9月龄(P<0.01);9月龄表达显著高于3月龄(P<0.05),极显著高于6月龄(P<0.01);3月龄表达极显著高于6月龄(P<0.01)。PICK1基因在黔北麻羊不同月龄睾丸中的表达量从高到低排列分别是:3月龄>6月龄>12月龄>9月龄>1月龄;3、6月龄其表达量极显著高于9、12月龄(P<0.01);1月龄与其他几个月龄相比,其表达量均极显著低于其他月龄(P<0.01)。结合黔北麻羊的生理机能发育情况,黔北麻羊在6月龄时已经基本达到性成熟,所以此时睾丸内细胞活动增强,睾丸机能活跃。本次试验结果表明,黔北麻羊睾丸生精功能的正常发挥或与MMP15及PICK1基因的表达量有关。

P wave onset time picking with the B-spline biorthogonal wavelet

The seismic wave consists of many seismic phases, which contain rich geophysical information from the hypocenter, medium of seismic wave passing through and so on. It is very important to detect and pick these seismic phases for understanding the mechanism of earthquake, the Earth structure and property of seismic waves. In order to reduce or avoid the loss resulted from the earthquake, one of the important goals of seismic event detecting is to obtain its related information before and after it occurs. Because of the particularity of P wave and S wave the seismic event detecting focuses on distinguishing P and S waves and picking their onset time, it has been becoming one of the research hotspots for many geophysicists to pick the P and S wave arrival accurately and effectively.

THE COMPARATIVE STUDY OF "THE SHIP OF DEATH" AND "AFTER APPLE—PICKING"——The Similiar Notes

After Apple-picking is one of the representatives of Robert’ Forst’s masterpieces. It was published in 1914. The speaker in this poem is a New England farmer.The time is in the late autumn.The poem begins with the farmer’s description of his working tool with loving exactness____a "long twopointed ladder","a barrel".Besides these,the farmer has also introduced for us apples on the bough, a drinking trough, the frosty grass,the

Comparative study of The ship of Death And After Apple—picking——The Differences Under the Theme of Deach

Though Frost and Laurence carry some similiar notes in the ar poems,this can not obscure their differenccs. Frist.Frost’s After APPle-picking can be regarded as the exampleof that dominant mode of the oppening 25 years of the century.It is aquietly reflective pome of the actual New England farmer’s life.HereForst takes the mask of a New England farmer and speaks to

Truncating PICK1 Variant Identified in Azoospermia Affected Mitochondrial Dysfunction in Knockout Mice

Objective The protein interacting with C kinase 1(PICK1)plays a critical role in vesicle trafficking,and its deficiency in sperm cells results in abnormal vesicle trafficking from Golgi to acrosome,which eventually disrupts acrosome formation and leads to male infertility.Methods An azoospermia sample was filtered,and the laboratory detection and clinical phenotype indicated typical azoospermia in the patient.We sequenced all of the exons in the PICK1 gene and found that there was a novel homozygous variant in the PICK1 gene,c.364delA(p.Lys122SerfsX8),and this protein structure truncating variant seriously affected the biological function.Then we constructed a PICK1 knockout mouse model using clustered regularly interspaced short palindromic repeat cutting technology(CRISPRc).Results The sperm from PICK1 knockout mice showed acrosome and nucleus abnormalities,as well as dysfunctional mitochondrial sheath formation.Both the total sperm and motility sperm counts were decreased in the PICK1 knockout mice compared to wild-type mice.Moreover,the mitochondrial dysfunction was verified in the mice.These defects in the male PICK1 knockout mice may have eventually led to complete infertility.Conclusion The c.364delA novel variant in the PICK1 gene associated with clinical infertility,and pathogenic variants in the PICK1 may cause azoospermia or asthenospermia by impairing mitochondrial function in both mice and humans.

Pregnancy outcomes following supplementation of single dose GnRH agonist to sustain the luteal phase in antagonist fresh embryo transfer cycles:A multicentric prospective cohort study

Objective:To determine whether a single dose of gonadotropin-releasing hormone(GnRH)agonist administered subcutaneously in addition to the regular progesterone supplementation could provide a better luteal support in antagonist protocol fresh embryo transfer cycles.Methods:This prospective,multicentric,cohort study included total 140 women,70 in each group.Controlled ovarian stimulation was carried out as per fixed GnRH antagonist protocol.The trigger was given with hCG.In vitro fertilization/intracytoplasmic sperm injection(IVF/ICSI)was performed and day-3 embryos were transferred.Patients were divided into groups 1 and 2 based on computer generated randomization sheet.Six days following oocyte retrieval,group 1 received 0.2 mg decapeptyl subcutaneously in addition to regular progesterone support while group 2 received progesterone only.Luteal support was given for 14 days to both groups;if pregnancy was confirmed luteal support was continued till 12 weeks of gestation.The clinical pregnancy rate was the primary outcome.The implantation rate,miscarriage rate,live birth delivery rate,and multiple pregnancy rates were the secondary outcomes.Results:A total of 140 patients were analysed,70 in each group.Clinical pregnancy rates(47.1%vs.35.7%;P=0.17),implantation rates(23.4%vs.18.1%,P=0.24),live birth delivery rates(41.4%vs.27.1%,P=0.08),and multiple pregnancy rates(21.2%vs.16.0%,P=0.74)were higher in group 1 than in group 2.Group 1 had a lower miscarriage rate than group 2(5.7%vs.8.6%;P=0.75).However,these differences were not statistically significant between the two groups.Conclusions:Administration of a single dose of GnRH agonist in addition to regular natural micronized vaginal progesterone as luteal support in GnRH antagonist protocol cycles marginally improves implantation rates,clinical pregnancy rates,and live birth delivery rates.However,more studies with higher sample sizes are needed before any conclusive statements about GnRH agonist as luteal phase support can be made.