Microfacies models and sequence stratigraphic architecture of the Oligocene-Miocene Qom Formation, south of Qom City, Iran

The Oligocene-Miocene Qom Formation has different depositional models in the Central han, Sanandaj-Sirjan and Urunlieh-Dokhtar inagmatic arc provinces in han. The Kahak section of the Qom Formation in the Uiumieh-Dokhtar magmatic arc has been studied, in order to determinate its microfacies, depositional model and sequence stratigraphy. The textural analysis and faunal assemblages reveal ten microfacies. These microfacies are indicative of five depositional settings of open marine, patch reef, lagoon, tidal flat and beach of the inner and middle ramp. On the basis of the vertical succession architecture of depositional system tracts, four third-order sequences have been recognized in the Oligocene-Miocene Kahak succession of Qom Formation. Based on the correlation charts, the transgression of the Qom Sea started fiom the southeast and continued gradually towards the north. This resulted in widespread northward development of the lagoon paleoenvironment in the Aquitanian-Burdigalian stages. Also, the sequence stratigraphic model of the Oligocene Miocene Qom Formation has an architecture similar to those that have developed from Oligocene Miocene global sea level changes.

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery,which has an important impact on gas field development planning and economic evaluation.Owing to the model’s simplicity,the decline curve analysis method has been widely used to predict production performance.The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs.In this paper,a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed.The sequence learning methods used in production performance analysis herein include the recurrent neural network(RNN),long short-term memory(LSTM)neural network,and gated recurrent unit(GRU)neural network,and their performance in the tight gas reservoir production prediction is investigated and compared.To further improve the performance of the sequence learning method,the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm,which can greatly simplify the optimization process of the neural network model in an automated manner.Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained.The predictive performance of LSTM and GRU is similar,and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production.

A Climatic Sequence Stratigraphic Model in the Terrestrial Lacustrine Basin:A Case Study of Green River Formation,Uinta Basin,USA

In recent years, with the development of terrestrial sequence stratigraphy, more attention has been focused on the study of the terrestrial lacustrine sequence stratigraphic model globally. Different viewpoints are preferred by researchers. Under the guidance of the theory of sequence stratigraphy, the findings of this paper indicate that climate is a major factor controlling the formation of the fourth-order sequence, based upon the study of the sequence stratigraphy in the Green River Formation of the Uinta basin in the USA. It also divides the fourth-order sequence in the terrestrial lacustrine basin into two system tracts： the wet （rising） half-cycle and the dry （falling） half- cycle, establishing a new-style fourth-order sequence stratigraphic model for the terrestrial lacustrine basin, that is, the climate-genetic sequence stratigraphic model. As a result, the theory of sequence stratigraphy is greatly enriched.

Classification and Facies Sequence Model of Subaqueous Debris Flows

Objective Debris flows are cohesive sediment gravity flows which occur in both subaerial and subaqueous settings. Compared to subaerial debris flows which have been well studied as a geological hazard, subaqueous debris flows showing complicated sediment composition and sedimentary processes were poorly understood. The main objective of this work is to establish a classification scheme and facies sequence models of subaqueous debris flows for well understanding their sedimentary processes and depositional characteristics.

EXTENSION OF SEQUENCE STRATIGRAPHIC MODELAND SEQUENCE STRATIGRAPHIC ANALYSIS IN LIMNIC DEPOSITIONAL BASIN

The architectural patterns of sedimentary succession are diverse in different depositionalbasins. The sedimentary architecture and geological condition of such basins asepicontinental sea, intraplate limnic basins, etc., differ cIearly from those of continentalmargin basin. Extension, complement and perfection of sequence stratigraphic models are needed in the studies of various depositional basins based on the classical sequence model. This paper, for this reason,expounds the thought, principles of sequence division, methodology and technology of the study of sequence stratigraphy in epicontinental and limnic basins.

Study on Carbonate Sequence Stratigraphy of Ordovician in Northern Ordos Basin

The formation of carbonates is different from that of siliciclastics. The controlling factors, such as the in-situ production of carbonates the transport, local variation in depositional processes and the way of dispersal are unique to carbonates systems, and have resulted in the formation of a wide variety of stratal patterns. The authors believe that all the factors influencing the formation of carbonates, such as the evolution of carbonate platform, transgression and regression, drowning and exposing of the platform, should be taken into consideration during the study of carbonate sequence stratigraphy.This paper attempts to summarize the study procedure in outcrop sequence stratigraphy and to describe the integrated identification of condensed section, first flooding surface and maximum flooding surface, so that the framework of high-resolution sequence stratigraphy can be constructed. During the Sandokan in early Ordovician, a carbonate-siliciclastic ramp was developed in northern Ordos, which changed into a carbonate rimmed shelf or a distally steepened ramp during the Zhuozishan and Kelimoli. It was the break in the Wulalike and Middle Ordovician,changing the sedimentary setting into an aulacogen According to the basin evolution, the authors have established some different idealized models for depositional sequences and systems tracts.

药品价格指数的构建与应用

目的基于药品零售价格大数据构建药品价格指数,描述其波动特征,发挥其药品价格宏观监管作用,促进药品价格保持合理水平。方法运用链式拉氏指数构建原理建立药品价格指数模型,运用时间序列模型描述指数波动特征,识别并分析药品价格波动异常状况。结果2015年1月—2020年12月,药品价格总指数小幅上涨,累计涨幅为14.43%,年均涨幅约2.40%,市场化改革成效较为显著。通过基于局部加权回归的季节趋势分解(seasonal-trend decomposition using loess,STL)方法对获得的药品价格总指数时间序列进行分析,指数呈长期平缓上升趋势,不规则波动值为-1.41~2.03,说明药品价格受外因影响较小,周期性特征仍有待进一步研究。2015年1月—2020年12月,根据药品价格指数共监测到价格异常风险32次。结论药品价格指数较全面地反映药品价格走势,对于药品价格异常波动具有一定的预警作用,能够为我国药品价格监管提供有效工具。

Modeling mechanism of a novel fractional grey mode based on matrix analysis

To fully display the modeling mechanism of the novelfractional order grey model （FGM （q,1））, this paper decomposesthe data matrix of the model into the mean generation matrix, theaccumulative generation matrix and the raw data matrix, whichare consistent with the fractional order accumulative grey model（FAGM （1,1））. Following this, this paper decomposes the accumulativedata difference matrix into the accumulative generationmatrix, the q-order reductive accumulative matrix and the rawdata matrix, and then combines the least square method, findingthat the differential order affects the model parameters only byaffecting the formation of differential sequences. This paper thensummarizes matrix decomposition of some special sequences,such as the sequence generated by the strengthening and weakeningoperators, the jumping sequence, and the non-equidistancesequence. Finally, this paper expresses the influences of the rawdata transformation, the accumulation sequence transformation,and the differential matrix transformation on the model parametersas matrices, and takes the non-equidistance sequence as an exampleto show the modeling mechanism.

PROTOTYPE SURFACE MICRO- PRECISION IN FUSED DEPOSITION MODELING PROCESS

To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is explained with three aspects concretely including the principle error of rapid prototyping （RP） process, the inherent characteristics of FDM process, and some mi- cro-scratches on the surface of the extruded fiber. Based on the micro-characters of section shape of the FDM prototype, a physical model reflecting the outer shape characters is abstracted. With the physical simplified and deduced, the evaluating equations of surface roughness are acquired. According to the FDM sample parts with special design for experimental measurement, the real surface roughness values of different inclined planes are obtained. And the measuring values of surface roughness are compared with the calculation values. Furthermore, the causes of surface roughness deviation between measuring values and calculation values are respectively analyzed and studied. With the references of analytic conclusions, the measuring values of the experimental part surface are revised, and the revised values nearly accord with the calculation values. Based on the influencing principles of FDM process parameters and special post processing of FDM prototype parts, some concrete measures are proposed to reduce the surface roughness of FDM parts, and the applying effects are better.

Transferable Features from 1D-Convolutional Network for Industrial Malware Classification

With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and lives.To identify and classify new malware variants,different types of deep learning models have been widely explored recently.Generally,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability.However,in current practical applications,an ample supply of data is absent in most specific industrial malware detection scenarios.Transfer learning as an effective approach can be used to alleviate the influence of the small sample size problem.In addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware detection.In this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning tasks.The experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.

Heuristics for Mixed Model Assembly Line Balancing Problem with Sequencing

The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model（VSMM） estimation approach, one of the multiple-model（MM） estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation（MSA） plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy（GE） measure is proposed so that the MSA is achieved on the minimum geometrical entropy（MGE） principle.Consequently, the minimum geometrical entropy multiple-model（MGEMM） framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm（PF-KMGEMM） as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm（PF-AMGEMM）, are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.

Robust estimation in inverse problems via quantile coupling

In this article we consider a sequence of hierarchical space model of inverse problems.The underlying function is estimated from indirect observations over a variety of error distributions including those that are heavy-tailed and may not even possess variances or means.The main contribution of this paper is that we establish some oracle inequalities for the inverse problems by using quantile coupling technique that gives a tight bound for the quantile coupling between an arbitrary sample p-quantile and a normal variable,and an automatic selection principle for the nonrandom filters.This leads to the data-driven choice of weights.We also give an algorithm for its implementation.The quantile coupling inequality developed in this paper is of independent interest,because it includes the median coupling inequality in literature as a special case.

Application and discussion of statistical seismology in probabilistic seismic hazard assessment studies

Earthquakes are one of the natural disasters that pose a major threat to human lives and property. Earthquake prediction propels the construction and development of modern seismology;however, current deterministic earthquake prediction is limited by numerous difficulties. Identifying the temporal and spatial statistical characteristics of earthquake occurrences and constructing earthquake risk statistical prediction models have become significant;particularly for evaluating earthquake risks and addressing seismic planning requirements such as the design of cities and lifeline projects based on the obtained insight. Since the 21 st century, the occurrence of a series of strong earthquakes represented by the Wenchuan M8 earthquake in 2008 in certain low-risk prediction areas has caused seismologists to reflect on traditional seismic hazard assessment globally. This article briefly reviews the development of statistical seismology, emphatically analyzes the research results and existing problems of statistical seismology in seismic hazard assessment, and discusses the direction of its development. The analysis shows that the seismic hazard assessment based on modern earthquake catalogues in most regions should be effective. Particularly, the application of seismic hazard assessment based on ETAS(epidemic type aftershock sequence)should be the easiest and most effective method for the compilation of seismic hazard maps in large urban agglomeration areas and low seismic hazard areas with thick sedimentary zones.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.

Submarine fans： A critical retrospective (1950-2015)

When we look back the contributions on submarine fans during the past 65 years （1950 -2015）, the empirical data on 21 modern submarine fans and I0 ancient deep-water sys- tems, published by the results of the First COMFAN （Committee on FANs） Meeting （Bouma eta｜., 1985a）, have remained the single most significant compilation of data on submarine fans. The 1970s were the ＂heyday＂ of submarine fan models. In the 21st century, the general focus has shifted from submarine fans to submarine mass movements, internal waves and tides, and contourites. The purpose of this review is to illustrate the complexity of issues surrounding the origin and classification of submarine fans. The principal ele- ments of submarine fans, composed of canyons, channels, and lobes, are discussed using nine modern case studies from the Mediterranean Sea, the Equatorial Atlantic, the Gulf of Mexico, the North Pacific, the NE Indian Ocean （Bay of Bengal）, and the East Sea （Korea）. The Annot Sandstone （Eocene-Oligocene）, exposed at Peira-Cava area, SE France, which served as the type locality for the ＂Bouma Sequence＂, was reexamined. The field details are documented in questioning the validity of the model, which was the basis for the turbidite- fan link. The 29 fan-related models that are of conceptual significance, developed during the period 1970-2015, are discussed using modem and ancient systems. They are： （I） the classic submarine fan model with attached lobes, （2） the detached-lobe model, （3） the channel-levee complex without lobes, （4） the delta-fed ramp model, （5） the gully-lobe model, （6） the suprafan lobe model, （7） the depositional lobe model, （8） the fan lobe model, （9） the ponded lobe model, （I0） the nine models based on grain size and sediment source, （11） the four fan models based on tectonic settings, （12） the Jackfork debrite model, （13） the basin-floor fan model, （14） supercritical and subcritical fans, and （15） the three types of fan reservoirs. Each model is unique, and the long-standing belief that submarine fans are composed of turbidites, in particular, of gravelly and sandy high-density turbi- dites, is a myth. This is because there are no empirical data to validate the existence of gravelly and sandy high-density turbidity currents in the modern marine environments. Also, there are no experimental documentation of true turbidity currents that can trans- port gravels and coarse sands in turbulent suspension. Mass-transport processes, which include slides, slumps, and debris flows （but not turbidity currenrs）, are the most viable mechanisms for transporting gravels and sands into the deep sea. The prevailing notion that submarine fans develop during periods of sea-level lowstands is also a myth. The geologic reality is that frequent short-term events that last for only a few minutes to several hours or days （e.g., earthquakes, meteorite impacts, tsunamis, tropical cyclones, etc.） are more important in controlling deposition of deep-water sands than sporadic long- term events that last for thousands to millions of years （e.g., lowstand systems tract）. Submarine fans are still in a stage of muddled turbidite paradigm because the concept of high-density turbidity currents is incommensurable.

The Modelling of Temporal Data in the Relational Database Environment

This research takes the view that the modelling of temporal data is a fundamental step towards the solution of capturing semantics of time. The problemsinhereat in the mod6iling of time are not unique to datahase processing. Therepresentation of temporal knowledge and temporal reasoning arises in a widerange of other disciplines. ln this paper an account is given of a techniquefor modelling the semantics of temporal data and its associated normalizationmcthod. It discusses the techniques of processing temporal data by employinga Time Sequence (TS) data model. It shows a number of different strategieswhich are used to classify different data properties of temporal data, and it goeson.to develop the model of temporal data and addresses issues of temporal dataapplication design by introducing the concept of temporal data normalisation.

A novel variable structure multi-model approach based on error-ambiguity decomposition

Model Set Adaptation(MSA)plays a key role in the Variable Structure Multi-Model tracking approach(VSMM).In this paper,the Error-Ambiguity Decomposition(EAD)principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth.Consequently,the EAD Variable Structure first-order General Pseudo Bayesian(EAD-VSGPB1)algorithm and the EAD Variable Structure Interacting Multiple Model(EAD-VSIMM)algorithm are constructed.The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions.The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that,compared to some existing multi-model algorithms,the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.