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.

Reconciling Cover Thickness Estimates in Cloncurry Region in Queensland, Australia using Bayesian Estimate Fusion

2020 ◽  
Author(s):  
Jelena Markov ◽  
Gerhard Visser
Keyword(s):  
Mineral Exploration ◽  
Optimal Solution ◽  
Geophysical Data ◽  
Bayesian Estimate ◽  
Mount Isa ◽  
History Of ◽  
Cover Thickness ◽  
Inference Methods ◽  
Drill Holes ◽  
Fault Information

<p>The Cloncurry region lies in NW of Queensland and includes the Mount Isa Inlier, one of the most highly endowed metallogenic provinces in Australia, which has a long history of mining and exploration. The area is covered by the Jurassic-Cretaceous Carpentaria and Eromanga Basin sediments with the Mount Isa Inlier outcropping to the West and South. The fully concealed Millungera Basin underlies younger basins to the East. In order to de-risk further mineral exploration in this region it is important to know the thickness of cover. There are a variety of geophysical data available that can be used to estimate cover thickness. The point depth estimates of cover are derived from geophysical data using different inference methods. In order to create a map, these individual depth estimates must be reconciled/interpolated. The conventional interpolation methods do not produce the most optimal solution since these methods don’t easily account for discrepancies in the geophysical data distribution, resolution of the data and consequently variable accuracy of the cover thickness depth estimates. Also, most of these techniques do not produce an uncertainty estimate of the result. We have developed a Bayesian estimate fusion method that accounts for the variable data inaccuracies of the point cover thickness estimates which produces a map of cover thickness and its uncertainty. Additionally, the method uses non-intersecting drill holes, which were not usually utilised to create a map of the cover thickness. The method deals with outliers, by differentiating between the point depth estimates related to the cover-basement interface and the false positives that might be coming from the intrasedimentary units or the deeper basement. Lastly, the method incorporates existing fault information which allows to better capture sharp cover thickness changes.</p>

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.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

scholarly journals <i>dh2loop</i> 1.0: an open-source Python library for automated processing and classification of geological logs

2021 ◽  
Vol 14 (11) ◽  
pp. 6711-6740
Author(s):  
Ranee Joshi ◽  
Kavitha Madaiah ◽  
Mark Jessell ◽  
Mark Lindsay ◽  
Guillaume Pirot
Keyword(s):  
Open Source ◽  
Mineral Exploration ◽  
String Matching ◽  
Drill Hole ◽  
Geological Survey ◽  
Database Structure ◽  
Automated Processing ◽  
Study Case ◽  
Textual Form

Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021​​​​​​​) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).

Estimation Update and Feedback Procedures

2008 ◽  
Author(s):  
P.J. Lee
Keyword(s):  
Water Saturation ◽  
Geophysical Data ◽  
Source Rocks ◽  
Geochemical Data ◽  
Burial History ◽  
Data Types ◽  
Petroleum Resource ◽  
History Of ◽  
Seismic Sections ◽  
Well Data

A basin or subsurface study, which is the first step in petroleum resource evaluation, requires the following types of data: • Reservoir data—pool area, net pay, porosity, water saturation, oil or gas formation volume factor, in-place volume, recoverable oil volume or marketable gas volume, temperature, pressure, density, recovery factors, gas composition, discovery date, and other parameters (refer to Lee et al., 1999, Section 3.1.2). • Well data—surface and bottom well locations; spud and completion dates; well elevation; history of status; formation drill and true depths; lithology; drill stem tests; core, gas, and fluid analyses; and mechanical logs. • Geochemical data—types of source rocks, burial history, and maturation history. • Geophysical data—prospect maps and seismic sections. Well data are essential when we construct structural contour, isopach, lithofacies, porosity, and other types of maps. Geophysical data assist us when we compile number-of-prospect distributions and they provide information for risk analysis.

scholarly journals A new method for interpolating linear features in aeromagnetic data

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. JM15-JM24 ◽  
Author(s):  
Tomas Naprstek ◽  
Richard S. Smith
Keyword(s):  
Field Data ◽  
Interpolation Method ◽  
Source Code ◽  
Synthetic Data ◽  
Aeromagnetic Data ◽  
Derivative Expansion ◽  
Linear Features ◽  
Interpolation Methods ◽  
Minimum Curvature ◽  
Linear Nature

When aeromagnetic data are interpolated to make a gridded image, thin linear features can result in “boudinage” or “string of beads” artifacts if the anomalies are at acute angles to the traverse lines. These artifacts are due to the undersampling of these types of features across the flight lines, making it difficult for most interpolation methods to effectively maintain the linear nature of the features without user guidance. The magnetic responses of dikes and dike swarms are typical examples of the type of geologic feature that can cause these artifacts; thus, these features are often difficult to interpret. Many interpretation methods use various enhancements of the gridded data, such as horizontal or vertical derivatives, and these artifacts are often exacerbated by the processing. Therefore, interpolation methods that are free of these artifacts are necessary for advanced interpretation and analysis of thin, linear features. We have developed a new interpolation method that iteratively enhances linear trends across flight lines, ensuring that linear features are evident on the interpolated grid. Using a Taylor derivative expansion and structure tensors allows the method to continually analyze and interpolate data along anisotropic trends, while honoring the original flight line data. We applied this method to synthetic data and field data, which both show improvement over standard bidirectional gridding, minimum curvature, and kriging methods for interpolating thin, linear features at acute angles to the flight lines. These improved results are also apparent in the vertical derivative enhancement of field data. The source code for this method has been made publicly available.

Seismic reflection profiles across the "Mine Series" in the Noranda camp of the Abitibi belt, eastern Canada

1995 ◽  
Vol 32 (2) ◽  
pp. 167-176 ◽  
Author(s):  
Pierre Verpaelst ◽  
A. Shirley Péloquin ◽  
Erick Adam ◽  
Arthur E. Barnes ◽  
John N. Ludden ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Reflection ◽  
Mineral Exploration ◽  
Drill Hole ◽  
Seismic Survey ◽  
Volcanic Complex ◽  
Eastern Canada ◽  
Crustal Layer ◽  
High Resolution Data ◽  
Seismic Reflection Profiles

The Abitibi–Grenville Lithoprobe project completed a regional (line 21) and a high-resolution (line 21-1) seismic survey in the Noranda Central Volcanic Complex of the Blake River Group, Abitibi, Quebec. Line 21 provides a regional framework in which the Archean crust is divided into three layers, two of which are discussed here: the uppermost layer, which corresponds to the Blake River Group, is the least reflective, and lies above 4 s (12 km), and the mid-crustal layer, which is composed of a complex pattern of generally east-northeast-dipping reflectors and lies between 4 and 8 s. Within the regional data, the Mine Series of the Central Volcanic Complex is imaged as a semitransparent series of reflectors overlying a highly reflective east-facing structure interpreted as the subvolcanic Flavrian pluton. The high-resolution data (line 21-1) were collected in the vicinity of the Ansil mine. The seismic images in this region can be controlled by surface geology and extensive drill-hole data, and the project was designed to test the applicability of seismic reflection profiling in providing structural and stratigraphic information for use in mineral exploration: shallow-dipping reflectors correlate well with lithological variations or contacts in the volcanic sequence; strong subhorizontal reflectors correspond to diorite and gabbro dykes and sills; several abrupt lateral changes in the reflectivity coincide with known intrusive contacts such as the Lac Dufault pluton.

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