Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Experimental and numerical simulation study on forced ventilation and dust removal of coal mine heading surface

In order to study the problems of unreasonable airflow distribution and serious dust pollution in a heading surface,an experimental platform for forced ventilation and dust removal was built based on the similar principles.Through the similar experiment and numerical simulation,the distribution of airflow field in the roadway and the spatial and temporal evolution of dust pollution under the conditions of forced ventilation were determined.The airflow field in the roadway can be divided into three zones:jet zone,vortex zone and reflux zone.The dust concentration gradually decreases from the head to the rear of the roadway.Under the forced ventilation conditions,there is a unilateral accumulation of dust,with higher dust concentrations away from the ducts.The position of the equipment has an interception effect on the dust.The maximum error between the test value and the simulation result is 12.9%,which verifies the accuracy of the experimental results.The research results can provide theoretical guidance for the application of dust removal technology in coal mine.

Angular unconformity in Pennsylvanian strata from 3-D seismic interpretation,Goldsmith Field,West Texas

The Pennsylvanian unconformity,which is a detrital surface,separates the beds of the Permian-aged strata from the Lower Paleozoic in the Central Basin Platform.Seismic data interpretation indicates that the unconformity is an angular unconformity,overlying multiple normal faults,and accompanied with a thrust fault which maximizes the region's structural complexity.Additionally,the Pennsylvanian angular unconformity creates pinch-outs between the beds above and below.We computed the spectral decomposition and reflector convergence attributes and analyzed them to characterize the angular unconformity and faults.The spectral decomposition attribute divides the broadband seismic data into different spectral bands to resolve thin beds and show thickness variations.In contrast,the reflector convergence attribute highlights the location and direction of the pinch-outs as they dip south at angles between 2° and 6°.After reviewing findings from RGB blending of the spectrally decomposed frequencies along the Pennsylvanian unconformity,we observed channel-like features and multiple linear bands in addition to the faults and pinch-outs.It can be inferred that the identified linear bands could be the result of different lithologies associated with the tilting of the beds,and the faults may possibly influence hydrocarbon migration or act as a flow barrier to entrap hydrocarbon accumulation.The identification of this angular unconformity and the associated features in the study area are vital for the following reasons:1)the unconformity surface represents a natural stratigraphic boundary;2)the stratigraphic pinch-outs act as fluid flow connectivity boundaries;3)the areal extent of compartmentalized reservoirs'boundaries created by the angular unconformity are better defined;and 4)fault displacements are better understood when planning well locations as faults can be flow barriers,or permeability conduits,depending on facies heterogeneity and/or seal effectiveness of a fault,which can affect hydrocarbon production.The methodology utilized in this study is a further step in the characterization of reservoirs and can be used to expand our knowledge and obtain more information about the Goldsmith Field.

Identification and distribution of 13003 landslides in the northwest margin of Qinghai-Tibet Plateau based on human-computer interaction remote sensing interpretation

The periphery of the Qinghai-Tibet Plateau is renowned for its susceptibility to landslides.However,the northwestern margin of this region,characterised by limited human activities and challenging transportation,remains insufficiently explored concerning landslide occurrence and dispersion.With the planning and construction of the Xinjiang-Tibet Railway,a comprehensive investigation into disastrous landslides in this area is essential for effective disaster preparedness and mitigation strategies.By using the human-computer interaction interpretation approach,the authors established a landslide database encompassing 13003 landslides,collectively spanning an area of 3351.24 km^(2)(36°N-40°N,73°E-78°E).The database incorporates diverse topographical and environmental parameters,including regional elevation,slope angle,slope aspect,distance to faults,distance to roads,distance to rivers,annual precipitation,and stratum.The statistical characteristics of number and area of landslides,landslide number density(LND),and landslide area percentage(LAP)are analyzed.The authors found that a predominant concentration of landslide origins within high slope angle regions,with the highest incidence observed in intervals characterised by average slopes of 20°to 30°,maximum slope angle above 80°,along with orientations towards the north(N),northeast(NE),and southwest(SW).Additionally,elevations above 4.5 km,distance to rivers below 1 km,rainfall between 20-30 mm and 30-40 mm emerge as particularly susceptible to landslide development.The study area’s geological composition primarily comprises Mesozoic and Upper Paleozoic outcrops.Both fault and human engineering activities have different degrees of influence on landslide development.Furthermore,the significance of the landslide database,the relationship between landslide distribution and environmental factors,and the geometric and morphological characteristics of landslides are discussed.The landslide H/L ratios in the study area are mainly concentrated between 0.4 and 0.64.It means the landslides mobility in the region is relatively low,and the authors speculate that landslides in this region more possibly triggered by earthquakes or located in meizoseismal area.

Optimization method of fracturing fluid volume intensity for SRV fracturing technique in shale oil reservoir based on forced imbibition:A case study of well X-1 in Biyang Sag of Nanxiang Basin,China

An optimization method of fracturing fluid volume strength was introduced taking well X-1 in Biyang Sag of Nanxiang Basin as an example.The characteristic curves of capillary pressure and relative permeability were obtained from history matching between forced imbibition experimental data and core-scale reservoir simulation results and taken into a large scale reservoir model to mimic the forced imbibition behavior during the well shut-in period after fracturing.The optimization of the stimulated reservoir volume(SRV)fracturing fluid volume strength should meet the requirements of estimated ultimate recovery(EUR),increased oil recovery by forced imbibition and enhancement of formation pressure and the fluid volume strength of fracturing fluid should be controlled around a critical value to avoid either insufficiency of imbibition displacement caused by insufficient fluid amount or increase of costs and potential formation damage caused by excessive fluid amount.Reservoir simulation results showed that SRV fracturing fluid volume strength positively correlated with single-well EUR and an optimal fluid volume strength existed,above which the single-well EUR increase rate kept decreasing.An optimized increase of SRV fracturing fluid volume and shut-in time would effectively increase the formation pressure and enhance well production.Field test results of well X-1 proved the practicality of established optimization method of SRV fracturing fluid volume strength on significant enhancement of shale oil well production.

Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

Application of Secondary Logging Interpretation—Taking Yan 9 Reservoir in X Area as an Example

Logging data and its interpretation results are one of the most important basic data for understanding reservoirs and oilfield development. Standardized and unified logging interpretation results play a decisive role in fine reservoir description and reservoir development. Aiming at the problem of the conflict between the development effect and the initial interpretation result of Yan 9 reservoir in Hujianshan area of Ordos Basin, by combining the current well production performance, logging, oil test, production test and other data, on the basis of making full use of core, coring, logging, thin section analysis and high pressure mercury injection data, the four characteristics of reservoir are analyzed, a more scientific and reasonable calculation model of reservoir logging parameters is established, and the reserves are recalculated after the second interpretation standard of logging is determined. The research improves the accuracy of logging interpretation and provides an effective basis for subsequent production development and potential horizons.

Effects and Causes of VR-Supported Interpreting Learning Environment on the Interpretation Classroom Anxiety of Student Interpreter

Interpreting activity is considered a high-anxiety activity due to its immediacy, multitasking, complexity of cognitive processing, and uncertainty of cognitive processing. Research has shown that interpreting anxiety, as the biggest emotional obstacle in the interpreting process, is the main emotional factor that leads to individual differences in interpreting. Students often claim to have fear or anxiety behaviors in interpreting exams, interpreting competitions, and interpreting classes. However, the research on interpreting teaching attaches importance to the cultivation of language knowledge, cultural knowledge, and interpreting skills, and does not pay enough attention to emotional factors such as motivation and anxiety in interpreting learning, which makes it difficult for the cultivated interpreters to meet the requirements of professional practice. In recent years, virtual reality technology (VR) has been gradually applied in the field of foreign language and interpreting teaching for creating a real, interactive and experiential language learning environment. Situated Learning Theory stresses that the fundamental mechanism for learning to take place is for individuals to participate in the real context in which knowledge is generated, and to realize the construction of knowledge through the interaction with the community of practice and the environment. Virtual reality technology can satisfy the needs of language learners for real contexts by providing learners with immersive, imaginative and interactive scenario simulations, and has a certain positive effect on alleviating learning anxiety. Therefore, relying on the virtual simulation course “United Nations Kubuqi International Desert Ecological Science and Technology Innovation International Volunteer Language Service Practical Training System”, this paper adopts a combination of quantitative and qualitative analyses to investigate the interpretation anxiety level of the interpreter trainees and the factors affecting them in the VR situation to help them discover effective responses to interpreter anxiety.

Transfer matrix method for free and forced vibrations of multi-level functionally graded material stepped beams with different boundary conditions

Functionally graded materials(FGMs)are a novel class of composite materials that have attracted significant attention in the field of engineering due to their unique mechanical properties.This study aims to explore the dynamic behaviors of an FGM stepped beam with different boundary conditions based on an efficient solving method.Under the assumptions of the Euler-Bernoulli beam theory,the governing differential equations of an individual FGM beam are derived with Hamilton’s principle and decoupled via the separation-of-variable approach.Then,the free and forced vibrations of the FGM stepped beam are solved with the transfer matrix method(TMM).Two models,i.e.,a three-level FGM stepped beam and a five-level FGM stepped beam,are considered,and their natural frequencies and mode shapes are presented.To demonstrate the validity of the method in this paper,the simulation results by ABAQUS are also given.On this basis,the detailed parametric analyses on the frequencies and dynamic responses of the three-level FGM stepped beam are carried out.The results show the accuracy and efficiency of the TMM.

Exact Solutions of Forced Schrödinger Equation and How to Choose the External Forces

Schrödinger equations are very common equations in physics and mathematics for nonlinear physics to model the dynamics of wave propagation in waveguides such as power lines, atomic chains, optical fibers, and even in quantum mechanics. But all these equations are most often studied without worrying about what would happen if this equation were maintained, that is to say, had a second member synonymous with an external force. It is true that on a physical level, such equations can be considered as describing the generation of waves on a waveguide using an external force. However, the in-depth analysis of this aspect is not at the center of our reflection in this article, but for us, it is a question of proposing exact solutions to this type of equation and above all proposing the general form of the external force so that the obtaining exact solutions is possible.

Preliminary report of coseismic surface rupture(part)of Türkiye's M_(W)7.8 earthquake by remote sensing interpretation

Both M_(W) 7.8 and M_(W) 7.5 earthquakes occurred in southeastern Türkiye on February 6,2023,resulting in numerous buildings collapsing and serious casualties.Understanding the distribution of coseismic surface ruptures and secondary disasters surrounding the epicentral area is important for post-earthquake emergency and disaster assessments.High-resolution Maxar and GF-2 satellite data were used after the events to extract the location of the rupture surrounding the first epicentral area.The results show that the length of the interpreted surface rupture zone(part of)is approximately 75 km,with a coseismic sinistral dislocation of 2-3 m near the epicenter;however,this reduced to zero at the tip of the southwest section of the East Anatolia Fault Zone.Moreover,dense soil liquefaction pits were triggered along the rupture trace.These events are in the western region of the Eurasian Seismic Belt and result from the subduction and collision of the Arabian and African Plates toward the Eurasian Plate.The western region of the Chinese mainland and its adjacent areas are in the eastern section of the Eurasian Seismic Belt,where seismic activity is controlled by the collision of the Indian and Eurasian Plates.Both China and Türkiye have independent tectonic histories.

Propagation Dynamics of Forced Pulsating Waves for a Time Periodic Lotka-Volterra Cooperative System with Nonlocal Effects in Shifting Habitats

In this paper, we will concern the existence, asymptotic behaviors and stability of forced pulsating waves for a Lotka-Volterra cooperative system with nonlocal effects under shifting habitats. By using the alternatively-coupling upper-lower solution method, we establish the existence of forced pulsating waves, as long as the shifting speed falls in a finite interval where the endpoints are obtained from KPP-Fisher speeds. The asymptotic behaviors of the forced pulsating waves are derived. Finally, with proper initial, the stability of the forced pulsating waves is studied by the squeezing technique based on the comparison principle.

Problems of Music Interpretation and Its Expression: On the Example of the Violin

Music is essentially a sonic phenomenon, an intangible art that we experience through our auditory organs. It is written as a physical note on paper. It is the result of harmony and composition of music that expresses the meaning of music created from the author’s mind, thinking, skills, and feelings, or composition or work. If a composition is not interpreted by a musician and set to music, it is nothing more than a notation and notation written down on paper. The main process of conveying the composition to the listeners through the musician’s interpretation of the music is somewhat overlooked. Therefore, in this research, the musician’s thinking and editing method, and how to express it through interpretation, were the doctoral dissertation and articles of researchers such as have been studied in comparison.

ForceCT双能量与低kV扫描技术在头颈CTA中的对比分析

探索并比较Force CT双能量与低kV扫描技术在头颈CTA中的作用。方法 以系统随机化法,将2022.01-2023.01在我院接受头颈CTA检查的40例患者均分为两组各20例。其中观察组患者采用低kV扫描技术,对照组则进行Force CT双能量扫描。比较两组的辐射剂量、图像质量。结果 观察组的辐射剂量指数低于对照组,P<0.05。观察组的大脑中动脉与颈内动脉图像质量均高于对照组,P<0.05。结论 运用低kV扫描技术,能较好地降低头颈CTA检查时的辐射剂量,与此同时还能得到更为高质量的图像,帮助病情的更快诊断,患者能尽快得到个性化的治疗,早日恢复健康。

Force CT高级建模迭代重建算法对门静脉图像质量的影响

目的:探讨Force CT高级建模迭代重建算法(ADMIRE)对门静脉图像质量影响。方法:回顾性分析2022年1—4月于中山大学附属第八医院行上腹部CT平扫及增强的40例患者影像资料,在syngo.via后处理工作站重建,采用门脉期原始数据重建出滤波反投影算法(FBP)、ADMIRE1、ADMIRE3、ADMIRE5 4组不同重建算法的图像,在4组不同算法图像上测量门静脉干中心位置、肝实质及同层竖脊肌的CT值和噪声值,计算门静脉和肝实质的信噪比(SNR)和对比噪声比(CNR)。此外,由两位具有5年以上诊断经验的医生对图像进行主观评分。结果:随着迭代等级的增高,图像的CNR、SNR呈现上升的趋势,图像的噪声值呈现下降的趋势,其中ADMIRE5组图像中门静脉和肝脏具有最高的CNR和SNR且具有最低的噪声值,以及最高的主观评分,采用ADMIRE5重建算法的门静脉与周围组织对比非常好,门静脉5级分支显示良好。结论:高级建模迭代重建ADMIRE算法相比于FBP重建算法,降噪效果更好,门静脉的显示更加锐利,能提升门静脉的图像质量。

A deep reinforcement learning(DRL)based approach for well-testing interpretation to evaluate reservoir parameters

Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.Automatic well test interpretations based on artificial intelligence are the most promising to solve the problem of non-unique solution.In this work,a new deep reinforcement learning(DRL)based approach is proposed for automatic curve matching for well test interpretation,by using the double deep Q-network(DDQN).The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing models.In addition,to alleviate the dimensional disaster problem of parameter space,an asynchronous parameter adjustment strategy is used to train the agent.Finally,field applications are carried out by using the new DRL approaches.Results show that step number required for the DDQN to complete the curve matching is the least among,when comparing the naive deep Q-network(naive DQN)and deep Q-network(DQN).We also show that DDQN can improve the robustness of curve matching in comparison with supervised machine learning algorithms.Using DDQN algorithm to perform 100 curve matching tests on three traditional well test models,the results show that the mean relative error of the parameters is 7.58%for the homogeneous model,10.66%for the radial composite model,and 12.79%for the dual porosity model.In the actual field application,it is found that a good curve fitting can be obtained with only 30 steps of parameter adjustment.

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Enhancing Emergency ECG Interpretation: A Practical Checklist Developed from a Study Assessing Young Physicians’ Diagnostic Abilities

Effective interpretation of electrocardiograms (ECGs) is a critical skill for healthcare professionals, especially in emergency settings. This study assessed the diagnostic abilities of 200 final-year medical students and young doctors in ECG interpretation. Subsequently, based on the study findings, a practical checklist was developed, and a comprehensive book entitled “Critical ECG in Emergencies” was authored by the researchers. The checklist provides cognitive aids and serves as a systematic guide to enhance ECG interpretation in urgent situations. The study revealed a need for improved confidence in interpreting critical ECGs among the participants. The checklist offers promise for reducing diagnostic errors and improving patient outcomes. Further research is required to evaluate its impact on daily practice and patient care, while the book provides valuable resources for healthcare professionals in emergency medicine.

Evaluation of Hydrocarbon Reservoir in the “SIMA” Field of Niger Delta Nigeria from Interpretation of 3D Seismic and Petrophysical Log Data

3D seismic and petrophysical log data interpretation of reservoir sands in “SIMA” Field, onshore Niger Delta has been undertaken in this study to ascertain the reservoir characteristics in terms of favourable structural and petrophysical parameters suitable for hydrocarbon accumulation and entrapment in the field. Horizon and fault interpretation were carried out for subsurface structural delineation. In all, seven faults (five normal and two listric faults) were mapped in the seismic section. These faults were major structure building faults corresponding to the growth and antithetic faults in the area within the well control. The antithetic fault trending northwest-southeast and the normal fault trending northeast-southwest on the structural high in the section act as good trapping mechanisms for hydrocarbon accumulations in the reservoir. From the manual and auto-tracking methods applied, several horizons were identified and mapped. The section is characterized by high amplitude with moderate-to-good continuity reflections appearing parallel to sub-parallel, mostly disturbed by some truncations which are more fault related than lithologic heterogeneity. The southwestern part is, however, characterized by low-to-high or variable amplitude reflections with poor-to-low continuity. Normal faults linked to roll-over anticlines were identified. Some fault truncations were observed due to lithologic heterogeneity. The combination of these faults acts as good traps for hydrocarbon accumulations in the reservoir. Reservoir favourable petrophysical qualities, having average NTG, porosity, permeability and water saturation of 5 m, 0.20423, 1128.219 kD and 0.458 respectively.