Field Data Sets Research Articles

Inversion of Gravity Anomalies by a Hybrid Metaheuristic Algorithm

In this work, we introduce application of a hybrid algorithm (DE/PSO) to estimate the model parameters from residual gravity anomalies due to some simple geometrical bodies. This algorithm combines differential evolution (DE) and particle swarm optimization (PSO). To investigate the performance of the hybrid algorithm, test studies were carried out using synthetic and field data sets. The synthetic data sets include noise-free and noisy synthetic anomalies. Two published gravity anomalies from Cuba and Canada were used as the field data. In the hybrid algorithm, DE and PSO yield [premature] solutions separately and share their best solutions during an iterative process. An openly accessible metaheuristics package (NMOF) in R programming environment was used to implement the hybrid algorithm. Through simulations using synthetic anomalies, DE/PSO algorithm was successful to provide improved results. In comparison to the solutions from the single algorithms (DE and PSO), the DE/PSO algorithm shows more effectiveness in terms of accuracy and convergence. The true model parameters of noise-free and noisy synthetic gravity anomalies were recovered well by the hybrid algorithm. The results of inversion for the field examples are characterized by low residual values between the observed gravity anomalies and the calculated ones.

Horizon picking algorithm using global optimization and coherence measurement in poststacked seismic data

Accurate horizon recognition within poststack seismic sections is important in seismic interpretation. Horizon picking techniques influence diverse seismic structural analyses and inversion methodologies. Despite encountering challenges such as computational demands and time constraints, the past few years have witnessed the development of numerous 2D and 3D methods. With the progress of modern computing, more robust and efficient techniques, harnessing artificial intelligence, have come to the fore. In this context, we develop an innovative cost-effective algorithm grounded in a global optimization framework. This algorithm combines the very fast simulated annealing method with a set of stability parameters and coherence measurements between neighboring seismic traces, which is used as the objective function. The search is executed on continuous and sequential clusters of seismic traces, with a focus on maximizing coherence amidst the presented events. We evaluate the algorithm’s efficacy by applying it to two distinct input data sets — enveloped and nonenveloped seismic traces. The initial application is to the time-migrated Marmousi data set, a synthetic marine data set originated from a complex geologic sedimentary basin situated in Angola, Africa. Subsequently, we apply the algorithm to depth-migrated land data extracted from a past survey conducted in the Tacutu Basin in northern Brazil. Outcomes arising from the application to 2D synthetic and field data sets underscore the method’s viability as a compelling alternative for seismic horizon picking within the time or depth domain.

Evaluation of urban drainage capacity using different satellite image sources and SWMM

ABSTRACT This research aims to cost-effectively identify inundation due to combined rainfall with tidal surges by evaluating the existing urban drainage system. The Storm Water Management Model (SWMM) applied two scenarios based on different Digital Elevation Model (DEM) sources. These are scenario A, i.e. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and scenario B, i.e. Shuttle Radar Topography Mission (SRTM). Then, catchment delineation and SWMM parameterization in the low-lying urban region of Chattogram City were executed. In contrast to scenario A, i.e. ASTER DEM, identified inundated nodes in the northwest part of the city, scenario B, i.e. SRTM DEM, showed more inundated nodes. In both scenarios, drainage capacity was observed in the field at two outlet points of Ispahani Khal (R2 = 0.91 and 0.85) and Chaktai Khal (R2 = 0.90 and 0.92), which reasonably mimics the model outcome. With an intensive field dataset, this model could provide relevant information for betterment.

The survey of industrial anomaly detection for industry 5.0

ABSTRACT With the impetus of Industry 5.0, smart manufacturing would witness the significant advancement of automation and digital transformation. Even though Deep Learning (DL) has been widely investigated in smart manufacturing to release the automation in industry 5.0, the difficulties still emerge in collecting the abundant defective labels and the appearance of new anomalies in the manufacturing system. Anomaly detection (AD) has emerged as a fundamental challenge in identifying irregularities within the manufacturing processes without rely on the large anomalous label data, but the discussion on the AD for smart manufacturing is still insufficient in industry 5.0. This research first discusses the context and definition of anomaly detection. Then, it focusses on DL-based AD method in industry areas and discusses the network design, advantages, and applications of the DL-based AD on industrial images. Additionally, the typical metrics used to evaluate DL-based methods and popular AD datasets in the industrial field have been described. Finally, the challenges and future prospects have been discussed.

Morphometry of non-tidal inlet (prorva) channels: Tendra-Dzharylgach barrier system, Black Sea Coast, Ukraine

This study focuses on relatively understudied coastal systems – non-tidal inlets along a barrier coast. We describe the morphological elements of these inlets (prorvas) along the northern coast of the Black Sea using an example of the Tendra-Dzharylgach barrier (TDB) system, Ukraine. Channels vary from 15 m to >700 m in width, with relatively shallow depths of 1.0–1.5 m (rarely >3 m). This is a first comprehensive overview of TDB breaches and their evolution as inlets, with dynamic relationships between the channel and its associated depositional elements (frontal and bay-side deltas). Comparisons are made with the analogs of the latter along tide-influenced coasts: ebb- and flood-tidal deltas, respectively. The region of interest is subject to the effects of both rapid geomorphological change and ongoing military conflict, making this field dataset of great relevance in complementing other remote sensing databases at nearby sites.

Seismic Imaging of the Arctic Subsea Permafrost Using a Least-Squares Reverse Time Migration Method

High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region.

The Reverse Effect of Reviewers’ Sentiment Tendency: The Role of Readers’ Empathy and Trust

Streamers have become a crucial component of consumers’ live shopping experience. Reviewers will transfer their emotions about the streamer to the product-after review. We innovatively verified the reverse effect of reviewers’ sentiment tendency toward streamer on the product sustained sale in the streamer’s live streaming. Results from a field data set and three lab experiments show that reviewers’ positive sentiment tendency toward the streamer has a negative effect on the readers’ purchase intention when the streamer resale this product in his live streaming. While reviewers’ negative sentiment tendency toward the streamer can enhance readers’ purchase intention to the product resold in the streamer’s live streaming. These effects are separately mediated by readers’ trust in the review and their empathy to the streamer. And the two mediators are moderated by the streamer’s expertise. Our findings expand the literatures about reviews’ emotions and suggest implications for streamers, retailers, and review platforms faced with the task of managing live-shopping reviews.

Reservoir oriented migrated seismic image improvement and poststack seismic inversion using well-seismic mistie

In seismic exploration of subsurface hydrocarbon reservoirs, migrated seismic image is the foundation for structural interpretation and subsequent seismic inversion for reservoir properties. Seismic events in migrated images can be mispositioned due to several factors including seismic velocity inaccuracy. The mispositioned events lead to poor well-seismic tie and degrades the accuracy of subsequent inversion and reservoir prediction. Particularly, the mistie can be quite obvious for the mid- and mature- phases of the reservoir development after many wells drilled. We propose to utilize the information from mistie between seismic images and synthetic traces to improve the accuracy of migrated seismic images and thus inversion results. Our method includes three major steps: firstly, calculate time shifts between the synthetic traces and the co-located seismic trace using the path-limited dynamic time warping (PDTW) algorithm; then compute the time shift volume for the entire migrated image using the seismic-driven modeling approach based on the time shift traces at the well locations; apply the time shift volume to update the migrated image. The improved seismic volume can be used for further processing such as seismic inversion, which makes the inversion results more consistent with seismic and well data. We apply our method to synthetic and field datasets. The results demonstrate that the updated seismic image has an improved seismic well tie and an improved inversion result. Our method indicates a venue for a better honor of seismic and well data in reservoir characterization.

Feasibility of Identifying Shale Sweet Spots by Downhole Microseismic Imaging

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic data. In this study, an alternating method is proposed to determine microseismic event locations, 3D P-wave velocity, and Poisson’s ratio models with data recorded from downhole monitoring arrays. The method combines the improved 3D traveltime tomography, which inverts P and S arrivals for 3D P-wave velocity and Poisson’s ratio structures simultaneously, and a 3D grid search approach for event locations in an iterative fashion. The traveltime tomography directly inverts the Poisson’s ratio structure instead of calculating the Poisson’s ratios from P- and S-wave velocities (i.e., Vp and Vs) that are inverted by conventional traveltime tomography separately. The synthetic results and analysis suggest that the proposed method recovers the true Poisson’s ratio model reasonably. Additionally, we apply the method to a field dataset, which indicates that it may help delineate the reservoir structure and identify potential shale sweet spots.

Seasonal biophysical interactions in tidal marsh evolution: insights from a synchronized dataset in Jiangsu, China

IntroductionTidal marsh wetlands provide essential and valuable services to the wider interconnected marine and coastal environment, although the complex intertwined processes in morphological evolution remain insufficiently understood owing to synchronized data scarcity, limiting the development of numerical models and management strategies.MethodsThis study investigated the hydrodynamic, biological, sediment and morphological processes on the Doulong tidal wetlands, Jiangsu, China, using a one-year field dataset that captured spatial and seasonal variations.Results and discussionOur results indicate that biophysical interactions among multiple processes could result in some overlooked sedimentary behaviours and bio-morphological patterns in tidal marsh wetlands. Firstly, the dominance of alongshore currents caused a rapid alongshore expansion of saltmarsh patches, by which the marsh edge achieved seaward advancing, markedly different from the widely reported cross-shore expansion. Secondly, results showed that the particle size of sediment near the marsh edge coarsened when plants withered and then fined when plants grew, indicating that the seasonal variation trend of sediment grain size in saltmarshes was opposite to the trend of vegetation biomass. Thirdly, the interaction between vegetation and stranded marine debris formed banded debris zones within the saltmarsh, where debris bands could cause a biomass reduction of up to 58%, disrupting the commonly-observed parabolic biomass-elevation relationship. Meanwhile, the seasonal variation of vegetation and hydrodynamics could alter the debris positions and hence result in the formation of multiple parallel debris bands. Overall, this study provides a synchronized dataset and elucidates specific bio-morphological relationships and processes that have thus far not been systematically documented, enhancing the comprehensive understanding of tidal marsh wetland evolution.

MAFDE-DN4: Improved Few-shot plant disease classification method based on Deep Nearest Neighbor Neural Network

Deep learning-based methods for accurately identifying plant diseases can be effective in improving crop yields. However, the effectiveness of these methods heavily relies on the availability of large-scale manually labeled datasets, which present technical and economic challenges. Few-shot learning methods can be generalized to new categories with a small number of samples, which is very promising in the field of plant disease recognition despite the limited sample size. However, due to the complexity of real-life scenarios, distribution of disease leaves, and significant intra-class variability and inter-class similarity resulting from crop species and disease species, existing methods perform poorly in the field of plant disease recognition. To address the above problems, this paper proposes an improved multi-scale attention fusion with discriminative enhancement deep nearest neighbor neural network MAFDE-DN4 based on DN4. Our approach makes three main contributions. First, we have designed a bidirectional weighted feature fusion module (BWFM) to enhance the aggregation of fine-grained features and enhance the network’s representation of complex disease images. Second, to tackle the issue of sparse feature descriptors being vulnerable to irrelevant noise in small sample conditions, a episodic attention module (EA) has been developed to produce scene category-relevant attention maps. This effectively mitigates the influence of irrelevant background information. Finally, we introduce additional spacing between category margins to enhance the original softmax loss function, amplify the inter-class differences to reduce the intra-class distances, and add L2 regularization constraint terms to stabilize the training process. To simulate different real-world scenarios, we set up different dataset settings. Under the 1-shot task and the 5-shot task, our method achieves 57.5% and 81.41% accuracy under the within-domain strategy and 36.54% and 51.23% accuracy under the cross-domain strategy. The experimental results show that our method outperforms existing related work in the field of plant disease recognition, whether it is a dataset with a single background or a field dataset with a complex background in a real scenario. MAFDE-DN4 based on Few-shot learning requires substantially less data on new categories of plant diseases.

2D Magnetotelluric Inversion Using Linear Finite Element Methods and a Discretize‐Last Strategy With First and Second–Order Anisotropic Regularization

AbstractWe present a new inversion scheme for 2D magnetotelluric data. In contrast to established approaches, it is based on a mesh‐free formulation of the Quasi‐Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) iteration which uses the cost function gradient to implicitly construct approximations of the Hessian inverse to update the unknown conductivity. We introduce conventional first–order regularization as well as second–order regularization where inversions based on the latter are more appropriate for sparse data and can be read as maximum likelihood estimation of the unknown conductivity. We apply first–order finite element method (FEM) discretizations of the inversion scheme, forward and adjoint problems, where the latter is required for the construction of the cost function gradients. We allow for unstructured first–order triangular meshes supporting an enhanced ground level resolution including topographical features and coarsening at the far field leading to significant reduction in computational costs from using structured mesh. Formulating the inversion iteration in continuous form prior to discretization eliminates bias due to local refinements in the mesh and gives way for computationally efficient sparse matrix techniques in the implementation. A keystone in the new scheme is the multi‐grid approximation of the Hessian of the regularizations to construct efficient preconditioning for the inversion iteration. The method is applied to the Commeni4 benchmark and two field data sets. Tests show that for both first and second–order regularization an anisotropic approach is important to address the vast differences in horizontal and vertical spatial scale which in conventional approaches is implicitly introduced through the elongated shape of grid cells.

An integrated dataset of near-surface Eulerian fields and Lagrangian trajectories from an ocean model

A dataset consisting of numerically simulated oceanic velocities and sea surface height changes, provided conjointly from Eulerian and Lagrangian points of view, is made available as cloud-optimized archives on a cloud storage platform for unrestricted access. The Eulerian component of the dataset comprises oceanic velocity components at 0 m and 15 m depth, as well as total and steric sea surface height changes, obtained at hourly time steps for one year, with an approximate horizontal resolution of 1/25 degree on an irregular global geographical spatial grid, from the HYbrid Coordinate Ocean Model. The Lagrangian component of the dataset comprises the trajectories of particles advected in the Eulerian velocity field of the model. The particles were advected forward and backward for 30 days from a regular 1/4 degree grid in order to achieve 60-day long trajectories at 0 m and 15 m depths, with start times separated by 30 days, in 11 releases. This integrated dataset may help to link Eulerian and Lagrangian observational perspectives.

A series of regression models to predict the weathering index of tropical granite rock mass

In the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is(50)), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is(50), and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models’ implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weathering grade and rock mass properties, which will affect excavatability in granitic areas.

Shoaling Internal Tides Propagating From a Shallow Ridge Modulated by the Kuroshio

AbstractThis study presents remotely generated internal tides that propagate into shallow regions and are modulated by background flows using numerical simulations and field observation data. Numerical results indicate that strongly enhanced semi‐diurnal (∼M2) internal tide energy is generated over a shallow ridge, the Izu‐Ogasawara Ridge. The generated internal tides propagates toward the Kuroshio upstream and shoal toward shallow regions near Cape Shiono‐Misaki. The intensified internal tide energy flux toward the cape is explained by two mechanisms: (a) intensified internal tide generation over the ridge due to the interaction between tides and the Kuroshio and (b) wave energy convergence along the Kuroshio due to wave refractions. A 13‐year field data set obtained from the cape was used to investigate shoaling internal tides influenced by the Kuroshio. The observed results reveal a significant positive correlation between the kinetic energy of semi‐diurnal internal tides and the background flows caused by the Kuroshio, which evidently supports the intensified internal tides attributed to the interaction between background flows and tides, as proposed by recent studies. The intensity of shoaling internal tides is also largely influenced by the path of the Kuroshio and seasonal effects. The magnitude of shoaling internal tides is clearly weaken as the Kuroshio meander occurs. Shoaling internal tides modulated by the Kuroshio can provide new insights into energy transport and mixing processes in coastal oceans.

Research on Intelligent Ventilation System of Metal Mine Based on Real-Time Sensing Airflow Parameters with a Global Scheme

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety.

An improved dataset of force fields, electronic and physicochemical descriptors of metabolic substrates

In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present MetaQM, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. MetaQM comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.

Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach

Wind energy, an essential component in the fight against climate change, relies heavily on precise, detailed wind field simulations to optimize wind farms, particularly in complex terrain with intricate wind patterns. However, conventional high-resolution simulations come with a hefty computational cost, limiting their applicability in real-time decision-making. This research addresses this challenge by proposing a machine learning-driven, computationally efficient approach utilizing a modified Generative Adversarial Network. The contribution of this work is threefold. Firstly, we provide access to a unique high-resolution dataset of wind fields in complex terrain. Secondly, we introduce a knowledge-based modification to the loss function, ensuring that the algorithm captures crucial characteristics of the flow within complex terrains. Finally, we demonstrate the potential of our approach to enhance wind flow resolution in real-life wind farms. Through this, our method delivers comparable accuracy to high-resolution simulations while substantially reducing computational demands. This advancement greatly enhances the accessibility and efficiency of high-resolution wind field simulations, facilitating real-time optimization of wind farms. Moreover, we illustrate that by designing an appropriate loss function informed by domain knowledge, we can mitigate the need for adversarial training.

Towards real-world state of health estimation: Part 2, system level method using electric vehicle field data

Accurate battery health estimation is pivotal for ensuring the safety and performance of electric vehicles (EVs). While predominant research has centered on laboratory-level single cells, the accurate estimation of battery system capacity using real-world data remains a challenge, due to the vast diversity in battery types, operating conditions, data recordings, etc. To this end, we release three large-scale field datasets of 464 EVs from three manufacturers, comprising over 1.2 million charging snippets. The EVs’ capacity and State of Health (SOH) are effectively labeled using K-means to cluster and concatenate charging snippets. A robust data-driven framework integrating a Gated Convolutional Neural Network (GCNN) for estimating battery capacity is proposed, and the results outperform other machine learning models. In addition, a fine-tuning technique is employed to further enhance model efficacy on new datasets and with limited training data. This research not only advances battery health estimations but also paves the way for broader applications in battery management systems (BMSs), offering a scalable solution to real-world challenges in battery technology.

BVTED: A Specialized Bilingual (Chinese–English) Dataset for Vulnerability Triple Extraction Tasks

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset’s value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence.