Complex Distribution Research Articles

Machine Learning Technique Reveals Intrinsic EEG Connectivity Characteristics of Patients With Mild Stroke During Cognitive Task Performing

Although convergent evidence has shown that patients with mild stroke (MS) are commonly accompanied by post-stroke cognitive and/or memory impairment, only disproportionate attention was paid compared to severe stroke. To promote post-stroke management for early intervention in MS-related cognitive impairment, a feasible and convenient method for MS detection is therefore favorable. A data-driven classification framework combined with quantitative graph theoretical analysis was introduced in this work, aiming to provide a comprehensive appreciation of MS-related brain network alterations. EEG functional connectivity (FC) was constructed from 45 patients with MS and 45 healthy participants during two cognitive tasks (i.e., visual and auditory oddball) and set as input for the classification model and graph theoretical analysis. As expected, patients showed significantly reduced behavioral performance in both tasks. Furthermore, we achieved a satisfactory classification accuracy of 88.9% with a decision fusion strategy from classification models of both tasks. The spatio-spectral characteristics of the discriminative FC revealed complex topological distributions in both tasks. Moreover, significantly decreased global efficiency was found, suggesting a MS-related disruption in parallel information processing. Overall, these results demonstrated the potential of FC as salient biomarkers for detecting MS, and extended our understanding of the underlying MS-related neural mechanisms during cognitive processing.

A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging

SUMMARY We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.

Fatigue strength assessment of preloaded cross-toothed flange connections based on the FKM guidelines

Cross-toothed flange connections are used in drivetrains for form-fit coupling of shafts. They belong to the group of non-shiftable couplings. Compared to frictional connections, tooth flange connections can transmit significantly higher torques in the same space. In contrast to other tooth couplings, the crossed tooth flange coupling investigated here is characterized by a particularly complex load distribution. The preload force, which is necessary to transmit operational loads, is generated by tensioning bolts, which can be located centrally or decentralized. Thus, the stress condition in the notch of the coupling is always determined by a superposition of stresses from preload force and operating loads.Extensive experimental and simulative investigations were carried out as a part of a research project. In addition to the development of a pragmatic, specially adapted calculation concept for cross-toothed couplings for practice, the experimental tests were accompanied by calculations with the FKM guideline “Analytical Strength Assessment” and the FKM guideline “non-linear”, The determination of local stresses was carried out with the help of elasticity theoretical and elastic-plastic FE full-contact calculations based on experimentally determined material data.This article deals with the possibilities of calculations based on the FE simulation results using the above mentioned FKM guidelines. The experimentally determined load-carrying capacity is critically compared to the calculation results. The focus is on the implementation of the fatigue strength analysis in the context of the corresponding boundary conditions. Topics such as the assumed knee point of the Wöhler curve to the fatigue strength range are discussed as well as the evaluation options for elastic-plastic FE results for the guideline „non-linear”.

Modeling Complex EEG Data Distribution on the Riemannian Manifold Toward Outlier Detection and Multimodal Classification.

The usage of Riemannian geometry for Brain-computer interfaces (BCIs) has gained momentum in recent years. Most of the machine learning techniques proposed for Riemannian BCIs consider the data distribution on a manifold to be unimodal. However, the distribution is likely to be multimodal rather than unimodal since high-data variability is a crucial limitation of electroencephalography (EEG). In this paper, we propose a novel data modeling method for considering complex data distributions on a Riemannian manifold of EEG covariance matrices, aiming to improve BCI reliability. Our method, Riemannian spectral clustering (RiSC), represents EEG covariance matrix distribution on a manifold using a graph with proposed similarity measurement based on geodesic distances, then clusters the graph nodes through spectral clustering. This allows flexibility to model both a unimodal and a multimodal distribution on a manifold. RiSC can be used as a basis to design an outlier detector named outlier detection Riemannian spectral clustering (odenRiSC) and a multimodal classifier named multimodal classifier Riemannian spectral clustering (mcRiSC). All required parameters of odenRiSC/mcRiSC are selected in data-driven manner. Moreover, there is no need to pre-set a threshold for outlier detection and the number of modes for multimodal classification. The experimental evaluation revealed odenRiSC can detect EEG outliers more accurately than existing methods and mcRiSC outperformed the standard unimodal classifier, especially on high-variability datasets. odenRiSC/mcRiSC are anticipated to contribute to making real-life BCIs outside labs and neuroergonomics applications more robust. RiSC can work as a robust EEG outlier detector and multimodal classifier.

EUPollMap: the European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach

Abstract. Modern and fossil pollen data are widely used in paleoenvironmental research to characterize past environmental changes in a given location. However, their discrete and discontinuous nature can limit the inferences that can be made from them. Deriving continuous spatial maps of the pollen presence from point-based datasets would enable more robust regional characterization of such past changes. To address this problem, we propose a comprehensive collection of European pollen presence maps including 194 pollen taxa derived from the interpolation of pollen data from the Eurasian Modern Pollen Database (EMPD v2) restricted to the Euro-Mediterranean Basin. To do so, we developed an automatic Kriging-based interpolation workflow to select an optimal geostatistical model describing the spatial variability for each taxon. The output of the interpolation model consists of a series of multivariate predictive maps of Europe at 25 km scale, showing the occurrence probability of pollen taxa, the predicted presence based on diverse probability thresholds, and the interpolation uncertainty for each taxon. Combined visual inspections of the maps and systematic cross-validation tests demonstrated that the ensemble of predictions is reliable even in data-scarce regions, with a relatively low uncertainty, and robust to complex and non-stationary pollen distributions. The maps, freely distributed as GeoTIFF files (https://doi.org/10.5281/zenodo.10015695, Oriani et al., 2023), are proposed as a ready-to-use tool for spatial paleoenvironmental characterization. Since the interpolation model only uses the coordinates of the observation to spatialize the data, the model can also be employed with fossil pollen records (or other presence/absence indicators), thus enabling the spatial characterization of past changes, and possibly their subsequent use for quantitative paleoclimate reconstructions.

Wind pressure distribution and non-Gaussian characteristics of a building complex with tree-inspired structures

In order to study the characteristics of wind pressure on the surface of a building complex composed of multiple complex curved roofs, the synchronized pressure tests on the rigid models were conducted in an atmospheric boundary layer wind tunnel. The building complex models are composed of three tree-inspired structures and two independent low-rise large-span buildings, and the surface wind pressure time series of three structures with complex curved roofs in the middle of the complex was measured. The results show the distribution characteristics of wind pressure coefficient and high-order moment are significantly affected by the geometries and structures created by various roof configurations. The focus is on the impact of these special complex structures on wind loads, and to summarize some rules that can be referred for designing, and provide some explanations from the perspective of the flow structures generation mechanism.

Probabilistic rotation modeling based on directional mixture density networks

Predicting 3D rotations from a single image presents a significant challenge, primarily due to the inherent uncertainty arising from factors such as high symmetry, self-obscuration, and noise in the 3D environment. In this work, we propose a novel multimodal-based probabilistic model that integrates the matrix Fisher distribution and von Mises Fisher distribution into a mixture density network. Our model not only captures the inherent uncertainty of the object but also learns this uncertainty directly from the training data, thereby enhancing the robustness, flexibility, and efficiency of the model. To further refine the model's ability to handle ambiguities and recognize multiple distinct modes, we introduce a relaxed version of the winner-take-all loss function. This adaptation significantly improves the model's capability in accurately representing complex multimodal distributions. The performance of our model is rigorously assessed using two challenging datasets: Pascal3D+ and ModelNet10-SO(3). Extensive experimental analysis highlights the model's exceptional capability to fit complex multimodal distributions. Notably, when tested on the ModelNet10-SO(3) dataset, which is characterized by its ambiguity, and the more unequivocal Pascal3D+ dataset, our model outperforms the prevailing top baseline models by achieving accuracy improvements of 2.7% and 3.4%, respectively, at the minimum angle threshold. These results not only demonstrate our model's advanced capabilities in fitting complex distributions but also validate its effectiveness in accurately predicting 3D rotations in both ambiguous and unambiguous scenarios.

Numerical study on crack propagation of CO2 fracturing in tight sandstone reservoir considering fluid-solid-temperature coupling

Tight sandstone has a tight structure and low natural production capacity, usually requiring the use of fracturing technology to achieve industrial production. Supercritical CO2 fracturing has attracted much attention due to its clean and efficient properties. In this paper, using the principles of damage mechanics, the crack propagation law during supercritical CO2 fracturing was studied by using numerical methods. The influence of different factors on the fracture propagation patterns is analyzed. Results indicate that supercritical CO2 fracturing can form complex fracture distribution patterns. This mode is divided into three different stages: slow fracture propagation, rapid fracture propagation, and rigid fracture propagation. In addition, highly heterogeneous formations generate more secondary fractures but limit their expansion.

Characterizing Bridge Thermal Response for Bridge Load Rating and Condition Assessment: A Parametric Study

Temperature is the main driver of bridge response. It is continuously applied and may have complex distributions across the bridge. Daily temperature loads force bridges to undergo deformations that are larger than or equal to peak-to-peak traffic loads. Bridge thermal response must therefore be accounted for when performing load rating and condition assessment. This study assesses the importance of characterizing bridge thermal response and separating it from traffic-induced response. Numerical replicas (i.e., fine element models) of a steel girder bridge are generated to validate the proposed methodology. Firstly, a variety of temperature distribution scenarios, such as those resulting from extreme weather conditions due to climate change, are modelled. Then, nominal traffic load scenarios are simulated, and bridge response is characterized. Finally, damage is modelled as a reduction in material stiffness due to corrosion. Bridge response to applied traffic load is different before and after the introduction of damage; however, it can only be correctly quantified when the bridge thermal response is accurately accounted for. The study emphasizes the importance of accounting for distributed temperature loads and characterizing bridge thermal response, which are important factors to consider both in bridge design and condition assessment.

Hypometabolic patterns are related to post-surgical seizure outcomes in focal cortical dysplasia: A semi-quantitative study.

Fluorine-18-fluorodeoxyglucose-positron emission tomography (FDG-PET) is routinely used for presurgical evaluation in many epilepsy centers. Hypometabolic characteristics have been extensively examined in prior studies, but the metabolic patterns associated with specific pathological types of drug-resistant epilepsy remain to be fully defined. This study was developed to explore the relationship between metabolic patterns or characteristics and surgical outcomes in type I and II focal cortical dysplasia (FCD) patients based on results from a large cohort. Data from individuals who underwent epilepsy surgery from 2014 to 2019 with a follow-up duration of over 3 years and a pathological classification of type I or II FCD in our hospital were retrospectively analyzed. Hypometabolic patterns were quantitatively identified via statistical parametric mapping (SPM) and qualitatively analyzed via visual examination of PET-MRI co-registration images. Univariate analyses were used to explore the relationship between metabolic patterns and surgical outcomes. In total, this study included data from 210 patients. Following SPM calculations, four hypometabolic patterns were defined including unilobar, multi-lobar, and remote patterns as well as cases where no pattern was evident. In type II FCD patients, the unilobar pattern was associated with the best surgical outcomes (p = 0.014). In visual analysis, single gyrus (p = 0.032) and Clear-cut hypometabolism edge (p = 0.040) patterns exhibited better surgery outcomes in the type II FCD group. PET metabolic patterns are well-correlated with the prognosis of type II FCD patients. However, similar correlations were not observed in type I FCD, potentially owing to the complex distribution of the epileptogenic region. In this study, we demonstrated that FDG-PET was a crucial examination for patients with FCD, which was a common cause of epilepsy. We compared the surgical prognosis for patients with different hypometabolism distribution patterns and found that clear and focal abnormal region in PET was correlated with good surgical outcome in type II FCD patients.

An integrative delimitation of the species boundaries within one hornbeam species complex (Betulaceae: Carpinus)

AbstractBoth hybridization and intraspecific morphological variation across environmental gradients complicate species delineation. We aimed to discern both possibilities that may blur species boundaries in the Carpinus viminea–Carpinus laxiflora–Carpinus londoniana species complex. We conducted statistical analyses on 535 specimens encompassing the entire distribution of this species complex to identify phenotypic clusters. Additionally, we analyzed genetic divergence and probable hybridization between clusters using 76 individuals from 37 populations. Based on phenotypic and genetic clusters, we tentatively recognized four species: C. viminea, C. fargesii, C. laxiflora, and C. londoniana. Except for rare overlapping distributions between C. fargesii and C. londoniana, the redefined four species are mostly allopatric to each another based on their distributions. The morphological delimitation, species boundary and distribution of each species differ distinctly from past taxonomic treatments. For example, specimens previously identified under C. viminea, in fact, belong to three different species. Hybrids between C. fargesii and C. londoniana exhibit morphological traits similar to C. viminea, thereby contributing to difficulties in determining species boundaries and outlining species distributions. These findings suggest that local selection and geographical isolation may together have promoted both phenotypic and genetic divergences within this species complex. However, interspecific hybridization blurs species boundaries by producing hybrids with phenotypic similarity in addition to intraspecific variation. This study emphasizes the importance of statistical analyses of population‐level morphological and genetic variations across major distributional ranges for an integrative delimitation of species boundaries and the identification of hybridization and hybrids.

A Pragmatic Approach for Chlorine Decay Modeling in Multiple-Source Water Distribution Networks Based on Trace Analysis

Providing water with adequate quality to users is one of the main concerns for water utilities. In most countries, this is ensured through the introduction of disinfectants, such as chlorine, which are subjected to decay over time, with consequent loss of disinfection action and the possible formation of harmful by-products. In this context, water quality models can be a useful tool to support management and, thus, ensure sufficient standards in all network points, but most of these models require the input of reaction parameters which could be difficult to obtain based on the information available to water utilities, especially in the case of complex water distribution networks (WDNs) supplied by more than one source. This study proposes a pragmatic, interval-number-based method to model chlorine decay in complex WDNs by relying on the use of the network hydraulic model and the results of trace analysis, which are exploited to obtain overall reaction rates. The method is applied to the case of a real WDN supplied by water sources with different qualitative features. The results obtained highlight that the method can help water utilities in the identification of overall water quality parameters.

Dual-Interference Channels Static Fourier Transform Imaging Spectrometer Based on Stepped Micro-Mirror: Data Processing and Experiment Research

The use of a dual-interference channels static Fourier transform imaging spectrometer based on stepped micro-mirror (D-SIFTS) for environmental gas monitoring has the advantages of high throughput, a compact structure, and a stable performance. It also has the characteristics of both a broad spectral range and high spectral resolution. However, its unique structural features also bring many problems for subsequent data processing, mainly including the complex distribution of the interference data, the low signal-to-noise ratio (SNR) of infrared scene images, and a unique inversion process of material information. To this end, this paper proposes a method of image and spectra information processing and gas concentration inversion. A multiscale enhancement algorithm for infrared images incorporating wavelet denoising is used to obtain high-quality remote sensing scene images, and spectral reconstruction optimization algorithms, such as interference intensity sequence resampling, are used to obtain accurate spectral information; the quantitative calibration model of the detected gas concentration is established to achieve high-precision inversion of gas concentration, and its distribution is visualized in combination with the scene image. Finally, the effectiveness and accuracy of the data processing algorithm are verified through the use of several experiments, which provide essential theoretical guidance and technical support for the practical applications of D-SIFTS.

Mass spectrometric analysis of transition metal complexes formed through contact of artificial sweat with circulating Euro coins

AbstractIt is well‐known that some metals/alloys sensitize the skin of susceptible individuals. Trace amounts of metals and chemical compounds containing metal ions can affect human health, i. e., cause allergy or inflammation. However, the details of this metal‐induced allergic reaction are not well understood. Complexes of nickel, copper, and zinc are known to be easily formed through contact of metal alloys with ubiquitously available organic compounds such as triacylglycerols, amino acids, urea, and fatty acids found in skin secretions or skin care products. Thus, an analytical technique is required to characterize and identify metal complexes formed on metallic everyday products, ideally offering a chemically specific, laterally resolved view of allergy‐causing processes. Here we present a mass‐spectrometry‐based analytical workflow to characterize metal‐containing compounds formed on the surface of 1‐euro coins when in contact with artificial sweat. This study identified different metal complexes by using tandem mass spectrometry (MS/MS). Surface imaging of 1‐Euro coins employing laser‐based mass spectrometry revealed the lateral distribution of Zn, Cu, and Ni complexes with low‐micrometer resolution.

Flood Discharge Measurement by STIV Coupled with Maximum Entropy Method Using Parameter Estimation by Numerical Analysis

The coupling of surface velocity measurement by image analysis and the maximum entropy method (MEM) is a non-contact measurement technique to estimate river discharge during flood with high accuracy. The MEM can represent complex vertical velocity distributions including velocity dips, but there is a challenge that the measurement of the cross-sectional velocity distribution by Acoustic Doppler Current Profiler (ADCP), etc. must be required at least once to determine the entropy parameter, which is a specific value at the cross section. This study proposes a new framework for determining the entropy parameter at new measurement sites based on the results of 3-D flow simulations. The framework is applied to two measurement sites, and the cross-sectional velocity distribution and the discharge estimated by the MEM with the entropy parameter are evaluated. The result shows that the framework enables the setting of an appropriate entropy parameter at a given site, and that the discharge estimated using the entropy parameter has a relative error of less than 10% to those measured by the ADCP.

Design and FPGA implementation of a hyperchaotic conservative circuit with initial offset-boosting and transient transition behavior based on memcapacitor

A charge-controlled memcapacitive conservative chaotic circuit with trigonometric function terms is constructed in this paper. By analyzing the characteristics of the infinite equilibrium point and the divergence of the system, it is proved that the system is conservative. Under the influence of system parameters, hyperchaotic and chaotic characteristics can be switched to each other. With the increase of parameters and initial values, the Lyapunov exponent is also greatly improved. In addition, the transient chaos phenomenon accompanied by the internal crisis is also found, which leads to the sudden change of the orbit state of the attractor, and different types of coexisting attractors in different regions of the basins are also found. Through further analysis of the phase diagram, bifurcation diagram and time-domain waveform, it is found that there are three initial offset-boosting behaviors with different characteristics in the system. Finally, the spectral entropy (SE) algorithm is used to compare the memcapacitive circuit complexity under different initial conditions and system parameters. We find that the SE complexity distribution map is consistent with the Lyapunov exponent trajectory, which further reflects the influence of system parameters and initial conditions on the dynamic characteristics of the system. On the platform of Cyclone IV E series, the FPGA main chip of EP4CE10F17C8N is used to complete the hardware implementation of the system. The FPGA implementation results are consistent with numerical simulation, and verify the realistic feasibility of the memcapacitive conservative chaotic circuit in practical application.

Three-Dimensional Spatial Microscopic Characteristics and Developmental Influencing Factors of Tight Gas Layers in Hangjinqi Prospect Area, Ordos Basin, China

The unconventional tight oil and gas resources in the Xinzhao East belt of the Hangjinqi Prospect area in the Ordos Basin of China are abundant. However, the reservoir’s internal storage space is complex, and the microscopic pore throat structural features are not well recognized, which has led to some trouble in the deployment of oil and gas exploration. To reveal the microscopic characteristics of the dense sandstone gas layer in the first member of the Lower Stone Box Formation of the D-well Zone in the Xinzhao East belt of the Hangjinqi Prospect area, a three-dimensional space digital core was built, and the stored set spatial data were extracted, based on rock sheet and coring data and X-CT scanning technology. Quartz grain size was segmented and analyzed based on an adaptive approach. The microscopic characteristics of the gas layer in the studied section and the factors influencing its development were studied, combining the use of a field emission scanning electron microscope, helium porosimeter, and gas permeability meter. We found that in the studied section, the porosity is relatively high, the pore throat size is large, and the pore permeability correlation is good. The reservoir space, which consists of intergranular pores, intragranular pores, and microcracks at the grain edges in the study area, is characterized by a complex distribution pattern. Within the gas layer, isolated pores are connected by microcracks to form a network of reservoir spaces, which increases the pore throat size, enhances the connectivity of the pore throat, and makes the microscopic characteristics of the reservoir space better. The first member of the Lower Stone Box Formation could be an advantageous reservoir. Hole–throat connectivity is poor because of the gas layer having underdeveloped primary pores, the blockage of pores by unstable minerals (kaolinite, etc.), and poorly connected pore throats based on insoluble mud cementation. The high content of quartz brittle minerals and the development of natural microcracks within the gas formation are favorable conditions for fracking development. The quartz grain size within the gas layer is positively correlated with the pore throat size, which suggests that the quartz grain size somewhat influences the microscopic characteristics of the reservoir space. This comprehensive study shows that the methodology of the study is more advantageous than traditional methods in the fine and three-dimensional spatial characterization of the microstructure of dense sandstone reservoirs. The research results of this paper have certain guiding significance for further reservoir evaluation and advantageous reservoir prediction in the Hangjinqi Prospect area in the Ordos Basin. We also provide the basis for the subsequent efficient development of the gas reservoir.

A hybrid sampling method for highly imbalanced and overlapped data classification with complex distribution

Class imbalance and overlap coupling is the primary cause of performance degradation for classifiers. Unfortunately, it always occurs. In this paper, we propose a hybrid sampling method derived from optimized generative adversarial network and natural neighbor search (HD-GNNS) for this scenario. The new approach considers both the global distribution and local distribution of the minority class to improve the data distribution fundamentally. First, natural neighbor search with Fisher’s discriminant ratio is conducted to screen overlapped sample subset and remove noise samples. It effectively overcomes the parameter sensitivity by adaptively determining the search radius. Then, an encoder with squeeze and excite block is introduced into generative adversarial network, and the structure of generative adversarial network is optimized with cross-layer and low-rank matrix. It better captures the distribution characteristics of minority samples in overlapped subset for oversampling. Afterwards, the local density of majority samples in overlapped subset is calculated by the aforementioned natural neighbor search method, and Thornton’s Separation Index is used to implement under-sampling adaptively. We evaluate the proposed approach on 1 artificial dataset, 14 UCI datasets and 8 real-word datasets. The experimental results show that the proposed HD-GNNS exhibits more impressive performance compared to other benchmark methods.

Evolution Characteristics of Void in the Caving Zone Using Fiber Optic Sensing.

Addressing the issue of low filling efficiency in gangue slurry backfilling due to unclear evolution characteristics of voids in the overlying collapsed rock mass during mining, this study utilizes fiber optic sensing technology to monitor real-time strain changes within the rock mass. It proposes a void zoning method based on fiber optic sensing for mining the overlying rock and, in combination with physical model experiments, systematically investigates the dimensions, distribution, and deformation characteristics of rock mass voids. By analyzing fiber optic sensing data, the correlation between the rate of void expansion and the stress state of the rock mass is revealed. The research results demonstrate that as mining progresses, the internal voids of the rock mass gradually expand, exhibiting complex spatial distribution patterns. During the mining process, the expansion of voids within the overlying collapsed rock mass is closely related to the stress state of the rock mass. The rate of void expansion is influenced by changes in stress, making stress regulation a key factor in preventing void expansion and rock mass instability. The application of fiber optic sensing technology allows for more accurate monitoring of changes in rock mass voids, enabling precise zoning of voids in the overlying collapsed rock mass during mining. This zoning method has been validated against traditional theoretical calculations and experimental results. This research expands our understanding of the evolution characteristics of voids in overlying collapsed rock mass and provides valuable reference for backfilling engineering practices and backfilling parameter optimization.

Numerical simulation study on evolution law of three-dimensional fracture network in unconventional reservoirs

It has become a consensus that large-scale hydraulic fracturing is adopted to achieve the stimulation of unconventional oil and gas reservoir. The complex fracture network formed by fracturing is closely related to the effect of reservoir stimulation, which has extremely complicated evolution process. Therefore, it is necessary to study the evolution law of fracture network in large-scale hydraulic fracturing of unconventional reservoirs. In this article, the geological engineering parameters of horizontal well H in shale gas reservoir in southern Sichuan are taken as an example, a three-dimensional fracture network expansion model is established based on the boundary element method and finite volume method, and the simulation of the complex fracture network in a whole well section is carried out to analyze the evolution law of reservoir fracture network under different geological and engineering parameters. The results show that the horizontal stress field distribution has a significant effect on fracture geometric form. Hydraulic fractures in reservoirs with larger horizontal stress difference have stronger directivity, while the horizontal wellbore tends to obtain better reservoir stimulation results when it is parallel to the minimum horizontal principal stress setting. The conjugated natural fracture developed in the reservoir inhibits the hydraulic expansion fractures in both directions. Although it increases the complexity of the fractures, it is not necessarily conducive to improving the reservoir stimulation effectiveness. The lower the strength of natural fracture is, the more complex the fracture geometric form becomes, and the smaller the stimulated reservoir volume is. Correspondingly, the higher the strength of natural fracture is, the simpler the fracture geometric form becomes, and the larger the stimulated reservoir volume is. Suitable fracturing construction displacement can not only contribute to form a more complex fracture distribution, but also help to obtain a larger stimulated reservoir volume. The optimal construction displacement ranges from 10 to 14 m3/min. Low viscosity fracturing fluids are suitable for the formation of long-narrow fractures and able to connect with the remote reservoir and form complex fracture networks. Lower viscosity fluids can be used to achieve better reservoir stimulation effectiveness when sand-carrying capacity is met.