Shot Data Research Articles

Numerical experiments on hydrogen storage characterization in porous media using elastic full-waveform inversion

In recent years, underground hydrogen storage (UHS) has garnered significant attention as a concept for large-scale energy storage. Developing hydrogen characterization and monitoring methods specific to each target geologic formation (e.g., salt caverns, reservoirs in depleted oil/gas fields, and saline aquifers in porous media) is crucial for the safe operation of hydrogen storage. However, due to the limited number of actual operations and laboratory experiments (e.g., rock physics for hydrogen) related to UHS in porous media, the specifications required for seismic monitoring (e.g., P-/S-wave velocity and density changes generated by hydrogen injection) are not yet fully understood. A recent study showed the results of laboratory experiments on cyclic UHS in porous media. The experiments demonstrated that P-wave velocity decreases nonlinearly with increasing hydrogen saturation in sandstone specimens, with a reduction of up to 3.5% observed at 36% hydrogen saturation. This study presents hydrogen characterization in porous media with conditions similar to the experiment using elastic full-waveform inversion (FWI) for time-lapse surface seismic data. The shot data are generated by seismic forward modeling for several synthetic subsurface models that incorporate a hydrogen plume. The hydrogen plume assumes four velocity-density scenarios, partially based on the laboratory study results. Numerical experiments are conducted to explore the validity and challenges of hydrogen characterization using elastic FWI.

Golf Club Selection with AI-Based Game Planning.

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player's maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome.

Data streaming infra-red video bolometer (IRVB) of Korea Superconducting Tokamak Advanced Research (KSTAR).

Due to the increasing demands for active plasma control operations, in situ diagnostics are highly sought after. Tungsten plasma-facing components have been utilized in the Korea Superconducting Tokamak Advanced Research (KSTAR) lower divertor since the 2023 campaign. Plasma radiation is a key parameter for plasma control, especially in radiation front control experiments. Therefore, the KSTAR infra-red video bolometer (IRVB) needs to be reconfigured into an in situ data streaming diagnostic. This requires comprehensive changes in both infra-red camera control and data analysis compared to the previous system. To ensure the stability of the reconfigured system, functional parts are grouped and separated into individual processes to protect camera acquisitions from errors in other processes. In addition, to enhance the speed of data streaming analysis, the analysis code has been optimized and converted into graphics processing unit (GPU) code. Besides the data streaming analysis, the system is also designed to support post-shot analysis with the entire frame data for the same shot to address frame drop issues encountered during data streaming. Radiation front control experiments with N2 gas seeding are successful results of the data streaming IRVB for its commissioning. This paper focuses on the development of the data streaming IRVB system.

An analysis of the hot hand phenomenon in basketball and mid-range shooting

Does the Hot Hand phenomenon exist during basketball, especially in the NBA? This question has been controversial over the past years in the sports field. The enormous literature on the Hot Hand effect in basketball was conducted to investigate the Hot Hand hypothesis. This study analyzes this question using a novel data set of all mid-range shots from the 2023-2024 NBA regular season, combined with data on teams and players. A model was built based on the definition of the Hot Hand and potential influential variables such as distance and location to analyze the players' shot data. Supervised machine learning methods fit the model and measure its performance. The results suggest that mid-range shooting streaks do not affect making the next shot in game situations, indicating that the Hot Hand effect is not present in mid-range shooting situations.

Calibration and characterization of optical and x-ray streak cameras using a Ti:Sapphire laser system.

Optical and x-ray streak cameras are used to study transient phenomena, particularly in the high-energy density physics regime. The Orion laser facility employs many different types of streak cameras, which are used to collect data on laser-plasma interactions as well as to verify the temporal profile and timing between the multiple Orion beamlines. Streak cameras are complex devices with very precise timing associated with them, which can often malfunction, resulting in the loss of shot data. Since Orion is a kJ-class Nd:glass laser system, it is not optimal to try and fault-find using Orion shots since this is both time consuming and prohibitively expensive. To enable the facile set-up, fault-finding, timing-in, and in-house calibration of Orion optical and x-ray diagnostics, a single laboratory system has been commissioned to provide an adjustable stimulus to streak cameras and other Orion short pulse diagnostics (such as pulse dilation photomultiplier tubes). The system comprises a Ti:Sapphire laser system capable of generating 400nm or 266nm laser pulses of duration less than 0.1ps; an optical system to deliver single pulses, pulse pairs, or a train of pulses; and timing electronics to synchronize the streak cameras with the laser and to record output data. The system can operate at Hertz repetition rates rather than the sub-mHz rate of the Orion laser. We present the commissioned system and results from initial testing of both optical and x-ray streak cameras used on Orion laser-plasma experiments.

Analisis Store Atmosphere dan Sales Promotion terhadap Impulse Buying yang Dimediasi oleh Shopping Emotion pada Toko Ritel Modern Miniso di Kota Bandung

This research aims to analyze the influence of store atmosphere and sales promotion on impulse buying mediated by shopping emotion at the Miniso modern retail store in Bandung City. Quantitative approach method using surveys based on deductive reasoning. The sample collection technique used was purposive sampling. Data was obtained from a survey distributed online using a Likert scale questionnaire with cross-sectional or shot study data collection techniques. The research results show that store atmosphere and sales promotion have a positive effect on impulse buying directly. However, store atmosphere and sales promotion have no effect on impulse buying through shopping emotion. In conclusion, although store atmosphere and sales promotion have a direct influence on impulse buying, they do not influence shopping emotion. This shows that other factors may be more dominant in mediating the relationship between store atmosphere and sales promotion and impulse buying. Keywords: Store Atmosphere, Shopping Emotions, Impulsive Buying, Sales Promotion

Relationships and Within-Group Differences in Physical Attributes and Golf Performance in Elite Amateur Female Players.

The aim of the present study was to examine the association between a comprehensive physical testing battery and measures of golf performance in elite female amateur players. Nineteen category one (handicap ≤ 5) or better golfers (age: 16.26 ± 1.28 years, height: 166.26 ± 3.62 cm, mass: 64.04 ± 11.27 kg, wingspan: 146.53 ± 15.59 cm, handicap: +1.45 ± 0.7) volunteered to participate in this investigation. All golfers attended a single 90 min testing session where golf shot data (clubhead speed [CHS], ball speed, carry distance, and smash factor) were measured with a Trackman 4 launch monitor and a battery of physical assessments were carried out. These included anthropometric data and assessments for seated thoracic rotation, the isometric mid-thigh pull (IMTP), isometric bench press, countermovement jump (CMJ), and seated medicine ball throws for distance. Pearson's r correlations showed CHS was the golf metric that most commonly demonstrated large associations with physical testing data, most notably with force at 100 ms during the isometric bench press (r = 0.70). Median split analysis was also conducted for the IMTP (force at 200 ms), isometric bench press (force at 100 ms), and CMJ (positive impulse). The results showed that players who produced more force at 200 ms during the IMTP exhibited a greater CHS (g = 1.13), ball speed (g = 0.90), and carry distance (g = 1.01). In addition, players with a greater positive impulse during the CMJ showed a greater ball speed (g = 0.93), carry distance (g = 1.29), and smash factor (g = 1.27). Collectively, these results highlight the relevance of explosive force production capabilities in both the lower and upper body for female golfers. This information can be used by practitioners to better target key physical attributes during testing and training of female players.

Identify Cricket Shots using Machine Learning

Cricket shot detection is a game-changing technology that offers deep insights into player performance and match data, completely changing the way the sport is played. The main elements and importance of cricket shot detection systems are explored in this abstract. Using computer vision and machine learning techniques, the system examines video footage of cricket matches to accurately detect and classify every shot made by batsmen. Shot types (such as cover drive, pull, or leg glance), shot trajectories, and success rates are among the important data it retrieves. Numerous stakeholders in the cricketing ecosystem find diverse uses for cricket shot detection. It provides coaches and professional athletes with an unmatched post-match analysis tool that helps with strategic planning and performance enhancement.The method is used by team analysts to create winning strategies by gaining insight into opponents' shot patterns. Shot detection provides compelling visualizations and real-time shot labels in the broadcasting domain, enhancing the viewing experience. While talent scouts and cricket organizations use technology to find players and nurture talent, cricket enthusiasts profit from comprehensive shot data. This abstract highlight cricket shot detection's potential and adaptability, highlighting how it can revolutionize the cricket industry. Technology keeps improving the game, empowering players, and enthralling spectators with a deeper comprehension of the sport. Several sports have received a lot of attention and popularity recently. Many people were pining for live sports action during the height of the recent outbreak because there were no sporting activities. With millions of devoted fans who watch the games with emotion, cricket is one of the most respected sports in India. Enticed by the game, fans frequently conduct in-depth evaluations of certain players, focusing on their skills and shot choices. A greater number of people are interested in assessing players' performances in order to make wise choices for fantasy teams, especially in light of the popularity of fantasy leagues and related services. Automation presents a potential solution to the significantly time-consuming and manual process of detecting cricket batters' shots. In order to accomplish its goals, this study uses deep learning in the form of Convolutional Neural Networks (CNNs) to present an efficient method for evaluating cricket strokes

Integration of Seismic and Well Log Data for Reservoir Characterization in Offshore Niger Delta, Nigeria

The demand for fossil fuels is continually increasing due to the growing global population and industrialization. It is thus necessary to find hydrocarbon prospects in new fields and those that have previously been classified as marginal fields in order to maximize production. An integrated approach is required to effectively characterize hydrocarbon reservoirs and assess their ability to store and produce hydrocarbons. 3D seismic reflection data and well log data were combined to quantitatively estimate hydrocarbon reserves in a field, offshore Niger delta, Nigeria. The reservoirs were delineated on the gamma ray and resistivity logs which penetrate four wells selected from the field. The petrophysical analysis provided information about the net-to-gross thickness ratio, porosity, shale volume and water saturation of the reservoirs, deduced from well log suites comprising gamma, resistivity, density and neutron-porosity logs through four wells. The seismic interpretation involved mapping of horizons and faults across the wells on the seismic section. Check shot data were used to tie the seismic data to the well log data to generate the synthetic seismogram. The time structural and depth structural maps were generated. The volumetric analysis entailed derivation of the Gross rock volume from the depth structural map, and estimation of the Stock Tank Oil Initially In Place (STOIIP). Four reservoirs were delineated in each of the four wells. The average thickness of the reservoirs ranges from 56.6 m to 232.7 m while the water saturation varies from 0.29% to 0.57%. The average porosities of the reservoirs ranges from 0.18% to 0.22%. The structural interpretation of the eight faults mapped reveals synthetic and antithetic faults, and rollover anticlines. The time- and depth structural maps generated from the mapped horizons show that the reservoirs are penetrated by the different faults. The Stock Tank Oil Initially In Place (STOIIP) of the reservoirs range from 25 MMstb to 468 MMstb. The study shows that the integration of 3D reflection data and well log data can be used effectively to estimate hydrocarbon reserves. The results of the study are expected to contribute to the development of new exploration and production strategies in the Niger delta basin and similar sedimentary environments.

Associations and Within-Group Differences in Physical Characteristics and Golf Performance Data in High-Level Amateur Players

The aim of the present study was to assess the relationship between a comprehensive physical testing battery and golf performance, as quantified through a variety of previously determined usable metrics from launch monitor data. Twenty-six high-level, amateur golfers undertook a series of physical assessments, including anthropometry measurements, isometric mid-thigh pull (IMTP), isometric bench press, countermovement jump (CMJ), seated medicine ball throws for distance, and seated thoracic rotation tests. In addition, golf shot data were recorded in an indoor biomechanics laboratory, with a driver and 6-iron to quantify clubhead speed (CHS), ball speed, carry distance, and smash factor. Pearson’s r associative analyses showed that the strongest relationships with the golf shot data were with the isometric bench press for the upper body (r values up to 0.76) and countermovement jump for the lower body (r values up to 0.82). In addition, the median split analysis of the physical performance data revealed that players who were able to exhibit greater maximal and explosive strength capabilities in the IMTP, isometric bench press, and CMJ assessments had a significantly greater CHS (g range = 1.09–1.28; p < 0.05), ball speed (g range = 1.18–1.41; p < 0.05), carry distance (g range = 1.06–1.53; p < 0.05), and smash factor (g range = 0.81–1.17; p < 0.05). These data underscore the importance of superior physical capacity for golfers, especially for maximal force production in both the lower and upper body, as well as explosive force production for the lower body.

An FCN-based transfer-learning method for spatial infrared moving-target recognition

Spatial infrared (IR) moving-target sample data are difficult to collect; thus, they cannot be used for mass-training, and limit development and application of deep learning methods in spatial IR target recognition. This study proposes a transfer-learning method based on fully convolutional network (FCN) for spatial IR moving-target recognition. First, we built a set of semi-physical scale models of spatial IR moving targets and obtained IR radiation characteristics as target-domain data using actual shot data and image inversion. Simulation data of the spatial target were obtained using the IR radiation intensity model, and used as the source-domain data together with the public UCR dataset. We used the dynamic time warping distance to measure the similarity between datasets to select appropriate source-domain datasets. We used an FCN framework as the main classification framework with a large number of source-domain samples for training. Knowledge transfer and model fine-tuning facilitate target-domain learning using only a few samples to improve classification accuracy. The experimental results show that the proposed model achieved an accuracy rate of 78%, which increases by the 20% improvement compared to direct training on the target domain. This study verifies the effectiveness of transfer learning between spatial targets and related domains and provides a reference for application of deep learning in spatial situational awareness.

VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching.

Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain a competitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations and video. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Through immersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) with flexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence.

Deriving Vp velocity and density properties of complex litho-structural units from the analysis of geophysical log data: A study from the Southern Apennines of Italy

This study is aimed to define a velocity and density database for the main litho-structural units of the Val d’Agri area of the Southern Apennines (Italy). Here we analyze fifteen digital borehole data comprising sonic log, check shot and density log data with dedicated graphic and statistical tools. We compare velocities acquired with two techniques: check-shots and sonic logging, discussing factors influencing them and their variations with a depth trend. In general, we observe that the comparison between the check shots and the sonic logs velocities gives consistent results. The detailed analysis of well logs and velocity distribution histograms, allow us to distinguish the following eight litho-structural units and their representative velocities and densities: Liguride tertiary flysches; Apennine Carbonate Platform, Lagonegro - Clastics, Lagonegro -Carbonates; Irpinia 1; Irpinia 2 and 3; Volturino foredeep turbidites; Apulian Carbonate Platform. The density values were calculated through the velocity density relationship using the Gardner equation, and subsequently compared with the measured density values. The comparison shows good correlations with the adopted Gardner coefficient of a = 0.30 and an exponent m = 0.25 for carbonatic succession, while a Gardner coefficient of a = 0.25 and an exponent m = 0.28 for clastic lithologies.The obtained results represent a key input for the future velocity and gravity modellings and for the time to-depth conversion of seismic reflection profiles, describing the vertical and lateral lithological heterogeneities between carbonate platforms, deep-sea basinal units and clastic/turbidite successions in the complex tectonic settings of the Southern Apennines.The approach applied in this work, based on a shared analytical and graphical analysis of borehole data, can be readily extended to other case studies worldwide, being the analyzed formations lithologically comparable to many other fold-and-thrust belt rocks.

Toward Unified and Quantitative Cinematic Shot Attribute Analysis

Cinematic Shot Attribute Analysis aims to analyze the intrinsic attributes of movie shots, such as movement and scale. In previous methods, specialized architectures were designed for each specific task and relied on the use of optical flow maps. In this paper, we consider shot attribute analysis as a unified task of motion–static weight allocation, and propose a motion–static dual-path architecture for recognizing various shot attributes. In this architecture, we design a new action cue generation module for adapting the end-to-end training process instead of a pre-trained optical flow network; and, to address the issue of limited samples in movie shot datasets, we design a fixed-size adjustment strategy to enable the network to directly utilize pre-trained vision transformer models while adapting to shot data inputs at arbitrary sample rates. In addition, we quantitatively analyze the sensitivity of different shot attributes to motion and static features for the first time. Subsequent experimental results on two datasets, MovieShots and AVE, demonstrate that our proposed method outperforms all previous approaches without increasing computational cost.

3D Check shot Modeling for Time-Depth Conversion

Time-depth conversion is one of the keys to successful drilling with a specific target. However, if the drilling target is incorrectly defined, it can cause significant material losses. For example, several cases found that the hydrocarbon depth was deeper than the expected target. As a result, the well will be considered dry even though it still has hydrocarbon reserves. In this study, we intend to develop 3D check shot modeling in which check shot and formation marker data are referred to and taken at the well to obtain a precise time-depth relationship to overcome this problem. The well data with check shot available that can be used for velocity modeling, for the time-depth conversion process is very limited. However, in development fields with dozens or even hundreds of wells, the time-to-depth conversion process becomes a challenge where it is not enough when using one single velocity. In addition, the velocity model must accommodate lateral heterogeneity so the depth structure map can match the well formation markers. On the other hand, seismic data can indirectly measure subsurface velocities vertically and horizontally. This research combines well-check shot data with seismic semblance velocity data to build a velocity model calibrated by well data. The time-to-depth conversion process with the calibrated velocity model has an average residual of less than 5 meters compared to only using check shot data, with an average residual is 25-meters.

Tensor tree decomposition as a rank‐reduction method for pre‐stack interpolation

AbstractTensors have been proposed to represent pre‐stack seismic data, particularly for seismic data denoising and reconstruction. They naturally permit describing multi‐dimensional seismic signals that depend on time (or frequency) and source and receiver coordinates. A tensor representation aims to preserve the information embedded in the multi‐linear array in a reduced space. Such a representation is part of many algorithms for seismic data reconstruction via tensor completion methodologies. We investigate and apply one particular tensor tree representation to seismic data reconstruction. The Tensor Tree decomposition methodology permits decomposing a high‐order tensor into third‐order tensors. The technique relies on the truncated singular value decomposition, which branches the tensors into low‐dimensional tensors. As a benefit, the tensor tree allows us to reorganize the tensor into a matrix that demands the least singular values to have an optimal low‐rank approximation. We have developed an algorithm that uses the tensor tree for data reconstruction in an iterative optimization scheme and directly compared it to the parallel matrix factorization. At first, we demonstrated the proposed methodology results with five‐dimensional synthetic shot data and then moved forward with five‐dimensional field data, where we analysed it both pre and post‐stack. The tensor tree performs well in reconstructing both synthetic and field data with high fidelity, at the same level as the well‐established parallel matrix factorization.

Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design

Passive-source exploration is a method of seismic exploration that has loose requirements on the conditions of the surface, is cheap, and does not require excitation by an active source. The ambient seismic signals collected from the field over an extended period of time can be used to generate virtual-shot seismic records similar to those obtained from the seismic exploration of an active source based on the relevant correlations, and this can in turn yield information on the underground structure through a series of conventional methods of processing seismic data. Three-dimensional (3D) processing can mitigate the influence of the azimuth of random noise to yield a more representative underground structure, but requires intensive computation. In this paper, we propose a 3D method to compute reflections of a passive source based on the geometry of seismic exploration. Assuming a high quality of imaging, we use information on the predesigned geometry to choose and correlate noisy synthetic data on the reflections by a seismic body to create virtual shot data, and subsequently capture images of the 3D data on passive reflection. The use of the predesigned geometry ensures that only the important and useful parts of the dataset are used for correlation and imaging, where this reduces the cost of computation. The proposed method can thus efficiently generate high-quality 3D synthetic data.

Building adjoint operators for least-squares migration using the acoustic wave equation

Seismic imaging can be solved as an inversion problem, which can be implemented as a least-squares migration (LSM). Compared with conventional migration algorithms, an LSM can produce imaging results with enhanced illumination and resolution. However, solving an inversion problem faces difficulties in convergence, stability, and computational efficiency. To address these issues, efforts have been spent on examining the key elements in an LSM, including the modeling operators, the migration operators, the inversion solvers, etc. Advanced modeling operators are developed to accurately simulate the seismic data. Innovative migration algorithms are implemented to precisely compute the subsurface image. Fast inversion solvers are used to improve computation efficiency. Although all these techniques are robust to improve the LSMs, they often are studied independently. It is not often considered whether these elements are properly combined when used in an LSM. We have constructed LSMs with adjoint modeling and migration operators, and we develop algorithms to prepare input shot data for these LSMs.

Tango Controls and data pipeline for petawatt laser experiments

Abstract The Centre for Advanced Laser Applications in Garching, Germany, is home to the ATLAS-3000 multi-petawatt laser, dedicated to research on laser particle acceleration and its applications. A control system based on Tango Controls is implemented for both the laser and four experimental areas. The device server approach features high modularity, which, in addition to the hardware control, enables a quick extension of the system and allows for automated data acquisition of the laser parameters and experimental data for each laser shot. In this paper we present an overview of our implementation of the control system, as well as our advances in terms of experimental operation, online supervision and data processing. We also give an outlook on advanced experimental supervision and online data evaluation – where the data can be processed in a pipeline – which is being developed on the basis of this infrastructure.

A method of fast reading massive shot data of seismic acquisition based on two-level index

A method of fast reading massive shot data of seismic acquisition based on two-level index