Field Data Sets Research Articles

Automatic velocity picking based on optimal key points tracking algorithm

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

Cross-domain transfer learning for weed segmentation and mapping in precision farming using ground and UAV images

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains.

Fast estimation of airflow distribution in an urban model using generative adversarial networks with limited sensing data☆

Fast estimation of instantaneous urban airflow distribution can protect pedestrians and objects from high-speed winds and promote high-efficiency natural ventilation. Statistical values based on instantaneous airflow distribution can also provide valuable data for wind environment assessments and urban construction. In this study, we evaluated the potential of generative adversarial networks (GANs), a deep-learning-based generative model that learns the probability distribution of training data and generates new data for estimating instantaneous flow fields in an urban model. It utilizes the velocity obtained from sensors as input, and we investigated the influence of constraints on the estimated results. We used two non-linear models based on GANs: Wasserstein GAN with gradient penalty (WGAN), which has no constraints, and conditional WGAN (CWGAN), which has constraints. We employed a flow field dataset obtained using a large-eddy simulation (LES) for model training and testing. The feasibility and accuracy of the flow field generated by the GANs were verified using the results of the LES and proper orthogonal decomposition–linear stochastic estimation (POD–LSE). For the estimated results, although the WGAN learned the probability distribution of the training data, it was unable to determine the temporal relationship among the estimated data. Conversely, the CWGAN successfully estimated the airflow distribution data for reconstruction and prediction, including the temporal relationship. Furthermore, the statistical quantities of the results from the CWGAN were closer to those of the LES than those of the POD–LSE.

Swin Transformer for simultaneous denoising and interpolation of seismic data

Seismic data are often characterized by low quality due to noise contamination or missing traces. Convolutional neural networks are popular in dealing with denoising and interpolation. However, fixed-size convolution kernels have limited feature extraction range, and popular networks aim at either denoising or interpolating. In this paper, we propose a Swin Transformer convolutional residual network (SCRN) for simultaneous denoising and interpolation. We step-by-step show on synthetic datasets that SCRN can effectively denoise, interpolate, or denoise and interpolate simultaneously. Following this, the trained simultaneous denoising and interpolation model is directly used to handle multi-tasks on the field datasets. We assess the reconstruction effect of SCRN based on synthetic and field datasets. Comparison methods include both supervised ones (DnCNN, Unet and RIDNet) and unsupervised ones (DDUL, MultiResUNet and DL-POCS). Experimental results show that SCRN outperforms the counterparts in terms of (1) quantitative evaluation indices, (2) event continuity and weak signal retention, (3) generalization seismic data ability to suppress various noise levels and interpolate different sampling rates, and (4) preservation of first-arriving edge signals. These results indicate that SCRN can effectively reconstruct seismic data.

Improving image resolution using deabsorption prestack time migration with effective Q estimation: A back-propagation network approach

The conventional time migration method does not consider the attenuation caused by the viscoelasticity of the underground media during the imaging process. Therefore, the final imaging amplitude and phase include inaccuracies caused by attenuation. In this study, we develop a migration scheme to compensate for absorption and dispersion using an effective quality factor ( Q) estimation based on a back-propagation (BP) neural network. We use BP neural network technology to automatically estimate the effective Q value from stacked imaging data, thereby avoiding manual Q estimation using conventional methods. Our scheme can be incorporated into conventional seismic data-processing workflows. Furthermore, synthetic and field data sets are used to validate our scheme, which is used to acquire high-resolution images with low noise levels. In addition to developing a completely data-driven Q-value estimation strategy, this study demonstrates close integration of artificial intelligence, data mining, and conventional geophysics; our approach is appropriate for estimating the effective Q and has strong industrial application value and significance.

Seismic data interpolation via Unet with non-local blocks

Due to the constraints of the exploration environment, acquired seismic data are commonly irregular or incomplete, which seriously affects the quality of subsequent seismic data processing and interpretation. Convolutional neural network (CNN)- based seismic data interpolation methods have been receiving increased attention owing to better efficiency and interpolation performance. UNet is a commonly used encoder-decoder network that uses skip connection, which can extract seismic data features using convolution filters. However, convolution filters are local operations that process a local neighbourhood and extract local features, but experience difficulty capturing global representations. Although an increase in network depth can expand the receptive field, the local operator still limits the precision of the recovered result. To address this limitation, we propose an improved UNet with a non-local block (NL-UNet), wherein non-local blocks are added to the classic UNet network. Owing to the addition of non-local blocks, NL-UNet can capture both local and non-local features of seismic data, which is conducive to improving its reconstruction performance. The applicability and effectiveness of NL-UNet were demonstrated through experiments on both synthetic and field datasets.

Ultra‐resolution surface‐consistent full waveform inversion

AbstractFull waveform inversion for land seismic data requires the development of specific strategies for modelling the complex response associated to the near surface. Seismic wave propagation is distorted by several effects, such as topographic relief, wavefield scattering, attenuation (often frequency‐dependent) and anisotropy. The modelling of such shallow complexities is often unmanageable by parametric inversions such that data preconditioning and surface‐related corrections are required before tackling the full waveform inversion velocity modelling problem. We developed a unified framework addressing both surface‐consistent corrections and full waveform inversion by using the transmitted portion of the wavefield. The problem of surface‐consistent decomposition is recast in terms of the transmitted wavefield leading to kinematic and dynamic corrections to account for sub‐resolution wave propagation distortions occurring in the near surface weathering layer. Seismic data are deconvolved from the effects of the near surface to better represent the deeper subsurface wavefield propagation. Signal‐to‐noise enhancement is obtained after the surface‐consistent transmission preconditioning by the generation of virtual super gathers reconstructed in the midpoint‐offset sorting domain. An original scheme of 1.5D Laplace–Fourier full waveform inversion, involving 3D radiation and 1D velocity inversion, is then applied for the velocity reconstruction where the amplitude information of the seismic data is fully preserved and utilized. The unified approach of surface‐consistent transmission preconditioning and velocity modelling is demonstrated on the Society of Exploration Geophysicists Advanced Modelling arid model dataset as well as on two complex field datasets containing near surface complexities typical of arid regions. The approach provides the solution of the near surface distortions by performing data preconditioning and velocity inversions in a unified scheme. The surface‐consistent full waveform inversion approach has been utilized for large land seismic projects and represents a robust tool for supporting land seismic imaging.

Borehole-driven sparse-spike deconvolution and seismic bandwidth enhancement

Reflectivity estimation approaches are usually driven solely by observed seismic data, leaving other types of a priori information, such as well-log data, out of the equation. This may result in solutions that honor the observed data but are not properly linked to geology. We provide a method for estimating sparse-spike reflectivity solutions that takes into account the observed data and any given sparse-spike model derived from well-log data. Starting with the well-log measurements, we estimate the reflectivities trace by trace by iteratively perturbing the previous solution with a warping function. This procedure is repeated until the synthetic trace formed by convolving with an appropriate wavelet fits the observed data. The assumption that two consecutive reflectivities have the same number of spikes with similar amplitudes and positions ensures lateral continuity. Because it is expected that reflectivity fluctuates smoothly from trace to trace, the technique is appropriate for areas with low to moderate structural complexity. The resulting sparse-spike reflectivity section can be considered as a product in and of itself. Even so, the interpreter might prefer a borehole-consistent frequency-enhanced seismic image, which can be easily produced by convolving the reflectivity with a suitable broadband wavelet. We rigorously test the technique on 1D and 2D synthetic data as well as on a 3D poststack field data set. The results indicate that we can obtain sparse-spike reflectivities with good lateral continuity that honor the observed data. Most importantly, the resulting reflectivity section (or frequency-enhanced data) is guaranteed to honor the well-log information too, which may aid users in seismic data analysis and interpretation.

An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset

Deep learning has gained widespread adoption in various fields, including object recognition, classification, and precision agriculture. This study aimed to investigate the use of deep convolutional neural networks for the real-time identification of diseases in tomato plant leaves. A customized field dataset was constructed, consisting of several images of tomato leaves captured using a mobile phone from agricultural fields in the Kerala and Tamil Nadu regions and classified into two categories: healthy and diseased. A YOLO v5 deep learning model was trained to classify images of tomato leaves into the respective categories. This study aimed to determine the most effective hyperparameters for the classification and detection of healthy and sick leaves sections, using both proprietary and publicly available datasets. The YOLO v5 model demonstrated a notable accuracy rate of 93% when evaluated in the test dataset. This method can help farmers quickly recognize diseased leaves and prompt the implementation of preventive measures to curtail the spread of tomato plant diseases.

Dispersal and grain size characteristics of the May 14, 2018 Shinmoedake eruption deposit, Kirishima Volcano, Japan, based on post-eruption field survey and meteorological datasets

This study describes the dispersal and grain size characteristics of the May 14, 2018 Shinmoedake eruption deposits of Kirishima Volcano in southern Kyushu, southwestern Japan. We discuss the eruption sequence, including the temporal variations in the behavior of the plume, by combining field and meteorological datasets. Following a magmatic activity in 2011 characterized by a substantial change in the eruption style (from subplinian eruptions to lava effusion) and subsequent vulcanian explosions, the Shinmoedake crater experienced intermittent eruptions in 2018. The May 14, 2018 eruption began at 14:44 with a vulcanian eruption, with the eruption plume rising 4500 m above the crater rim. Thereafter, it transitioned to an ash eruption; the plume height decreased gradually until the eruption ceased at 16:10. The tephra fall deposits were distributed more than 27 km to the southeast of the source crater; the mass of the tephra fall deposit was approximately 2.1 × 107 kg, calculated based on an isomass map. The deposit incidence differed between the east and west sides of the major dispersal axis. The deposits found east of the main dispersal axis were primarily composed of coarse to medium sand-sized particles with no fine fraction (fine sand to silt in size). In contrast, the deposits west of the axis were finer-grained than those east of the axis. We analyzed photographs of the eruption plume, along with the regional meteorological data and the dispersal and grain-size characteristics of the deposits, and reached the following conclusion: during the May 14, 2018 eruption, the wind directions above the Shinmoedake crater fluctuated across altitudes. The westerly winds dispersed the eruption plume that rose to a higher altitude, containing coarser tephra associated with the initial vulcanian eruption, further to the east rather than along the main axis. In contrast, a lower-altitude ash eruption plume that was rich in fine materials was dispersed westward rather than along the main axis, which was influenced by northerly winds. The findings of this study can support the analysis of similar volcanic events.Graphical

Time‐lapse applications of the Marchenko method on the Troll field

AbstractThe data‐driven Marchenko method is able to redatum wavefields to arbitrary locations in the subsurface, and can, therefore, be used to isolate zones of specific interest. This creates a new reflection response of the target zone without interference from over‐ or underburden reflectors. Consequently, the method is well suited to obtain a clear response of a subsurface reservoir, which can be advantageous in time‐lapse studies. The isolated responses of a baseline and monitor survey can be more effectively compared; hence, the retrieval of time‐lapse characteristics is improved. This research aims to apply Marchenko‐based isolation to a time‐lapse marine data set of the Troll field in Norway in order to acquire an unobstructed image of the primary reflections and retrieve small time‐lapse traveltime difference in the reservoir. It is found that the method not only isolates the primary reflections but can also estimate internal multiples outside the recording time. Both the primaries and the multiples can then be utilized to find time‐lapse traveltime differences. More accurate ways of time‐lapse monitoring will allow for a better understanding of dynamic processes in the subsurface, such as observing saturation and pressure changes in a reservoir or monitoring underground storage of hydrogen and CO2.

KULF-TT53: A Display-Specific Turntable-Based Light Field Dataset for Subjective Quality Assessment

Light field datasets enable researchers to conduct both objective and subjective quality assessments, which are particularly useful when acquisition equipment or resources are not available. Such datasets may vary in terms of capture technology and methodology, content, quality characteristics (e.g., resolution), and the availability of subjective ratings. When contents of a light field dataset are visualized on a light field display, the display system matches the received input to its output capabilities through various processes, such as interpolation. Therefore, one of the most straightforward methods to create light field contents for a specific display is to consider its visualization parameters during acquisition. In this paper, we introduce a novel display-specific light field dataset, captured using a DSLR camera and a turntable rig. The visual data of the seven static scenes were recorded twice by using two settings of angular resolution. While both were acquired uniformly within a 53-degree angle, which matches the viewing cone of the display they were captured for, one dataset consists of 70 views per content, while the other of 140. Capturing the contents twice was a more straightforward solution than downsampling, as the latter approach could either degrade the quality or make the FOV size inaccurate. The paper provides a detailed characterization of the captured contents, as well as compressed variations of the contents with various codecs, together with the calculated values of commonly-used quality metrics for the compressed light field contents. We expect that this dataset will be useful for the research community working on light field compression, processing, and quality assessment, for instance to perform subjective quality assessment tests on a display with a 53-degree display cone and to test new interpolation methods and objective quality metrics. In future work, we will also focus on subjective tests and provide relevant results. This dataset is made free to access for the research community.

First Break Traveltime Picking Using Unsupervised Machine Learning and Super‐Virtual Refraction Interferometry

AbstractAn accurate and trustworthy first break picking plays a key role in static correction calculations, velocity analysis, and deconvolution. First break traveltimes picking accuracy ensures correct and reliable seismic data processing results. Many methods are available to perform the first break pickings automatically. Most of these methods, however, are not robust when there is a low signal‐to‐noise ratio (SNR). In this study, to automatically determine the traveltimes of the first arrivals, we used the density‐based spatial clustering application with noise (DBSCAN) and the super‐virtual refraction interferometry (SVI). In the case of noisy data, using only DBSCAN for first break picking shows poor accuracy. To deal with this issue, SVI is employed to improve the SNR of the data, and then DBSCAN is applied again to the enhanced data for accurate picking. In the first step, DBSCAN is used to define a muting window for SVI. In the second step, SVI is employed to improve the first arrival SNR. In the third step, DBSCAN is applied to determine the final first arrival traveltime. The proposed approach is tested on synthetic and field data sets, where the manual and the cross‐correlation pickings are compared to the DBSCAN automatic pickings. We further test the proposed approach on a publicly available data set to benchmark our technique for first‐break accuracy with other established methods. Results show that the first arrival traveltime picks using the proposed approach are very accurate compared to manual picking.

Attenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach

AbstractThe presence of the air–water interface (or free‐surface) creates two major problems in marine seismic data for conventional seismic processing and imaging: free‐surface multiples and ghost reflections. The attenuation of free‐surface multiples remains one of the most challenging noise attenuation problems in seismic data processing. Current solutions suffer from the removal of the primary events along with the multiple events especially when the primary and multiple events overlap (e.g., adaptive subtraction). The effective attenuation of ghost reflections (or deghosting) requires acquisition‐ and/or processing‐related solutions which generally address the source‐side and receiver‐side ghosts separately. Additionally, an essential requirement for a successful implementation of free‐surface multiple attenuation and seismic dehosting is the requirement of dense seismic data acquisition parameters which is not realistic for two‐dimensional and/or three‐dimensional marine cases. We present a convolutional neural network approach for free‐surface multiple attenuation and seismic deghosting. Unlike the existing solutions, our approach operates on a single trace at a time, and neither relies on the dense acquisition parameters nor requires a subtraction process to eliminate free‐surface multiples, and it removes both the source ghost and receiver ghost simultaneously. We train a network using subsets of the Marmousi and Pluto velocity models and make predictions using subsets of the Sigsbee velocity model. We show that the convolutional neural network predictions give a correlation coefficient of 0.97 on average with the numerically modeled data for the synthetic examples. We illustrate the efficacy of our convolutional neural network–based technique using the Mobil AVO Viking Graben field data set. The application of our algorithm demonstrates that our convolutional neural network–based approach removes different orders of free‐surface multiples (e.g., first and second orders) and recovers the low‐frequency content of the seismic data (which is essential for, for instance, full‐waveform inversion applications and broadband processing) by successfully removing the ghost reflections while preserving and increasing the continuity of the primary reflection.

Surface Measure to Depth (SMeTD): a new low-budget system for 3D water temperature measurements for combining with UAV-based thermal infrared imagery

Characterising spatial patterns in water temperature is important for monitoring aquatic habitats and understanding physical and biogeochemical processes to support environmental management decisions. As freshwater bodies exhibit high spatial and temporal variability, high-resolution 3D temperature data are essential to understand local anomalies. The acquisition of simultaneously high spatial and temporal datasets in the field has so far been limited by costs and/or workload associated with commonly used monitoring systems.We present a new, low-cost, spatially and temporally flexible 3D water temperature monitoring system, Surface Measures to Depth (SMeTD). SMeTD can be used to provide information on the relation of water surface temperature to changes with depth, characterise water temperature in 3D and ground truth remotely sensed thermal infrared data. The systems performance was tested under laboratory conditions and under controlled conditions in the field. This revealed an accuracy comparable to established but more expensive monitoring systems. Field testing of SMeTD involved 1-min data collection of 3D water temperature for a full diurnal cycle in a lake. The 3D temperature patterns were supported by a thermal infrared image of the lakes surface. The field dataset demonstrated higher water temperatures and higher water temperature variation at the surface compared to deeper layers. SMeTD can be used to observe a broad range of hydrological processes in natural and artificial aquatic environments and help to understand processes involved with energy budgets, infiltration, limnology, or groundwater surface water exchange.

Correlation warping radius tracking for condition monitoring of rolling bearings under varying operating conditions

Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful and effective HI applicable for nonstationary operating condition is studied in this paper. Inspired by chaos fractal theory (CFT) and slowly varying damage dynamics theory (SVDDT), this paper calculates the range of correlation warping radius (CWR) of each segment of signal through phase space reconstruction, and then finds that the boundary of correlation warping radius range (CWR-B) in normal conditions follows a fixed linear relationship. Through the application of this linear relationship, a new health monitoring indicator for vibration signals of rotating machinery has been proposed, through correlation warping radius tracking measurement (CWRTM). The proposed indicator not only has outstanding monitoring advantages for rotating machinery in a cyclo-stationary condition, but also applicable for machinery condition monitoring under variable operating conditions, achieving the unity of methods for condition monitoring in stationary and variable operating conditions. Compared with some typical health indicators, the experimental results on bearing run-to-failure dataset and wind turbine generator bearing field dataset show that the proposed indicator in this article is not only sensitive to early-stage fault, but also validated robust to varying operating conditions.

Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies

The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%).

Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data

Abstract. Soil and underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored to predict the response of sloping soil to precipitation. The case of a pyroclastic coarse-grained soil mantle overlaying a karstic bedrock in the southern Apennines (Italy) is described. Field monitoring of stream level recordings, meteorological variables, and soil water content and suction has been carried out for a few years. To enrich the field dataset, a synthetic series of 1000 years has been generated with a physically based model coupled to a stochastic rainfall model. Machine learning techniques have been used to unwrap the non-linear cause–effect relationships linking the variables. The k-means clustering technique has been used for the identification of seasonally recurrent slope conditions in terms of soil moisture and groundwater level, and the random forest technique has been used to assess how the conditions at the onset of rainfall controlled the attitude of the soil mantle to retain much of the infiltrating rainwater. The results show that the response in terms of the fraction of rainwater remaining stored in the soil mantle at the end of rainfall events is controlled by soil moisture and groundwater level prior to the rainfall initiation, giving evidence of the activation of effective drainage processes.

You fooled me, so I’ll tell you about myself! personnel-related brand betrayal experiences and disclosure of personal information

Past research has extensively studied the negative effects of brand betrayals on consumer attitudes, but largely ignored their potential positive consequences. Also, while previous research has focused on betrayals made by the brand itself, it has paid less attention to betrayals by the brand’s personnel. This paper focuses on one potentially important positive consequence of brand personnel betrayal experiences (a consumer’s feeling of being betrayed by the brand staff members): the increased willingness of consumers to share personal insights and information with the brand after experiencing a brand personnel betrayal. A field data set and two online experiments show that consumers are more prepared to share personal information with the brand when experiencing brand personnel betrayals than when experiencing other types of service or brand failures. The effect is mediated by consumers’ social affirmation mindset and moderated by privacy concerns.

An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow

Animation visualization is one of the primary methods for analyzing unsteady flow fields. In this paper, we addressed the issue of data visualization for large-scale unsteady flow fields using animation. Loading and rendering individual time steps sequentially can result in substantial frame delay, whereas loading and rendering all time steps simultaneously can result in excessive memory usage. To address these issues, the proposed method analyzes the variable description information in the data files to bypass redundant variables and read the flow field data as required. Second, a hash table is constructed to derive the two-dimensional surface mesh of the flow field and complex mesh cells are simplified into simple linear cells to reduce the mesh’s complexity. This paper presents a method for reducing the memory usage of complex data sets by more than 90%, compared with the ParaView data reading method. The proposed method is tested on four sets of unstructured unsteady flow field data with different data structures. The animation visualization method based on simplified data can achieve an average frame rate of less than 100ms and supports real-time user interaction on personal computers. It extends the ability of personal computers to analyze large-scale unstructured unsteady flow fields.