ترغب بنشر مسار تعليمي؟ اضغط هنا

Testing Geological Models with Terrestrial Antineutrino Flux Measurements

532   0   0.0 ( 0 )
 نشر من قبل Stephen Dye
 تاريخ النشر 2009
  مجال البحث فيزياء
والبحث باللغة English
 تأليف Steve Dye




اسأل ChatGPT حول البحث

Uranium and thorium are the main heat producing elements in the earth. Their quantities and distributions, which specify the flux of detectable antineutrinos generated by the beta decay of their daughter isotopes, remain unmeasured. Geological models of the continental crust and the mantle predict different quantities and distributions of uranium and thorium. Many of these differences are resolvable with precision measurements of the terrestrial antineutrino flux. This precision depends on both statistical and systematic uncertainties. An unavoidable background of antineutrinos from nuclear reactors typically dominates the systematic uncertainty. This report explores in detail the capability of various operating and proposed geo-neutrino detectors for testing geological models.



قيم البحث

اقرأ أيضاً

We have examined the impact of new Daya Bay, Double Chooz, and RENO measurements on global fits of reactor antineutrino flux data to a variety of hypotheses regarding the origin of the reactor antineutrino anomaly. In comparing RENO and Daya Bay meas urements of inverse beta decay (IBD) yield versus $^{239}$Pu fission fraction, we find differing levels of precision in measurements of time-integrated yield and yield slope, but similar central values, leading to modestly enhanced isotopic IBD yield measurements in a joint fit of the two datasets. In the absence of sterile neutrino oscillations, global fits to all measurements now provide 3{sigma} preference for incorrect modeling of specific fission isotopes over common mis-modeling of all beta-converted isotopes. If sterile neutrino oscillations are considered, global IBD yield fits provide no substantial preference between oscillation-including and oscillation-excluding hypotheses: hybrid models containing both sterile neutrino oscillations and incorrect $^{235}$U or $^{239}$Pu flux predictions are favored at only 1-2{sigma} with respect to models where $^{235}$U, $^{238}$U, and $^{239}$Pu are assumed to be incorrectly predicted.
It is known that neutron background is a big problem for low-background experiments in underground Laboratories. Our global net of en-detectors sensitive to thermal neutrons includes the detectors running both on the surface and at different depths u nderground. We present here results obtained with the en-detector of 0.75 m^2 which is running more than 3 years in underground room at a depth of 25 m of water equivalent in Skobeltsyn Institute of Nuclear Physics, Moscow. Spontaneous increases in thermal neutron flux up to a factor of 3 were observed in delayed anti-correlation with barometric pressure. The phenomenon can be explained by a radon barometric pumping effect resulting in similar effect in neutron flux produced in (alpha,n)-reactions by alpha-decays of radon and its daughters in surrounding rock
Current methods of monitoring subsurface CO$_2$, such as repeat seismic surveys, are episodic and require highly skilled personnel to acquire the data. Simulations based on simplified models have previously shown that muon radiography could be automa ted to continuously monitor CO$_2$ injection and migration, in addition to reducing the overall cost of monitoring. In this paper, we present a simulation of the monitoring of CO$_2$ plume evolution in a geological reservoir using muon radiography. The stratigraphy in the vicinity of a nominal test facility is modelled using geological data, and a numerical fluid flow model is used to describe the time evolution of the CO$_2$ plume. A planar detection region with a surface area of 1000 m$^2$ is considered, at a vertical depth of 776 m below the seabed. We find that one year of constant CO$_2$ injection leads to changes in the column density of $lesssim 1%$, and that the CO$_2$ plume is already resolvable with an exposure time of less than 50 days.
120 - Hao Deng , Yang Zheng , Jin Chen 2021
The three-dimensional (3D) geological models are the typical and key data source in the 3D mineral prospecitivity modeling. Identifying prospectivity-informative predictor variables from the 3D geological models is a challenging and tedious task. Mot ivated by the ability of convolutional neural networks (CNNs) to learn the intrinsic features, in this paper, we present a novel method that leverages CNNs to learn 3D mineral prospectivity from the 3D geological models. By exploiting the learning ability of CNNs, the presented method allows for disentangling complex correlation to the mineralization and thus opens a door to circumvent the tedious work for designing the predictor variables. Specifically, to explore the unstructured 3D geological models with the CNNs whose input should be structured, we develop a 2D CNN framework in which the geometry of geological boundary is compiled and reorganized into multi-channel images and fed into the CNN. This ensures an effective and efficient training of CNNs while allowing the prospective model to approximate the ore-forming process. The presented method is applied to a typical structure-controlled hydrothermal deposit, the Dayingezhuang gold deposit, eastern China, in which the presented method was compared with the prospectivity modeling methods using hand-designed predictor variables. The results demonstrate the presented method capacitates a performance boost of the 3D prospectivity modeling and empowers us to decrease work-load and prospecting risk in prediction of deep-seated orebodies.
The San Andreas fault (SAF) in the USA is one of the most investigated self-organizing systems in nature. In this paper, we studied some geophysical properties of the SAF system in order to analyze the behavior of earthquakes in the context of Tsalli ss $q$--Triplet. To that end, we considered 134,573 earthquake events in magnitude interval $2leq m<8$, taken from the Southern Earthquake Data Center (SCEDC, 1932 - 2012). The values obtained ($q$--Triplet$equiv$${$$q$$_{stat}$,$q$$_{sen}$,$q$$_{rel}$$}$) reveal that the $q_{stat}$--Gaussian behavior of the aforementioned data exhibit long-range temporal correlations. Moreover, $q_{sen}$ exhibits quasi-monofractal behavior with a Hurst exponent of 0.87.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا