Ray Data Research Articles

Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

Abstract Formation resistivity is crucial for petrophysics and formation evaluation. Laboratory measurements and/or well logging can be used to provide resistivity data. Laboratory measurements are time-consuming and costly, limiting their use. Furthermore, certain log records may be missing in some segments for a variety of reasons, including instrument failure, poor hole conditions, and data loss due to storage and incomplete recording. The purpose of this study is to apply support vector machines (SVM), and functional networks (FN) to introduce intelligent models for formation resistivity prediction using other available logging parameters. The well logs include gamma ray, density, neutron, and sonic data. The predictive models were built using a data collection of roughly 4300 data points collected from vertical sections of complex reservoirs. For model training and testing, the data set was split at random in a 70:30 ratio. The predictive models were validated using a different set of data (around 1300 points) that had not been seen by the model. The models predicted the target with a good correlation coefficient (R) of around 0.93 and accepted root-mean-squared error (RMSE) of 0.3 for training and testing. The suggested methods for estimating formation resistivity from available logging parameters are shown to be reliable in this study. Resistivity prediction can fill the missing gaps in log tracks and may save money by removing resistivity logs running in all offset wells in the same field.

Testing the simultaneity of Forbush decreases with algorithm-selected Forbush event catalogue

Accurate detection and precise timing of transient events such as X-ray photons, $$\gamma $$ -ray burst, coronal mass ejections (CMEs), ground-level enhancements (GLEs) and Forbush decreases (FDs) frequently raise issues that remain on the cutting edge of research in astrophysics. In an attempt to automate FD event selection, a combination of Fast Fourier transform as well as FD detection algorithms implemented in the statistical computing software R was developed and recently used to calculate the magnitude and FD event timing. The R-FD code implemented in the present study includes several different calculations. Some subroutines detect both small and large transient intensity reductions (minima/pits) as well as increases (maxima/peaks) in cosmic ray (CR) data. Others calculate event amplitude, timing and cataloguing of the events identified. As the current work focuses on reductions in CR flux (FDs), the subroutine that identifies increases was disabled. Totals of 229 FDs at Magadan neutron monitor (NM), 230 (Oulu NM) and 224 (Inuvick NM) were identified with daily averaged data, while 4032 (Magadan), 4144 (Oulu) and 4055 (Inuvick) were detected with hourly averages. FDs identified as simultaneous at the three stations totaled 99 for the daily and 261 for the hourly CR averages respectively.

Quantitative Evaluation of Light Collimating for Commercial UV-LEDs Based on Analytic Collimating Lens

This paper proposes a lens design method for effectively collimating the light emitting from a light-emitting diode (LED). This collimating lens contains two aspherical lens surfaces which can be mathematically characterized using a few designing parameters, and hence is called an analytic collimating lens. An optical ray-tracing algorithm has been developed for these analytic collimating lenses to analyze their optical performance and to optimize their designs. Six high-power and commercially available ultraviolet (UV) LEDs are chosen as examples for demonstrating the optimal collimating lens design. For each UV-LED, the corresponding optical collimating lens is determined by inputting the ray data file provided by the manufacture over a finite-size emitting area. The divergent angles of the six UV-LEDs have been successfully collimated to a narrow range in between 1.56° to 2.84° from their original radiation angle around 46° to 120°. Furthermore, the proposed analytical collimating lenses are suitable for mass-production using standard mold injection methods, and hence possess great potentials for industry applications of LEDs.

New study of the neutron rich 136Te isotope through decay spectroscopy

136Sb is a neutron rich nucleus with one valence proton and three valence neutrons outside the doubly-magic nucleus 132Sn. It plays an important role in the rapid neutron capture process (r-process) as participates in its path further connected to the nucleosynthesis and the type II supernovae. In this work, a new β decay γ ray data on 136Te is obtained. The experiment is performed at the Institute Laue-Langevin using the Lohengrin spectrometer and the thermal neutron-induced fission reaction on $_{92}^{235}{{\rm{U}}_{143}}$. A specific β-β condition method is investigated for the γ-ray detection.

Подавление шума и выделение когерентных структур сигнала на основе алгоритма нелинейной адаптации

An algorithm for noise suppression and extraction of coherent structures of a complex signal is proposed. The algorithm is based on the joint application of packet wavelet decompositions and adaptive stochastic thresholds. Thresholds are estimated with a given confidence level based on the α-quantiles of Student's distribution. The operations of the algorithm are described and a scheme for its implementation is proposed. On the example of data from neutron monitors (recording the intensity of secondary cosmic rays, www.nmdb.eu), the efficiency of the algorithm is shown. The selected coherent structures characterize the occurrence of anomalies in variations in the intensity of cosmic rays. Anomalies in cosmic ray data create a radiation hazard, disrupt radio communications, and cause satellites to malfunction, leading to disorientation and destruction. Therefore, the task of timely diagnosis of such events is relevant. The application of the algorithm made it possible to clearly detect and evaluate the moments of occurrence of anomalies in cosmic rays, which were observed during periods of magnetospheric disturbances.

Prediction of Gamma Ray data from pre-stack seismic reflection partial angle stacks using Continuous Wavelet Transform and convolutional neural network approach

Seismic inversion is one of the critical issues for geophysicists in the oil and gas industry. It is commonly used to estimate the distribution of facies-types and fluids in reservoirs across the value chain from exploration to development. However, the low vertical and spatial resolution of seismic data leads to large uncertainties in these estimations, which makes direct use of inversion results challenging. The objective of this study was the estimation of a 3D volume of Gamma Ray data from pre-stack seismic reflectivity partial angle stacks based on the Convolutional Neural Network architecture by representing data through the Continuous Wavelet Transform. The proposed methodology used 132 deviated wells from the Azeri and Chirag parts of the Azeri – Chirag – Gunashli field in the South Caspian Basin as training data. Blind tests show promising Gamma Ray predictions. To optimize the predictions, sensitivity analysis was performed on the Continuous Wavelet Transform parameters (wavelet types, range of pseudo-frequencies, etc.), the Convolutional Neural Network architecture (number of convolutional and pooling layers, dropout, activation functions) and the network parameters (batch size, number of epochs, optimizers, etc.). Due to the vast amount of data required to speed up the learning process, simulations were performed on a Graphics Processing Unit based on the Compute Unified Device Architecture platform.

Do the Geminga, Monogem and PSR J0622+3749 γ -ray halos imply slow diffusion around pulsars?

The HAWC Collaboration has reported the detection of an extended $\gamma$-ray emission around the Geminga and Monogem pulsars of a few degree extension. Very recently, the LHAASO Collaboration released also the data for an extended $\gamma$-ray emission around the pulsar PSR J0622+3749. This flux can be explained with electrons and positrons injected from these sources and their inverse Compton Scattering on the interstellar radiation fields. So far the size of such $\gamma-$ray halos has been interpreted as the result of the diffusion coefficient around the sources being about two orders of magnitude smaller than the average in the Galaxy. However, this conclusion is driven by the assumption that particles propagate diffusively right away after the injection without taking into account the ballistic propagation. The propagation of cosmic-ray leptons in the proximity of the Geminga, Monogem and PSR J0622+3749 pulsars is examined here considering the transition from the quasi-ballistic, valid for the most recently injected particles, to the diffusive transport regime. For typical interstellar values of the diffusion coefficient, the quasi-ballistic regime dominates the lepton distribution up to distances of a few tens of parsec from the pulsar for particle energies above $\sim 10$ TeV. In this regime the resulting $\gamma-$ray source tends to be rather compact, despite particles travel a long distance. Indeed, for larger values of the diffusion coefficient, particles propagate ballistically up to larger distances with the result of a more point-like $\gamma-$ray source. When such transition is taken into account, a good fit to the HAWC and LHAASO $\gamma-$ray data around Geminga, Monogem and PSR J0622+3749 is obtained without the need to invoke a strong suppression of the diffusion coefficient.

Dynamic ecophenotypy in the Silurian Monograptidae (Graptolithina)

ABSTRACTThe monograptids from the Wenlock and Ludlow (mid- to late Silurian) of the palaeotropical Baltic Basin exhibit thickened ring structures (sicular annuli) over their initial phase of growth. Appearing before the lundgreni extinction event, they persisted throughout the remainder of the Silurian, fluctuating in number over that period. To better understand the mechanisms controlling their development and variation, counts of sicular annuli were taken from three well cores in Lithuania, compared between species in each sample and compared with contemporaneous gamma ray data, accompanied by the stable isotope (δ13C), and acritarch diversity data. Mean counts of annuli fluctuated greatly over the studied interval, but showed negligible variation between species, indicating that the trait is ecophenotypic. The fluctuation in annulus presence aligned with variations in fourth- and fifth-order cycles derived from the gamma ray trends, which represent significant sea level fluctuations, δ13C ratios, and primary productivity, suggesting that annuli were more plentiful in high-stand states which are associated with the wetter climate and more productive conditions, whereas dryer, less productive conditions were not conducive to annulus development. In light of this evidence, we hypothesise that the action of upwelling as a result of intensified storm events during wetter periods would have encouraged phytoplankton blooms, increasing construction of annuli. These results show the potential utility of sicular annuli in the study of Silurian climate change and give new insights into graptolite palaeoecology.

Восстановление векторного поля по данным его продольного и поперечного лучевых преобразований

An algorithm for the complete reconstruction of a vector field in three-dimensional Euclidean space based on incomplete integral data of a longitudinal and a transverse ray transforms is constructed. From of data of a longitudinal ray transform given on the family of straight lines intersecting a piecewise smooth curve, the solenoidal part of the vector field is constructed, and from of data of a transverse ray transform, the potential of an unknown field is constructed. The problem of reconstruction is also solved in the case of a family of lines intersecting a curve at infinity.

Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs

Derivative and volatility attributes can be usefully calculated from recorded gamma ray (GR) data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such attributes extract information about the log curve shape that cannot be readily discerned from the recorded well log data. A logged wellbore section for which 8911 data records are available for the three recorded logs (GR, sonic (DT) and bulk density (PB)) is evaluated. That section demonstrates the value of the GR attributes for machine learning (ML) lithofacies predictions. Five feature selection configurations are considered. The 9-var configuration including GR, DT, PB and six GR attributes, and the 7-var configuration of GR and the six GR attributes, provide the most accurate and reproducible lithofacies predictions. The other three feature configurations evaluated do not include the GR attributes but just one to three of the recorded log features. The results of seven ML models and two regression models reveal that K-nearest neighbor (KNN), random forest (RF) and extreme gradient boosting (XGB) are the best performing models. They generate between 14 and 23 misclassification from 8911 data records for the 9-var model. Multi-layer perceptron (MLP) and support vector classification (SVC) do not perform well with the 7-var model which lacks the PB feature displaying the highest correlation with facies class. Annotated confusion matrices reveal that KNN, RF and XGB models can effectively distinguish all facies classes for the 9-var and 7-var configurations (that includes the GR attributes), whereas none of the models can achieve that outcome for the 3-var configuration (that excludes the GR attributes). Accurately distinguishing lithofacies using well-log data in sedimentary sections is an important objective in applied geoscience. The straightforward, GR-attribute method proposed works to improve confidence in ML-lithofacies classifications based on limited recorded well-log data.

Purity monitoring for ProtoDUNE-SP

The Deep Underground Neutrino Experiment is a next-generation long-baseline neutrino oscillation experiment based on liquid argon time projection chamber technology. DUNE-s single-phase prototype ProtoDUNE-SP at CERN finished its two-year Phase-1 running in July 2021, successfully collected test-beam and cosmic ray data. A key aspect of LArTPC calibration is the lifetime of drift electrons. A purity monitor is a miniature TPC measuring the lifetime of electrons generated from the photocathode via the photoelectric effect. It enables continuous monitoring of the detector status, especially when filling the cryostat and when liquid argon recirculation systems operate. The purity monitoring system in ProtoDUNE-SP Phase-1 monitored liquid argon purity throughout its entire lifetime. It is essential to the experiment’s successful commissioning, operation, and data taking. This poster discusses the design, implementation, and results of purity monitors and plans.

A Framework for Data-Driven Solutions with COVID-19 Illustrations

Data–driven solutions have long been keenly sought after as tools for driving the world’s fast changing business environment, with business leaders seeking to enhance decision making processes within their organisations. In the current era of Big Data, applications of data tools in addressing global, regional and national challenges have steadily grown in almost all fields across the globe. However, working in silos has continued to impede research progress, creating knowledge gaps and challenges across geographical borders, legislations, sectors and fields. There are many examples of the challenges the world faces in tackling global issues, including the complex interactions of the 17 Sustainable Development Goals (SDG) and the spatio–temporal variations of the impact of the on-going COVID–19 pandemic. Both challenges can be seen as non–orthogonal, strongly correlated and requiring an interdisciplinary approach to address. We present a generic framework for filling such gaps, based on two data-driven algorithms that combine data, machine learning and interdisciplinarity to bridge societal knowledge gaps. The novelty of the algorithms derives from their robust built–in mechanics for handling data randomness. Animation applications on structured COVID–19 related data obtained from the European Centre for Disease Prevention and Control (ECDC) and the UK Office of National Statistics exhibit great potentials for decision-support systems. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from GitHub and Kaggle. Our results exhibit consistent performance across samples, resonating with cross-disciplinary discussions on novel paths for data-driven interdisciplinary research.

Consistent explanation for the cosmic-ray positron excess in p-wave Sommerfeld-enhanced dark matter annihilation

Sommerfeld-enhanced dark matter (DM) annihilation through s-wave has been widely considered as a consistent explanation for both the observed cosmic-ray (CR) positron excess and the DM thermal relic density. However, as the s-wave Sommerfeld-enhanced annihilation cross section increases monotonically with decreasing DM velocity, severe constraints appear from the data of gamma rays from dwarf spheroidal satellite galaxies (dSphs) and the cosmic microwave background (CMB), as the relevant typical DM velocities are even lower than that in the Galactic halo. In this work, we consider Sommerfeld-enhanced p-wave DM annihilation wherethe DM annihilation cross section can be enhanced at certain velocities but eventually be highly suppressed when the DM velocities become extremely low. We calculate the velocity-dependent astrophysics factors (J-factors) for the Sommerfeld-enhanced p-wave DM annihilation for fifteen nearby dSphs. Taking the channel of DM annihilating into 4μ through two light mediators as an example, we show that there are parameter regions where this mechanism can account for the CR positron excess and the DM relic density, while being compatible with the constraints from dSphs gamma rays measured by Fermi-LAT and that from the CMB measured by PLANCK.

Diagenesis of paleodrainages in Lake Way and Lake Maitland, Western Australia, and the role of authigenic Mg-clays and dolomite in the genesis of channel and playa uranium deposits

ABSTRACT This study integrates mineralogical and hydrogeochemical analysis of channel and playa uranium deposits to characterize aquifer evolution and the physico-chemical mechanisms that result in the accumulation of uranium into potentially economic deposits. This subset of surficial U deposits occur in Tertiary to Recent calcrete and dolomitic, clay-rich fluvial paleochannel and palustrine sediments, wherein uranium is largely bound in the potassium-uranyl-vanadate mineral carnotite [K2(UO2)2(VO4)2·3H2O]. Scanning electron microanalysis indicates that the carnotite mineralization is part of a late-diagenetic mineral assemblage that critically includes Mg-clays (sepiolite and stevensite), amorphous magnesium silicate, and synsedimentary dolomite. This authigenic mineral assemblage is observed concentrated in fractures and pores in groundwater calcrete and silty salt marsh “palustrine” sediments. Drill-hole gamma ray and conductivity data from the Centipede-Millipede uranium deposit indicate that the locus of uranium mineralization occurs near the present-day water table where oxidizing fresh-to-brackish groundwater interacts with playa brine, forming a hypopycnal groundwater estuary beneath the clay pan and salt marsh. It is interpreted that effective U fixing occurs in areas where groundwater, near-saturated with respect to carnotite, is hydrologically focused upward and into the zone of evaporation. The appreciable precipitation deficit in the Northern Yilgarn is interpreted to produce an evaporation-driven positive feedback mechanism that results in the co-precipitation of Mg-clays, dolomite, and carnotite. The presence of vanadium-rich Mn-oxide phases in high-grade U ore zones indicates that Mn-redox cycling may serve an important role in increasing the local activity of V, and thus carnotite saturation. Mineralogical comparison of other channel and playa uranium deposits throughout Western Australia and Namibia have identified a similar mineral association and paragenetic trend, suggesting that contemporaneous evaporative precipitation of Mg-clays and dolomite are integral in achieving carnotite saturation and precipitation.

Unitarisation dependence of diffractive scattering in light of high-energy collider data

We study the consequences of high-energy collider data on the best fits to total, elastic, inelastic, and single-diffractive cross sections for pp and poverline{p} scattering using different unitarisation schemes. We find that the data are well fitted both by eikonal and U-matrix schemes, but that diffractive data prefer the U-matrix. Both schemes may be generalised by means of an additional parameter; however, this yields only marginal improvements to the fits. We provide estimates for ρ, the ratio of the real part to the imaginary part of the elastic amplitude, for the different fits. We comment on the effect of the different schemes on present and future cosmic ray data.

Studying Cosmic Ray Sources Using Intergalactic Electromagnetic Cascades

In this paper, intergalactic electromagnetic cascades are used as a probe of cosmic ray sources. This is achieved as follows. In extragalactic space, cosmic rays initiate electromagnetic cascades, in which gamma-ray and neutrino emission arises. We used the joint analysis of cosmic ray data, along with extragalactic gamma-ray and neutrino emissions, to study particle acceleration in the vicinity of supermassive black holes. Particle injection spectra depend on processes of particle acceleration, and here we discuss models with various injection spectra. The computations of the propagation of cosmic rays in space were performed using the publicly available TransportCR code. It was found that a new subclass of sources might exist that does not contribute to the particle flux on Earth, instead to gamma-ray and neutrino emissions arising in electromagnetic cascades. In addition, the upper limit of the relative number of ‘exotic’ supermassive black holes surrounded by a superstrong magnetic field is derived.

Performance study of a 3×1×1 m3 dual phase liquid Argon Time Projection Chamber exposed to cosmic rays

We report the results of the analyses of the cosmic ray data collected with a 4 tonne (3×1×1 m3) active mass (volume) Liquid Argon Time-Projection Chamber (TPC) operated in a dual-phase mode. We present a detailed study of the TPC's response, its main detector parameters and performance. The results are important for the understanding and further developments of the dual-phase technology, thanks to the verification of key aspects, such as the extraction of electrons from liquid to gas and their amplification through the entire one square metre readout plain, gain stability, purity and charge sharing between readout views.

Investigating the Influence of Cosmic Rays on Ozone Layer Depletion at Beijing, China

In the present study monthly and annually data of ozone depth and cosmic rays (CRs) intensity at China Beijing during the period of 1984-2010 have been analyzed to investigate the effects of cosmic rays intensity on ozone layer depletion (OLD). This communication implements statistical analysis on the data sets for the specified period mentioned above. The analyses exhibit that mean monthly variation of cosmic rays intensity increases. Our investigation claims that in the month of May (1984-2010) changes occurred in the decrease the ozone depth due to the increase in cosmic rays intensity .This sort of study confirms cosmic rays influence on the ozone layer depletion.

Automated Method for Cosmic Ray Data Analysis and Detection of Sporadic Effects

Automated Method for Cosmic Ray Data Analysis and Detection of Sporadic Effects

The SπRIT time projection chamber.

The Superconducting Analyzer for MUlti-particles from RAdioIsotope (SAMURAI) Pion-Reconstruction and Ion-Tracker Time Projection Chamber (SπRIT TPC) was designed to enable measurements of heavy ion collisions with the SAMURAI spectrometer at the RIKEN radioactive isotope beam factory and provides constraints on the equation of state of neutron-rich nuclear matter. The SπRIT TPC has a 50.5cm drift length and an 86.4 × 134.4cm2 pad plane with 12 096 pads that are equipped with the generic electronics for TPCs. The SπRIT TPC allows for an excellent reconstruction of particles and provides isotopic resolution for pions and other light charged particles across a wide range of energy losses and momenta. The details of the SπRIT TPC are presented, along with discussion of the TPC performance based on cosmic rays and charged particles emitted in heavy ion collisions.