Estimates Of Production Rates Research Articles

Growth parameters of the clupeids Limnothrissa miodon and Stolothrissa tanganicae in northern Lake Tanganyika (Bujumbura sub-basin)

The analysis of growth parameters in fish stocks, such as the asymptotic length (L∞) and the curvature parameter (K), is crucial for the estimation of production rates and total mortality rates in fisheries management. Here, we estimated the growth parameters of the clupeids Limnothrissa miodon and Stolothrissa tanganicae in the northern part of Lake Tanganyika (Bujumbura sub-basin). Both species are important targets of pelagic fisheries, but the available estimates are based on scarce size frequency data, and rarely on size as a function of age data. We provide new estimates of L∞ and K based on size as a function of age data, with age being inferred from otolith weights. Furthermore, we reanalyze several existing length frequency datasets using advanced statistical procedures to test if growth parameters estimated by length frequency analysis are consistent with those obtained from size as a function of age. We found that length frequency analysis consistently underestimates L∞ and overestimates K as compared to length-at-age data. We recommend that length-at-age data are collected at a much broader spatial and temporal scale than currently available to advance our understanding of the growth dynamics of these two sardine species. This will improve the assessment of stock productivity of Lake Tanganyika’s pelagic fisheries and the conservation of these economically important fish species.

Characterization of in situ cosmogenic 14CO production, retention and loss in firn and shallow ice at Summit, Greenland

Abstract. Measurements of carbon-14-containing carbon monoxide (14CO) in glacial ice are useful for studies of the past oxidative capacity of the atmosphere as well as for reconstructing the past cosmic ray flux. The 14CO abundance in glacial ice represents the combination of trapped atmospheric 14CO and in situ cosmogenic 14CO. The systematics of in situ cosmogenic 14CO production and retention in ice are not fully quantified, posing an obstacle to interpretation of ice core 14CO measurements. Here we provide the first comprehensive characterization of 14CO at an ice accumulation site (Summit, Greenland), including measurements in the ice grains of the firn matrix, firn air and bubbly ice below the firn zone. The results are interpreted with the aid of a firn gas transport model into which we implemented in situ cosmogenic 14C. We find that almost all (≈ 99.5 %) of in situ 14CO that is produced in the ice grains in firn is very rapidly (in <1 year) lost to the open porosity and from there mostly vented to the atmosphere. The timescale of this rapid loss is consistent with what is expected from gas diffusion through ice. The small fraction of in situ 14CO that initially stays in the ice grains continues to slowly leak out to the open porosity at a rate of ≈ 0.6 % yr−1. Below the firn zone we observe an increase in 14CO content with depth that is due to in situ 14CO production by deep-penetrating muons, confirming recent estimates of 14CO production rates in ice via the muon mechanisms and allowing for narrowing constraints on these production rates.

Production of Dark Sector Particles via Resonant Positron Annihilation on Atomic Electrons.

Resonant positron annihilation on atomic electrons provides a powerful method to search for light new particles coupled to e^{+}e^{-}. Reliable estimates of production rates require a detailed characterization of electron momentum distributions. We describe a general method that harnesses the target material Compton profile to properly include electron velocity effects in resonant annihilation cross sections. We additionally find that high-Z atoms can efficiently act as particle physics accelerators, providing a density of relativistic electrons that allows one to extend by several times the experimental mass reach.

Variations in the Solar Modulation Parameter Over the Last 9.5 Thousand Years and the Tilt of the Geomagnetic Dipole

Background: Calculations of the solar modulation parameter (Φ) over the past millennia typically use the relationship between the production rate of cosmogenic isotopes, the earth's dipole moment, and the magnitude of Φ. The cosmogenic isotopes 14C and 10Be are typically used in these studies. When studying solar modulation, the cyclic change in dipole tilt is usually not taken into account, which affects estimates of past solar activity. Methods: Tree rings are a reliable basis for obtaining a radiocarbon time scale (IntCal13). However, determining the concentration of 14C in tree rings is a difficult and controversial task. The time scale derived from the 10Be production rate simulation (GICC05) is less reliable. Nevertheless, there is a way to combine the accuracy of the radiocarbon time scale with the reliability of estimates of the 10Be production rate. This method is the synchronization of the radiocarbon and beryllium-10 series. We have selected the most relevant methods for calculating the solar modulation parameter Φ for the Holocene. When calculating Φ, 10Be data synchronized with 14C data were used. The latest data on the earth's dipole moment were considered. Empirical Mode Decomposition (EMD) was used in the analysis of Φ. Results: It has been shown that the first two decomposition modes are oscillating components with periods of 710 and 208 years, the amplitudes of which increase with time, reaching a maximum of 2500 BP. From contemplation, it follows that the 710-year oscillations are apparently caused by fluctuations in the tilt of the earth's dipole. After excluding the EMD component associated with the 710-year cyclicity, a corrected series was obtained for the solar modulation parameter, free from the influence of changes in the tilt of the magnetic dipole. Conclusion: The rate of formation of cosmogenic radionuclides depends on the intensity of penetration of Galactic Cosmic Rays (GCRs) into the earth's atmosphere. Before reaching earth, GCRs must cross the heliosphere, where they are exposed to solar modulation. Adequate consideration of solar modulation parameters is important for the correct interpretation of the rate of production of cosmogenic isotopes and solar activity.

Ectomycorrhizal necromass turnover is one‐third of biomass turnover in hemiboreal Pinus sylvestris forests

Societal Impact StatementEfficient mitigation of climate change requires predictive models of forest ecosystems as sinks for atmospheric carbon. Mycorrhizal fungi are drivers of soil carbon storage in boreal forests, yet they are typically excluded from ecosystem models, because of a lack of information about their growth and turnover. Closing this knowledge gap could help us better predict future responses to climate change and guide policy decisions for sustainable management of forest ecosystems. This study provides new estimates of the production and turnover of mycorrhizal mycelial biomass and necromass. This information can facilitate the integration of mycorrhizal fungi into new predictive models of boreal forest soils.Summary In boreal forests, turnover of biomass and necromass of ectomycorrhizal extraradical mycelia (ERM) are important for mediating long‐term carbon storage. However, ectomycorrhizal fungi are usually not considered in ecosystem models, because data for parameterization of ERM dynamics is lacking. Here, we estimated the production and turnover of ERM biomass and necromass across a hemiboreal Pinus sylvestris chronosequence aged 12 to 100 years. Biomass and necromass were quantified in sequentially harvested in‐growth bags, and incubated in the soil for 1–24 month, and Bayesian calibration of mathematical models was applied to arrive at parametric estimates of ERM production and turnover rates of biomass and necromass. Steady states were predicted to be nearly reached after 160 and 390 growing season days, respectively, for biomass and necromass. The related turnover rates varied with 95% credible intervals of 1.7–6.5 and 0.3–2.5 times yr−1, with mode values of 2.9 and 0.9 times yr−1, corresponding to mean residence times of 62 and 205 growing season days. Our results highlight that turnover of necromass is one‐third of biomass. This together with the variability in the estimates can be used to parameterize ecosystem models, to explicitly include ERM dynamics and its impact on mycorrhizal‐derived soil carbon accumulation in boreal forests.

Estimation of Heat Production Rate using Thermal Data During Exercise in Indoor Environments: A Study of Heat Storage Rate in Male Athletes.

The increasing preference for indoor exercise spaces highlights the relationship between indoor thermal environments and physiological responses, particularly concerning thermal comfort during physical activity. Determining the metabolic heat production rate during exercise is essential for optimizing the thermal comfort, well-being, and performance of individuals engaged in physical activities. This value can be determined during the activity using several methods, including direct calorimetry measurement, indirect calorimetry that uses analysis of respiratory gases, or approximations using collected data such as speed, body mass, and heart rate. The study aimed to calculate the metabolic heat production rate by infrared thermal evaluation (ITE) based on the body's thermal balance approach and compare it with the values determined by indirect calorimetry (IC). Fourteen participants volunteered for the study, using a cycling ergometer in a controlled climatic chamber. After the familiarization sessions, maximal O2 intake levels (VO2max) were determined through maximal graded exercise tests. Subsequently, constant work rate exercise tests were performed at 60% of VO2max for 20min. The metabolic heat production rates were calculated by IC and ITE for each athlete individually. Respiratory gases were used to determine IC, while body skin and core temperatures, along with physical environmental data, were applied to calculate ITE using the human body thermal balance approximation of ASHRAE. According to the results, heat storage rates were misleading among the body's heat transfer modes, particularly during the first 8min of the exercise. ITE showed a moderate level of correlation with IC (r: 0.03-0.86) with a higher level of dispersion relative to the mean (CV%: 12-84%). Therefore, a new equation (ITEnew) for the heat storage rates was proposed using the experimental data from this study. The results showed that ITEnew provided more precise estimations for the entire exercise period (p > 0.05). Correlations between ITEnew and IC values were consistently strong throughout the exercise period (r: 0.62-0.85). It can be suggested that ITEnew values can predict IC during the constant work rate steady-state exercise.

Estimation of nuclear properties in a deuteron–lithium target using JENDL/DEU-2020 for IFMIF and similar irradiation facilities

An accelerator-based neutron source powered by deuteron–Li (d–Li) reactions is among the most promising neutron sources for fusion materials irradiation facilities, such as IFMIF, IFMIF-DONES, and A-FNS. The yield estimation of neutrons and other particles is critical to the design of these facilities. Presently, the McDelicious code is employed for the aforementioned yield estimations, although it is not an open code. Recently, a deuteron-induced reaction library, JENDL/DEU-2020, was released by the Japan Atomic Energy Agency. Thus, employing benchmark tests, such as the Particle and Heavy Ion Transport code System (PHITS) + JENDL/DEU-2020, Monte Carlo N-Particle + JENDL/DEU-2020, and McDelicious, we confirm that PHITS (or Monte Carlo N-Particle (MCNP)) + JENDL/DEU-2020 effectively reproduces the experimental data and McDelicious results. We apply PHITS + JENDL/DEU-2020 to the yield estimations of the neutron, gamma, and charged particles in a Li target and to the yield estimation of the charged particles in the target backplate for A-FNS. The estimated production rates of tritium and 7Be during the continuous operation using a deuteron beam of 40 MeV and 125 mA are 1.4 × 1016 atoms/s (2.3 g/FPY) and 5.9 × 1014 atoms/s (0.21 g/FPY), respectively, and the estimated production rates of He and H in the target backplate for A-FNS are 3.2 × 102 and 1.8 × 103 appm/FPY, respectively.

Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

AbstractIn this study, a neural network model is developed for the prediction of oil flow rates in wells lifted by electrical submersible pumps (ESPs). Three attributes of the model in this work make this study unique. First, the knowledge on the computational cost of models has been presented, a rarity in most neural network models on this subject; second, the models have been explicitly presented, a feature uncommon in published ANN predictive modelling studies; and third, it includes a sensitivity analysis of input variables. The dataset utilized for the model development comprises 275 data points collected from ESP-lifted wells in the Middle East. Statistical evaluation of the model’s performance using the metrics such as mean square error, root mean square error and coefficient of determination demonstrates high predictive accuracy with respective values of 0.0000201861, 0.00449 and 0.999. In order to ascertain the parametric importance of the inputs, Garson’s algorithm was utilized. In this regard, choke size and upstream pressure had the highest influence (19% and 16%, respectively), while casing head pressure had the least effect (4.8%) on oil flow rate. In terms of memory requirements and processing speed for software applications, the model had a memory footprint of 888 bytes and required 191 multiply and accumulate operations to give an output. By utilizing the proposed models, the time-consuming separator tests measurements of flow rate would no longer be necessary and real-time results could be provided in the field. This work would be useful to production engineers who seek a quick and accurate means of estimating oil flow rate from ESP wells in real time.

CO2-Free Hydrogen Production by Methane Pyrolysis Utilizing a Portion of the Produced Hydrogen for Combustion

Air pollutants such as carbon dioxide and nitrogen oxides emitted by the combustion of fossil fuels have become the subject of increasing concern. Hydrogen has accordingly emerged as a promising low-emission alternative energy source. Among the various methods for hydrogen production, methane pyrolysis, which produces hydrogen without emitting carbon dioxide, has gained substantial attention. This study evaluated the self-sustainability of a new hydrogen production system based on methane pyrolysis, in which a portion of the hydrogen produced is used as combustion fuel rather than relying on catalysts and electrical heating. Coupled heat transfer and one-dimensional reaction simulations employing two plug-flow reactors of a counterflow double-pipe heat exchanger were conducted to investigate the feasibility and efficiency of the proposed system, as well as the influence of flow conditions on hydrogen production. The results confirmed system viability, informed the estimation of hydrogen production rates, and provided methane conversion rate data emphasizing the critical role of low-flow conditions and residence time in system efficiency. Additionally, the production of carbon constituted a significant aspect of system efficiency. These findings indicate that the proposed system can produce environmentally friendly hydrogen, contributing to its potential utilization as a sustainable energy source.

Towards Human–Robot Collaboration in Construction: Understanding Brickwork Production Rate Factors

This study explores the critical determinants impacting labor productivity in brickwork operations within the construction industry—a matter of academic and practical significance, particularly in the era of increasing human–robot collaboration. Through an extensive literature review on construction labor productivity, this study identifies factors affecting brickwork productivity. Data were collected from active construction sites during brick wall construction through on-site measurements and participatory observation, and the relative importance of these factors is determined using Principal Component Analysis (PCA)-factor analysis. The validity of the analysis is established through the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity, with a KMO value of 0.544 and significance at the 0.05 significance level. The analysis reveals four principal components explaining 75.96% of the total variance. Notably, this study identifies the Euclidean distances for the top factors: weather (0.980), number of helpers (0.965), mason competency (0.934), and number of masons (0.772). Additionally, correlation coefficients were observed: wall area had the highest correlation (0.998), followed by wall length (0.853) and height (0.776). Interestingly, high correlations did not necessarily translate to high factor importance. These identified factors can serve as a foundation for predictive modeling algorithms for estimating production rates and as a guideline for optimizing labor in construction planning and scheduling, particularly in the context of human–robot collaboration.

Validity of the NTNU Prediction Model for D&B Tunnelling

AbstractThe Norwegian University of Science and Technology (NTNU) has since the early 1970s published prediction models for estimated production rates, time consumption, and costs for rock works. Since the early development of the NTNU models the tunnelling industry has continuously improved and the models have been updated on several occasions. The NTNU models are presently being updated to better fit the modern tunnelling trends in Norway. The current prediction model for D&B blast design is in this research paper compared with new data from thirteen recent Norwegian tunnel projects. The paper demonstrates that the empirical methodology behind the prediction model is still highly relevant, but some further improvements and additional empirical relations are suggested to enable the model to better cope with the recent 16 years of development in technology, equipment, contractual-issues and blast design. The new datasets show that modern D&B tunnelling employ more drill holes and specific charging per blast round than before. The current NTNU model thus under-predicts the actual tunnelling performance in the blast design. Some suggestions are made for further improving the model, where the updated drill length, the contour requirements, and the tendency to including the longitudinal ditches into the main blast are suggested as the main reasons for the disparity in drill hole numbers. Yet, the current trend in specific charging seem to have doubled since the early 2000s and cannot be explained by the increase in drill holes alone. Further development ought to be included in the blastability index (SPR) so that geological conditions that are on the extreme side of poor blastability can be properly accounted for in the blast design.

Cosmogenic 10Be in pyroxene: laboratory progress, production rate systematics, and application of the 10Be–3He nuclide pair in the Antarctic Dry Valleys

Abstract. Here, we present cosmogenic-10Be and cosmogenic-3He data from Ferrar dolerite pyroxenes in surficial rock samples and a bedrock core from the McMurdo Dry Valleys, Antarctica, with the goal of refining the laboratory methods for extracting beryllium from pyroxene, further estimating the 10Be production rate in pyroxene and demonstrating the applicability of 10Be–3He in mafic rock. The ability to routinely measure cosmogenic 10Be in pyroxene will open new opportunities for quantifying exposure durations and Earth surface processes in mafic rocks. We describe scalable laboratory methods for isolating beryllium from pyroxene, which include a simple hydrofluoric acid leaching procedure for removing meteoric 10Be and the addition of a pH 8 precipitation step to reduce the cation load prior to ion exchange chromatography. 10Be measurements in pyroxene from the surface samples have apparent 3He exposure ages of 1–6 Myr. We estimate a spallation production rate for 10Be in pyroxene, referenced to 3He, of 3.6 ± 0.2 atoms g−1 yr−1. 10Be and 3He measurements in the bedrock core yield initial estimates for parameters associated with 10Be and 3He production by negative-muon capture (f10∗=0.00183 and f3∗fCfD=0.00337). Next, we demonstrate that the 10Be–3He pair in pyroxene can be used to simultaneously resolve erosion rates and exposure ages, finding that the measured cosmogenic-nuclide concentrations in our surface samples are best explained by 2–8 Myr of exposure at erosion rates of 0–35 cm Myr−1. Finally, given the low 10Be in our laboratory blanks (average of 5.7 × 103 atoms), the reported measurement precision, and our estimated production rate, it should be possible to measure 2 g samples with 10Be concentrations of 6 × 104 and 1.5 × 104 atoms g−1 with 5 % and 15 % uncertainty, respectively. With this level of precision, Last Glacial Maximum to Late Holocene surfaces can now be dated with 10Be in pyroxene. Application of 10Be in pyroxene, alone or in combination with 3He, will expand possibilities for investigating glacial histories and landscape change in mafic rock.

Phytoplankton pigment in situ measurements uncertainty evaluation: an HPLC interlaboratory comparison with a European-scale dataset

Phytoplankton pigment data play a crucial role in ecological studies, enabling the identification of algal groups and estimation of primary production rates. Accurate measurements of chlorophyll a (TChl a) and other marine pigments are essential for the development of bio-optical algorithms and the validation of satellite data products. High-performance liquid chromatography (HPLC) is the gold standard method for quantifying multiple pigments in a single water sample. This study aims to investigate the uncertainties associated with phytoplankton pigment quantification by comparing duplicate sample analyses conducted by two laboratories, the Joint Research Centre of the European Commission (J) and the DHI Group, Denmark (D). The analyses were performed using the same HPLC method. The dataset comprised 957 natural samples collected between 2012 and 2017 from various European seas, representing different trophic conditions with TChl a concentrations ranging from 0.083 to 27.35 mg/m3. The study compared the results of the two independent analyses for TChl a and primary phytoplankton pigments, including chlorophyll b, chlorophyll c, carotens, fucoxanthin, 19′-butanoyloxyfucoxanthin, diadinoxanthin, diatoxanthin, 19′-hexanoyloxyfucoxanthin, peridin, and zeaxanthin. The percent difference between the two analyses was calculated to assess the uncertainties associated with pigment quantification. The mean percent difference observed between the two independent analyses of TChl a was 10.8%. For the primary phytoplankton pigments, the associated mean percent difference was 16.9%. These results meet the requirements of 15% and 25% uncertainties, respectively, which are applicable for the validation of satellite data products. The comparative analysis between the two laboratories demonstrates that the uncertainties associated with phytoplankton pigment quantification are within acceptable ranges for the validation of satellite data products. Moreover, the study investigates the propagation of uncertainties in diagnostic pigment values to phytoplankton indexes, which are derived using pigment-based algorithms to characterize phytoplankton populations according to functional types.

Compositional modeling of gas-condensate viscosity using ensemble approach

In gas-condensate reservoirs, liquid dropout occurs by reducing the pressure below the dew point pressure in the area near the wellbore. Estimation of production rate in these reservoirs is important. This goal is possible if the amount of viscosity of the liquids released below the dew point is available. In this study, the most comprehensive database related to the viscosity of gas condensate, including 1370 laboratory data was used. Several intelligent techniques, including Ensemble methods, support vector regression (SVR), K-nearest neighbors (KNN), Radial basis function (RBF), and Multilayer Perceptron (MLP) optimized by Bayesian Regularization and Levenberg–Marquardt were applied for modeling. In models presented in the literature, one of the input parameters for the development of the models is solution gas oil ratio (Rs). Measuring Rs in wellhead requires special equipment and is somewhat difficult. Also, measuring this parameter in the laboratory requires spending time and money. According to the mentioned cases, in this research, unlike the research done in the literature, Rs parameter was not used to develop the models. The input parameters for the development of the models presented in this research were temperature, pressure and condensate composition. The data used includes a wide range of temperature and pressure, and the models presented in this research are the most accurate models to date for predicting the condensate viscosity. Using the mentioned intelligent approaches, precise compositional models were presented to predict the viscosity of gas/condensate at different temperatures and pressures for different gas components. Ensemble method with an average absolute percent relative error (AAPRE) of 4.83% was obtained as the most accurate model. Moreover, the AAPRE values for SVR, KNN, MLP-BR, MLP-LM, and RBF models developed in this study are 4.95%, 5.45%, 6.56%, 7.89%, and 10.9%, respectively. Then, the effect of input parameters on the viscosity of the condensate was determined by the relevancy factor using the results of the Ensemble methods. The most negative and positive effects of parameters on the gas condensate viscosity were related to the reservoir temperature and the mole fraction of C11, respectively. Finally, suspicious laboratory data were determined and reported using the leverage technique.

A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes

Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow rates. Several correlations have been suggested to model the multiphase flow of oil and gas via surface chokes. However, substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity, anisotropism, variance in reservoir fluid characteristics at diverse subsurface depths, which introduces complexity in production data. Therefore, the estimation of daily oil and gas production rates is still challenging for the petroleum industry. Recently, hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum domain. This paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke (viz. stacked generalization and voting architectures), followed by an assessment of the impact of input production control variables. Otherwise, machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North Sea. Feature engineering has been properly applied to select the most suitable contributing control variables for daily production rate forecasting. This study provides a chronological explanation of the data analytics required for the interpretation of production data. The test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting.

A data-driven approach to estimating post-discovery parameters of unexplored oilfields

Consider a typical situation where an investor is considering acquiring an unexplored oilfield. The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology, depth, depositional system, diagenetic overprint, structural compartmentalization, and trap type are available. In this situation, investors usually estimate production rates using a volumetric approach. A more accurate estimation of production rates can be obtained using analytical methods, which require additional data such as net pay, porosity, oil formation volume factor, permeability, viscosity, and pressure. We call these data post-discovery parameters because they are only available after discovery through exploration drilling. A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data. Using the Gaussian mixture model, and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established. We came up with 12 reservoir types. Subsequently, an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types. Based on k-fold cross-validation with k = 10, the accuracy of the classification model is stable with an average of 87.9%. With our approach, an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters' joint probability distribution. The investor can incorporate this information into a valuation model to calculate the production rates more accurately, estimate the oilfield's value and risk, and make an informed acquisition decision accordingly.

Carbon budget of Earth's deep mantle constrained by petrogenesis of silica-poor ocean island basalts

The convecting mantle is the Earth's largest carbon reservoir that controls the carbon outflux from the interior to surface, but its carbon content remains largely uncertain due to limited constraints on carbon inventory of the deep mantle. This knowledge gap can be filled by a global assessment of CO2 in ocean island basalts (OIBs), which are associated with upwelling mantle plumes from the deep mantle. However, this approach is hindered because of limited samples of undegassed, primary melts from intraplate magmatic settings and the debate on the origin of volatile-rich alkaline OIBs from plume versus lithospheric mantle. Here we examine the origin of silica-poor, alkaline OIBs and constrain CO2 in the primary melts of global OIBs by applying the newly developed liquid-based thermobarometer and primary melt correction scheme for silica-poor mantle-derived magmas. The averaged primary melt compositions of alkaline OIBs from individual island groups are more CO2-rich (∼3–11 wt% CO2) than those of subalkaline OIBs (∼0–7 wt% CO2), but both series from the same island groups yield final equilibration with the mantle at ∼3–5 GPa and mantle potential temperatures of ∼1430–1530°C. Our findings indicate that alkaline and subalkaline OIBs are generated from carbon-rich and carbon-poor domains, respectively, in the mantle plume sources, rather than deriving from two different sources in the plume and lithosphere mantle or variable extents of melting of a source with identical carbon content. Despite the source heterogeneity between alkaline and subalkaline OIBs, CO2 in the averaged primary melts of individual island groups displays strong positive correlations with Nb and Ba, which define robust CO2/Nb (1850 ± 196) and CO2/Ba ratios (226 ± 22) for the less-degassed deep, OIB source mantle. Combining these ratios with primitive mantle Nb and Ba contents, we estimate that the deep mantle on average has ∼330–400 ppm carbon. With an averaged CO2 content of ∼4 wt% in their primary melts, global OIBs are likely produced from the deep mantle source by ∼3% melting. The mean CO2 content in the primary melts of global OIBs, together with the previous estimate of plume magma production rate, yields a flux of ∼5.5 × 1012 mol/yr CO2 or ∼66 Mt/yr carbon through ocean island volcanism. If the OIB source mantle carbon budget is mostly primordial, our estimation provides a useful reference point for undifferentiated bulk silicate Earth to further examine carbon distribution and/or loss through Earth's history.

Magnetotelluric support for edge-driven convection and shear-driven upwelling in the Newer Volcanics Province

Intraplate volcanic provinces present significant natural hazards to many populated regions globally but their origins are poorly understood. Though hypotheses involving mantle plumes are predominant, the Newer Volcanics Province of southeast Australia—a relatively young (< 4.5 Ma), EW trending collection of over 400 volcanic centres—is increasingly attributed to some combination of edge-driven convection (EDC) and shear-driven upwelling (SDU). In this paper, we provide magnetotelluric (MT) data in support of these geodynamic processes. Three-dimensional inversion of 49 new broadband MT sites, in combination with 143 previously collected broadband, long-period, and geomagnetic depth soundings, reveals an elongate zone of moderately low resistivity (∼ 10–300 Ω m) spanning the Mt Gambier subprovince at a depth of between 20 and 40 km. The newly defined Gambier Conductor is contiguous to, and orientationally aligned with, significant step in the seismically-defined lithosphere-asthenosphere boundary (LAB) presented by earlier studies. Moderately low resistivity is interpreted as fluid-catalysed alteration of iron-bearing crust resulting from percolating magmatic volatiles. We argue that localised low resistivity (< 10 Ω m) at ~ 25 km depth in the mid-lower crust is associated with 1.2–3.6% partial melt. Supporting evidence indicates possible crustal thickening from 5.8 Ma at a rate comparable to estimates of SDU-induced surface eruptions and previous NVP production rate estimates.

The application of Monte Carlo modelling to quantify in situ hydrogen and associated element production in the deep subsurface

The subsurface production, accumulation, and cycling of hydrogen (H2), and cogenetic elements such as sulfate (SO42-) and the noble gases (e.g., 4He, 40Ar) remains a critical area of research in the 21st century. Understanding how these elements generate, migrate, and accumulate is essential in terms of developing hydrogen as an alternative low-carbon energy source and as a basis for helium exploration which is urgently needed to meet global demand of this gas used in medical, industrial, and research fields. Beyond this, understanding the subsurface cycles of these compounds is key for investigating chemosynthetically-driven habitability models with relevance to the subsurface biosphere and the search for life beyond Earth. The challenge is that to evaluate each of these critical element cycles requires quantification and accurate estimates of production rates. The natural variability and intersectional nature of the critical parameters controlling production for different settings (local estimates), and for the planet as a whole (global estimates) are complex. To address this, we propose for the first time a Monte Carlo based approach which is capable of simultaneously incorporating both random and normally distributed ranges for all input parameters. This approach is capable of combining these through deterministic calculations to determine both the most probable production rates for these elements for any given system as well as defining upper and lowermost production rates as a function of probability and the most critical variables. This approach, which is applied to the Kidd Creek Observatory to demonstrate its efficacy, represents the next-generation of models which are needed to effectively incorporate the variability inherent to natural systems and to accurately model H2, 4He, 40Ar, SO42- production on Earth and beyond.

Improving estimation of entropy production rate for run-and-tumble particle systems by high-order thermodynamic uncertainty relation.

Entropy production plays an important role in the regulation and stability of active matter systems, and its rate quantifies the nonequilibrium nature of these systems. However, entropy production is hard to experimentally estimate even in some simple active systems like molecular motors or bacteria, which may be modeled by the run-and-tumble particle (RTP), a representative model in the study of active matters. Here we resolve this problem for an asymmetric RTP in one dimension, first constructing a finite-time thermodynamic uncertainty relation (TUR) for a RTP, which works well in the short observation time regime for entropy production estimation. Nevertheless, when the activity dominates, i.e., the RTP is far from equilibrium, the lower bound for entropy production from TUR turns out to be trivial. We address this issue by introducing a recently proposed high-order thermodynamic uncertainty relation (HTUR), in which the cumulant generating function of current serves as a key ingredient. To exploit the HTUR, we adopt a method to analytically obtain the cumulant generating function of the current we study, with no need to explicitly know the time-dependent probability distribution. The HTUR is demonstrated to be able to estimate the steady state energy dissipation rate accurately because the cumulant generating function covers higher-order statistics of the current, including rare and large fluctuations besides its variance. Compared to the conventional TUR, the HTUR could give significantly improved estimation of energy dissipation, which can work well even in the far from equilibrium regime. We also provide a strategy based on the improved bound to estimate the entropy production from a moderate amount of trajectory data for experimental feasibility.