An improved approach to age-modeling in deep time: Implications for the Santa Cruz Formation, Argentina

2019 ◽  
Vol 132 (1-2) ◽  
pp. 233-244 ◽  
Author(s):  
Robin B. Trayler ◽  
Mark D. Schmitz ◽  
José I. Cuitiño ◽  
Matthew J. Kohn ◽  
M. Susana Bargo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Isotope Composition ◽  
Santa Cruz ◽  
Model Uncertainties ◽  
Weighted Mean ◽  
Simple Method ◽  
Deep Time ◽  
Likelihood Functions ◽  
Proxy Records ◽  
Crystal Residence

AbstractAccurate age-depth models for proxy records are crucial for inferring changes to the environment through space and time, yet traditional methods of constructing these models assume unrealistically small age uncertainties and do not account for many geologic complexities. Here we modify an existing Bayesian age-depth model to foster its application for deep time U-Pb and 40Ar/39Ar geochronology. More flexible input likelihood functions and use of an adaptive proposal algorithm in the Markov Chain Monte Carlo engine better account for the age variability often observed in magmatic crystal populations, whose dispersion can reflect inheritance, crystal residence times and daughter isotope loss. We illustrate this approach by calculating an age-depth model with a contiguous and realistic uncertainty envelope for the Miocene Santa Cruz Formation (early Miocene; Burdigalian), Argentina. The model is calibrated using new, high-precision isotope dilution U-Pb zircon ages for stratigraphically located interbedded tuffs, whose weighted mean ages range from ca. 16.78 ± 0.03 Ma to 17.62 ± 0.03 Ma. We document how the Bayesian age-depth model objectively reallocates probability across the posterior ages of dated horizons, and thus produces better estimates of relative ages among strata and variations in sedimentation rate. We also present a simple method to propagate age-depth model uncertainties onto stratigraphic proxy data using a Monte Carlo technique. This approach allows us to estimate robust uncertainties on isotope composition through time, important for comparisons of terrestrial systems to other proxy records.

Early Eocene simulations with three different palaeogeographic conditions

2021 ◽  
Author(s):  
Zijian Zhang ◽  
Zhongshi Zhang ◽  
Zhengtang Guo
Keyword(s):  
Early Eocene ◽  
Model Intercomparison ◽  
Warm Climate ◽  
Time Model ◽  
Deep Time ◽  
Climate Proxy ◽  
Proxy Records ◽  
Intercomparison Project ◽  
Series Of Experiments ◽  
Very High

<p>The early Eocene is a warm period with a very high atmosphere CO<sub>2</sub> level in the Cenozoic. It  provides a good reference for our future climate under the Representative Concentration Pathway 8.5 scenario. Therefore, the early Eocene climate has received many attentions in  modeling studies, for example, the Deep-Time Model Intercomparison Project (DeepMIP). However, the early Eocene palaeogeographic conditions show remarkable contrasts to the present conditions. Meanwhile, there are a few different reconstructions for the early Eocene palaeogeography, which may cause further model spreads in simulating the early Eocene warm climate. Here, we present a series of experiments carried out with the NorESM1-F, under the framework of DeepMIP. In these experiments, we consider three different palaeogeographic reconstructions for the early Eocene. We also compare our simulations with climate proxy records, to validate which palaeogeographic reconstructions can reproduce simulations that agree better with the climate proxy records.</p>

scholarly journals Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 258
Author(s):  
Zhihang Xu ◽  
Qifeng Liao
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Information Gain ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Likelihood Functions ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Bayesian Monte Carlo ◽  
Expected Information Gain

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.

Clumped Isotope Paleothermometry: Principles, Applications, and Challenges

2012 ◽  
Vol 18 ◽  
pp. 101-114 ◽  
Author(s):  
Hagit P. Affek
Keyword(s):  
Isotope Composition ◽  
Isotope Analysis ◽  
Atomic Scale ◽  
Clumped Isotopes ◽  
Temperature Dependent ◽  
Deep Time ◽  
Diagenetic Alteration ◽  
Carbonate Formation ◽  
Rare Isotopes ◽  
Clumped Isotope

Clumped isotopes geochemistry measures the thermodynamic preference of two heavy, rare, isotopes to bind with each other. This preference is temperature dependent, and is more pronounced at low temperatures. Carbonate clumped isotope values are independent of the carbonate δ13C and δ18O, making them independent of the carbon or oxygen composition of the solution from which the carbonate precipitated. At equilibrium, it is therefore a direct proxy for the temperature in which the carbonate mineral formed. In most cases, carbonate clumped isotopes record the temperature of carbonate formation, irrespective of the mineral form (calcite, aragonite, or bioapatite) or the organism making it. The carbonate formation temperatures obtained from carbonate clumped isotope analysis can be used in conjunction with the δ18O of the same carbonate, to constrain the oxygen isotope composition of the water from which the carbonate has precipitated. There are, however, cases of deviation from thermodynamic equilibrium, where both clumped and oxygen isotopes are offset from the expected values. Such carbonates must be characterized and calibrated separately. For deep-time applications, special care must be paid to the preservation of the original signal, in particular with respect to diagenetic alteration associated with atomic scale diffusion that may be undetectable by common tests for diagenesis.

scholarly journals Typical representatives of free homotopy classes in multi-punctured plane

2019 ◽  
Vol 11 (03) ◽  
pp. 623-659
Author(s):  
Maxim Arnold ◽  
Yuliy Baryshnikov ◽  
Yuriy Mileyko
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Probability Measure ◽  
Homotopy Class ◽  
Piecewise Linear ◽  
Homotopy Classes ◽  
Simple Method ◽  
Monte Carlo Techniques ◽  
Free Homotopy Class

We show that a uniform probability measure supported on a specific set of piecewise linear loops in a nontrivial free homotopy class in a multi-punctured plane is overwhelmingly concentrated around loops of minimal lengths. Our approach is based on extending Mogulskii’s theorem to closed paths, which is a useful result of independent interest. In addition, we show that the above measure can be sampled using standard Markov Chain Monte Carlo techniques, thus providing a simple method for approximating shortest loops.

scholarly journals Determination of an Effective Detector Position for Pulsed-Neutron-Source Alpha Measurement by Time-Dependent Monte Carlo Neutron Transport Simulations

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Sang Hoon Jang ◽  
Hyung Jin Shim
Keyword(s):  
Monte Carlo ◽  
Neutron Source ◽  
Neutron Transport ◽  
Signal Amplitude ◽  
Time Dependent ◽  
Convergence Time ◽  
Simple Method ◽  
Detector Position ◽  
Pulsed Neutron ◽  
Pulsed Neutron Source

A simple method using the time-dependent Monte Carlo (TDMC) neutron transport calculation is presented to determine an effective detector position for the prompt neutron decay constant (α) measurement through the pulsed-neutron-source (PNS) experiment. In the proposed method, the optimum detector position is searched by comparing amplitudes of detector signals at different positions when their α estimates by the slope fitting are converged. The developed method is applied to the Pb-Bi-zoned ADS experimental benchmark at Kyoto University Critical Assembly. The α convergence time estimated by the TDMC PNS simulation agrees well with the experimental results. The α convergence time map and the corresponding signal amplitude map predicted by the developed method show that polyethylene moderator regions adjacent to fuel region are better positions than other candidates for the PNS α measurement.

scholarly journals Field gamma spectrometry, Monte Carlo simulations and potential of non-invasive measurements

2012 ◽  
Vol 39 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Guillaume Guérin ◽  
Norbert Mercier
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Dose Rate ◽  
Gamma Dose ◽  
Luminescence Dating ◽  
Gamma Dose Rate ◽  
Invasive Technique ◽  
Simple Method ◽  
Dose Rates ◽  
Non Invasive

Abstract The determination of gamma dose rates is of prior importance in the field of luminescence dating methods. In situ measurements are usually performed by the insertion of dosimeters or a portable gamma spectrometer cell in sediments. In this paper, Monte-Carlo simulations using the Geant4 toolkit allow the development of a new technique of insitu gamma dose rate evaluations: a spectrometer cell is placed on the surface of sediments under excavation to acquire successive spectra as sediments are removed by excavations. The principle of this non-invasive technique is outlined and its potential is discussed, especially in the case of environments in which radioelements are heterogeneously distributed. For such cases, a simple method to reconstruct gamma dose rate values with surface measurements using an attenuator is discussed, and an estimation of errors is given for two simple cases. This technique appears to be applicable, but still needs experimental validation.

scholarly journals A simple method for implementing Monte Carlo tests

2019 ◽  
Vol 35 (3) ◽  
pp. 1373-1392 ◽  
Author(s):  
Dong Ding ◽  
Axel Gandy ◽  
Georg Hahn
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Statistical Test ◽  
Uniform Bound ◽  
Data Set ◽  
Simple Method ◽  
Practical Applications ◽  
Tuning Parameters ◽  
Sequence Method ◽  
Uniformly Bounded

Abstract We consider a statistical test whose p value can only be approximated using Monte Carlo simulations. We are interested in deciding whether the p value for an observed data set lies above or below a given threshold such as 5%. We want to ensure that the resampling risk, the probability of the (Monte Carlo) decision being different from the true decision, is uniformly bounded. This article introduces a simple open-ended method with this property, the confidence sequence method (CSM). We compare our approach to another algorithm, SIMCTEST, which also guarantees an (asymptotic) uniform bound on the resampling risk, as well as to other Monte Carlo procedures without a uniform bound. CSM is free of tuning parameters and conservative. It has the same theoretical guarantee as SIMCTEST and, in many settings, similar stopping boundaries. As it is much simpler than other methods, CSM is a useful method for practical applications.

Monte Carlo Simulations with an Improved Potential Function for Cu(II)-Water Including Neighbour Ligand Corrections

1991 ◽  
Vol 46 (4) ◽  
pp. 357-362 ◽  
Author(s):  
Bernd M. Rode ◽  
Saiful M. Islam
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Potential Function ◽  
Hydration Shell ◽  
Pair Potential ◽  
Computational Effort ◽  
Direct Construction ◽  
Simple Method ◽  
Dilute Aqueous Solution ◽  
Simple Pair

Abstract Monte Carlo simulations for a Cu2+ ion in infinitely dilute aqueous solution were performed on the basis of a simple pair potential function leading to a first-shell coordination number of 8, in contrast to experimental data. A simple method was introduced therefore, which allows the direct construction of a pair potential containing the most relevant 3-body interactions by means of a correction for the nearest neighbour ligands in the ion's first hydration shell. This procedure leads to much improved results, without significant increase in computational effort during potential construction and simulation

DEFINITION OF PETROLEUM RESERVES USING PROBABILITY ANALYSIS

1979 ◽  
Vol 19 (1) ◽  
pp. 197
Author(s):  
B.G. McKay ◽  
N.F. Taylor
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Oil And Gas ◽  
Water Saturation ◽  
Cumulative Probability ◽  
Reservoir Properties ◽  
Simple Method ◽  
Volume Porosity ◽  
Cumulative Curve ◽  
Definition Of

The realistic estimation of reserves and resources is important to many diverse groups including explorers, producers, auditors, taxmen, bankers, shareholders and governments. Reserves data are used in different ways for a variety of reasons and often the figures are used without adequate definition and/or recognition of the uncertainties associated with them. Any calculation method which fails to consider the uncertainties involved, cannot portray a realistic assessment of reserves.Esso Australia Ltd. uses a relatively simple method to generate probability distribution curves in order to allow a more perceptive definition of the range of reserves for the offshore oil and gas fields in the Gippsland Basin and Esso is advocating wider petroleum and mineral industry acceptance of this approach.The method involves defining data distributions for each of the reservoir properties (volume, porosity, water saturation, compressibility and recovery factor) which are multiplied using Monte Carlo Simulation to generate the distribution of reserves. Actual input consists of data from:A high confidence area immediately surrounding well control, where the rock volume is relatively closely defined and the distributions of the other parameters, with the exception of recovery factors, reflect the observed variations.Other areas which are only seismically controlled, where the data ranges reflect both observed and interpreted variations in volume (gross and net), porosity, water saturation, compressibility and recovery factor.The curves generated for each area are then added by Monte Carlo Summation to yield the probability distribution of reserves for the whole field. In this method all available data are used and fewer subjective decisions are necessary. The computer generated distribution curves plot cumulative probability on the y-axis versus reserves on the x-axis. The curves allow the evaluation of the entire range of potential reserves, are valuable in economic and risk assessments and allow for more consistency in defining reserves for reporting purposes. The different categories of reserves, viz. "proved", "probable" or "possible", can be specified from the total field curves at defined probabilities. Moreover, the slope of the cumulative curve provides a direct indication of the level of knowledge of the field or parts of it.

Sign in / Sign up

Export Citation Format

Share Document