scholarly journals Geophysical Field Data Interpolation Using Stochastic Partial Differential Equations for Gold Exploration in Dayaoshan, Guangxi, China

Minerals ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Zhenwei Guo ◽  
Xiangping Hu ◽  
Jianxin Liu ◽  
Chunming Liu ◽  
Jianping Xiao
Keyword(s):  
Field Data ◽  
Mineral Exploration ◽  
Estimation Method ◽  
Synthetic Data ◽  
Geophysical Data ◽  
Partial Differential ◽  
Geophysical Field ◽  
Mathematical Algorithm ◽  
Model Based ◽  
Gold Mineral

In a geophysical survey, one of the main challenges is to estimate the physical parameter using limited geophysical field data with noise. Geophysical datasets are measured with sparse sampling in a survey. However, the limited data constrain the geophysical interpretation. Traditionally, the field data has been interpolated using mathematical algorithm. In many cases, the estimated field data uncertainties are required to determine which earth models are consistent with the observations. A model-based data-estimation method can provide precise information for imaging and interpretation. The approach used in this paper is based on a stochastic partial differential equation, and it is employed to predict the geophysical data. With this statistical model-based approach, the sparse sample from a survey is used to estimate the underlying spatial surface, and it is assumed that the predicted geophysical data have the same probability density function as the observed data. Furthermore, this method can return the uncertainties of the prediction. Both the synthetic data and the gold mineral exploration field data cases illustrate that this approach leads to better results than traditional methods.

Cover thickness uncertainty mapping using Bayesian estimate fusion: leveraging domain knowledge

2019 ◽  
Vol 219 (3) ◽  
pp. 1474-1490 ◽  
Author(s):  
Gerhard Visser ◽  
Jelena Markov
Keyword(s):  
Domain Knowledge ◽  
Mineral Exploration ◽  
Synthetic Data ◽  
Drill Hole ◽  
Geophysical Data ◽  
Bayesian Estimate ◽  
Data Types ◽  
Interpolation Methods ◽  
Uncertainty Mapping ◽  
Cover Thickness

SUMMARY Thickness of cover over crystalline basement is an important consideration for mineral exploration in covered regions. It can be estimated from a variety of geophysical data types using a variety of inference methods. A robust method for combining such estimates to map the cover–basement interface over a region of interest is needed. Due to the large uncertainties involved, these need to be probabilistic maps. Predominantly, interpolation methods are used for this purpose, but these are built on simplifying assumptions about the inputs which are often inappropriate. The Bayesian estimate fusion is an alternative capable of addressing that issue by enabling more extensive use of domain knowledge about all inputs. This study is intended as a first step towards making the Bayesian estimate fusion a practical tool for cover thickness uncertainty mapping. The main contribution is to identify the types of data assumptions that are important for this problem, to demonstrate their importance using synthetic tests and to design a method that enables their use without introducing excessive tedium. We argue that interpolation methods like kriging often cannot achieve this goal and demonstrate that Markov chain Monte Carlo sampling can. This paper focuses on the development of statistical methodology and presents synthetic data tests designed to reflect realistic exploration scenarios on an abstract level. Intended application is for the early stages of exploration where some geophysical data are available while drill hole coverage is poor.

A novel fusion model based online state of power estimation method for lithium-ion capacitor

2021 ◽  
Vol 36 ◽  
pp. 102387
Author(s):  
Wenxin Chen ◽  
Cheng Xu ◽  
Manlin Chen ◽  
Kai Jiang ◽  
Kangli Wang
Keyword(s):  
Estimation Method ◽  
Lithium Ion ◽  
Power Estimation ◽  
Fusion Model ◽  
Model Based ◽  
Lithium Ion Capacitor ◽  
State Of Power

scholarly journals Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 674
Author(s):  
Kushani De De Silva ◽  
Carlo Cafaro ◽  
Adom Giffin
Keyword(s):  
State Of The Art ◽  
Estimation Method ◽  
Synthetic Data ◽  
Noisy Data ◽  
Bayesian Framework ◽  
Physical Systems ◽  
Gradient Profile ◽  
Ill Posed ◽  
Profile Estimation ◽  
Different Levels

Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the estimated system. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. This becomes worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the gradient profile of discrete noisy data is presented. The method was developed within a Bayesian framework. Comprehensive numerical experiments were conducted on synthetic data at different levels of noise. The accuracy of the proposed method was quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.

Improvement of METANET Model Based on Critical Density Modeling for Traffic Control

2021 ◽  
Author(s):  
Lina Fu ◽  
Jie Fang ◽  
Yunjie Lyu ◽  
Huahui Xie
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Field Data ◽  
Critical Density ◽  
Predictive Performance ◽  
Density Model ◽  
Model Based ◽  
Freeway Control ◽  
Density Modeling

Freeway control has been increasingly used as an innovative approach to ease traffic congestion, improve traffic safety and reduce exhaust emissions. As an important predictive model involved in freeway control, the predictive performance of METANET greatly influences the effect of freeway control. This paper focuses on modifying the METANET model by modeling the critical density. Firstly, the critical density model is deduced based on the catastrophe theory. Then, the perturbation wave and traveling wave that are obtained using the macro and micro data, respectively, have been developed to modify the above proposed critical density model. Finally, the numerical simulation is established to evaluate the effectiveness of the modified METANET model based on the field data from the realistic motorway network. The results show that overall, the predicted data from the modified METANET model are closer to the field data than those obtained from the original model.

Sign in / Sign up

Export Citation Format

Share Document