Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Fluid identification and tight oil layer classification for the southwestern Mahu Sag,Junggar Basin using NMR logging-based spectrum decomposition

The intricate distribution of oil and water in tight rocks makes pinpointing oil layers challenging.While conventional identification methods offer potential solutions,their limited accuracy precludes them from being effective in their applications to unconventional reservoirs.This study employed nuclear magnetic resonance(NMR)spectrum decomposition to dissect the NMR T_(2)spectrum into multiple subspectra.Furthermore,it employed laboratory NMR experiments to ascertain the fluid properties of these sub-spectra,aiming to enhance identification accuracy.The findings indicate that fluids of distinct properties overlap in the T_(2)spectra,with bound water,movable water,bound oil,and movable oil appearing sequentially from the low-value zone to the high-value zone.Consequently,an oil layer classification scheme was proposed,which considers the physical properties of reservoirs,oil-bearing capacity,and the characteristics of both mobility and the oil-water two-phase flow.When applied to tight oil layer identification,the scheme's outcomes align closely with actual test results.A horizontal well,deployed based on these findings,has produced high-yield industrial oil flow,underscoring the precision and dependability of this new approach.

Systematic Evaluation of Pharmacognostic Identification of Polygonum capitatum

[Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical identification was conducted on plant roots, stems, leaves, flowers and fruits. The P. capitatum powder was processed for physical and chemical distinction by FeCl 3 chromogenic reaction, hydrochloric acid magnesium powder reaction, AlCl 3 color development reaction and thin-layer chromatography.Microscope identification was carried out on the powder. Plant genome DNeasy Plant Kit was adopted for DNA molecular marker identification. [Results] The results showed that the stem of P. capitatum was tufted, the leaves were oval, 2 to 5 cm long, and 1 to 2 cm wide;the leaf apex was acute and cuneate at the base, the inflorescence was capitate, paired or solitary;the raceme was erect and nearly spherical, and the perianth was light red. Furthermore, for the chromogenic reaction of FeCl 3 ethanol extract of P. capitatum , appeared blue and turned to dark blue after long time storing at room temperature. For the reaction of hydrochloric acid magnesium powder, the alcohol extract of P. capitatum , exhibited deep red. In the color reaction of AlCl 3, the alcohol extract revealed yellow fluorescence under 360 nm UV lamp. Microscope identification of the powder displayed pollen grains, crystal sheath fibers, cellulose, vessels, starch grains, cork cells, and other characteristic fragments. In addition, DNA barcoding electrophoresis results showed that P. capitatum showed a clear and bright single band near 500 bp, and further sequencing results showed that the sequence differences were mainly concentrated in ITS1 and ITS2 region. [Conclusions] Systematic evaluation for the identification of P. capitatum is established, which combines with macroscopic identification, physicochemical identification, powder microscope identification, and DNA molecular identification. Finally, the original medicinal material is identified as P. capitatum Buch.-Ham. ex D. Don.

Pharmacognosy research and identification of Euphorbia prostrata Ait.

Background:Euphorbia prostrata Ait.is an annual herb widely distributed in the southern region of China with great medical values on Anti-inflammation,insect repellent,treatment of diarrhea.Despite its extensive uses as a traditional Chinese medicine,no systematic research on the identification of E.prostrata has been reported.Methods:The study aimed to establish an accurate identification system for E.prostrata through traditional pharmacognostical methods,including botanical origin,morphological characters,medicinal material characters,microscopic characters,physicochemical parameters determination,phytochemical screening,and DNA barcoding analysis.Results:Physicochemical results show that this plant likely contains flavonoids,anthraquinones,and other substances.The ITS loci of the nuclear genome and psbA-trnH loci of the chloroplast genome were selected and evaluated,which were the most variable loci.Conclusion:The findings of this study are expected to contribute to the development of species identification,as well as provide references for authenticity identification,genetic relationship analysis,and further utilization of E.prostrata.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Identification and evaluation of shale oil micromigration and its petroleum geological significance

Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale.

Nondestructive technique for identifying nuclides using neutron resonance transmission analysis at CSNS Back-n

Nondestructive and noninvasive neutron assays are essential applications of neutron techniques.Neutron resonance transmission analysis(NRTA)is a powerful nondestructive method for investigating the elemental composition of an object.The back-streaming neutron line(Back-n)is a newly built time-of-flight facility at the China Spallation Neutron Source(CSNS)that provides neutrons in the eV to 300 MeV range.A feasibility study of the NRTA method for nuclide identification was conducted at the CSNS Back-n via two test experiments.The results demonstrate that it is feasible to identify different elements and isotopes in samples using the NRTA method at Back-n.This study reveals its potential future applications.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity

Enhanced osteoclastogenesis and osteoclast activity contribute to the development of osteoporosis,which is characterized by increased bone resorption and inadequate bone formation.As novel antiosteoporotic therapeutics are needed,understanding the genetic regulation of human osteoclastogenesis could help identify potential treatment targets.This study aimed to provide an overview of transcriptional reprogramming during human osteoclast differentiation.Osteoclasts were differentiated from CD14+monocytes from eight female donors.RNA sequencing during differentiation revealed 8980 differentially expressed genes grouped into eight temporal patterns conserved across donors.These patterns revealed distinct molecular functions associated with postmenopausal osteoporosis susceptibility genes based on RNA from iliac crest biopsies and bone mineral density SNPs.Network analyses revealed mutual dependencies between temporal expression patterns and provided insight into subtype-specific transcriptional networks.The donor-specific expression patterns revealed genes at the monocyte stage,such as filamin B(FLNB)and oxidized low-density lipoprotein receptor 1(OLR1,encoding LOX-1),that are predictive of the resorptive activity of mature osteoclasts.The expression of differentially expressed G-protein coupled receptors was strong during osteoclast differentiation,and these receptors are associated with bone mineral density SNPs,suggesting that they play a pivotal role in osteoclast differentiation and activity.The regulatory effects of three differentially expressed G-protein coupled receptors were exemplified by in vitro pharmacological modulation of complement 5 A receptor 1(C5AR1),somatostatin receptor 2(SSTR2),and free fatty acid receptor 4(FFAR4/GPR120).Activating C5AR1 enhanced osteoclast formation,while activating SSTR2 decreased the resorptive activity of mature osteoclasts,and activating FFAR4 decreased both the number and resorptive activity of mature osteoclasts.In conclusion,we report the occurrence of transcriptional reprogramming during human osteoclast differentiation and identified SSTR2 and FFAR4 as antiresorptive G-protein coupled receptors and FLNB and LOX-1 as potential molecular markers of osteoclast activity.These data can help future investigations identify molecular regulators of osteoclast differentiation and activity and provide the basis for novel antiosteoporotic targets.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Identifying the real fracture hidden in rock microcrack zone by acoustic emission energy

Identifying the real fracture of rock hidden in acoustic emission(AE)source clusters(AE-depicted microcrack zone)remains challenging and crucial.Here we revealed the AE energy(representing dissipated energy)distribution rule in the rock microcrack zone and proposed an AE-energy-based method for identifying the real fracture.(1)A set of fracture experiments were performed on granite using wedgeloading,and the fracture process was detected and recorded by AE.The microcrack zone associated with the energy dissipation was characterized by AE sources and energy distribution,utilizing our selfdeveloped AE analysis program(RockAE).(2)The accumulated AE energy,an index representing energy dissipation,across the AE-depicted microcrack zone followed the normal distribution model(the mean and variance relate to the real fracture path and the microcrack zone width).This result implies that the nucleation and coalescence of massive cracks(i.e.,real fracture generation process)are supposed to follow a normal distribution.(3)Then,we obtained the real fracture extension path by joining the peak positions of the AE energy normal distribution curve at different cross-sections of the microcrack zone.Consequently,we distinguished between the microcrack zone and the concealed real fracture within it.The deviation was validated as slight as 1–3 mm.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Dynamic Identification and Evolution of Urban-suburban-rural Transition Zones Based on the Blender of Natural and Humanistic Factors: A Case Study of Chengdu, China

Urban-suburban-rural(U-S-R)zones exhibit distinctive transitional characteristics in interaction between human and nature.U-S-R transition zones(U-S-RTZ)are also highlighting the function diversity and landscape heterogeneity across territorial spaces.As a super megacity in western China,Chengdu’s rapid urbanization has driven the evolution of U-S-R spaces,resulting in a sequential structure.To promote the high-quality spatial development of urban-rural region in a structured and efficient manner,it is essential to con-duct a scientific examination of the multidimensional interconnection within the U-S-RTZ framework.By proposing a novel identifica-tion method of U-S-RTZ and taking Chengdu,China as a case study,grounded in a blender of natural and humanistic factors,this study quantitatively delineated and explored the spatial evolutions of U-S-RTZ and stated the optimization orientation and sustainable devel-opment strategies of the production-living-ecological spaces along the U-S-R gradients.The results show that:1)it is suitable for the quantitative analysis of U-S-RTZ by established three-dimensional identification system in this study.2)In 1990-2020,the urban-sub-urban transition zones(U-STZ)in Chengdu have continuously undergone a substantial increase,and the scale of the suburban-rural transition zones(S-RTZ)has continued to expand slightly,while the space of rural-ecological transition zones(R-ETZ)has noticeably compressed.3)The landuse dynamics within U-S-RTZ has gradually increased in 1990-2020.The main direction of landuse transition was from farmland to construction land or woodlands,with the expansion of construction land being the most significant.4)R-ETZ primarily focus on ecological functions,and there is a trade-off relationship between the production-ecological function within the S-RTZ,and in the U-STZ,production-living composite functions are prioritized.This study emphasizes the importance of elastic planning and precise governance within the U-S-RTZ in a rapid urbanization region,particularly highlighting the role of suburbs as landscape corridors and service hubs in urban-rural integration.It elucidates to the practical implications for achieving high-quality development of integrated U-S-R territorial spaces.

Identifying multidisciplinary problems from scientific publications based on a text generation method

Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.

Stratigraphic identification using real-time drilling data

Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing method.The method can handle invalid drilling data generated during manual operations.The correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was established.The average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic interface.The average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,respectively.The results obtained using the present method were consistent with geotechnical survey results.The proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying.

A process-oriented approach for identifying potential landslides considering time-dependent behaviors beyond geomorphological features

Geomorphological features are commonly used to identify potential landslides.Nevertheless,overemphasis on these features could lead to misjudgment.This research proposes a process-oriented approach for potential landslide identification that considers time-dependent behaviors.The method integrates comprehensive remote sensing and geological analysis to qualitatively assess slope stability,and employs numerical analysis to quantitatively calculate aging stability.Specifically,a time-dependent stability calculation method for anticlinal slopes is developed and implemented in discrete element software,incorporating time-dependent mechanical and strength reduction calculations.By considering the time-dependent evolution of slopes,this method highlights the importance of both geomorphological features and time-dependent behaviors in landslide identification.This method has been applied to the Jiarishan slope(JRS)on the Qinghai-Tibet Plateau as a case study.The results show that the JRS,despite having landslide geomorphology,is a stable slope,highlighting the risk of misjudgment when relying solely on geomorphological features.This work provides insights into the geomorphological characterization and evolution history of the JRS and offers valuable guidance for studying slopes with similar landslide geomorphology.Furthermore,the process-oriented method incorporating timedependent evolution provides a means to evaluate potential landslides,reducing misjudgment due to excessive reliance on geomorphological features.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.