scholarly journals Insights and Lessons from 3D Geological and Geophysical Modeling of Mineralized Terranes in Tasmania

Minerals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1195
Author(s):  
Daniel Bombardieri ◽  
Mark Duffett ◽  
Andrew McNeill ◽  
Matthew Cracknell ◽  
Anya Reading
Keyword(s):  
3D Modeling ◽  
Mineral Exploration ◽  
Mineral Resources ◽  
3D Models ◽  
Data Sets ◽  
Potential Field Data ◽  
Geophysical Modeling ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Model Features

Over the last two decades, Mineral Resources Tasmania has been developing regional 3D geological and geophysical models for prospective terranes at a range of scales and extents as part of its suite of precompetitive geoscience products. These have evolved in conjunction with developments in 3D modeling technology over that time. Commencing with a jurisdiction-wide 3D model in 2002, subsequent modeling projects have explored a range of approaches to the development of 3D models as a vehicle for the better synthesis and understanding of controls on ore-forming processes and prospectivity. These models are built on high-quality potential field data sets. Assignment of bulk properties derived from previous well-constrained geophysical modeling and an extensive rock property database has enabled the identification of anomalous features that have been targeted for follow-up mineral exploration. An aspect of this effort has been the generation of uncertainty estimates for model features. Our experience is that this process can be hindered by models that are too large or too detailed to be interrogated easily, especially when modeling techniques do not readily permit significant geometric changes. The most effective 3D modeling workflow for insights into mineral exploration is that which facilitates the rapid hypothesis testing of a wide range of scenarios whilst satisfying the constraints of observed data.

scholarly journals A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

2021 ◽  
Author(s):  
William Gregory ◽  
Isobel Lawrence ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Data Sets ◽  
Sea Ice Thickness ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Arctic Sea ◽  
Thickness Variability ◽  
Satellite Radar ◽  
Validation Experiments

<p>Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thicknessestimates, which in turn inform on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporalresolution of radar freeboard is limited as 30 days are required in order to generate pan-Arctic coverage fromCryoSat-2, or 27 days from Sentinel-3 satellites. This therefore hinders our ability to understand physical processesthat drive sea ice thickness variability on sub-monthly time scales. In this study we exploit the consistency betweenCryoSat-2, Sentinel-3A and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboardestimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian Process Regressionto learn functional mappings between radar freeboard observations in space and time, and to subsequently retrievepan-Arctic freeboard, as well as uncertainty estimates. The estimated daily fields are, on average across the 2018-2019season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 2 mm, and cross-validation experiments show thaterrors in predictions are, on average, within 3 mm across the same period. This method presents as a robust frameworkwhich can be used to model a wide range of statistical problems, from interpolation of altimetry data sets, to timeseries forecasting.</p>

Geophysical inversion contributions to mineral exploration: lessons from the Footprints project

2019 ◽  
Vol 56 (5) ◽  
pp. 525-543 ◽  
Author(s):  
Marc A. Vallée ◽  
William A. Morris ◽  
Stéphane Perrouty ◽  
Robert G. Lee ◽  
Ken Wasyliuk ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Physical Property ◽  
3D Models ◽  
Geological Model ◽  
Inversion Algorithm ◽  
Potential Field Data ◽  
Mining Sites ◽  
Copper Site ◽  
Magnetic Signature ◽  
Depth Extent

Magnetic and gravity inversions are used to create 2D or 3D models of the magnetic susceptibility and density, respectively, using potential field data. Unconstrained inversions generate an output based on mathematical constraints imposed by the inversion algorithm. Constrained inversions integrate lithological, structural, and petrophysical information in the inversion process to produce more geologically meaningful results. This study analyses the validity of this assertion in the context of the NSERC-CMIC Mineral Exploration Footprints project. Unconstrained and constrained geophysical inversions were computed for three mining sites: a gold site (Canadian Malartic, Québec), a copper site (Highland Valley, British Columbia), and a uranium site (Millennium – McArthur River, Saskatchewan). After initially computing unconstrained inversions, constrained inversions were developed using physical property measurements, which directly link geophysics to geology, and lithological boundaries extracted from an interpreted geological model. While each derived geological model is consistent with the geophysical data, each site exhibited some magnetic complexity that confounded the inversion. The gold site includes regions with a strong magnetic signature that masks the more weakly magnetic zone, thereby hiding the magnetic signature associated with the ore body. Initial unconstrained inversions for the copper site yielded solutions with invalid depth extent. A consistency between the constrained model and the geological model is reached with iterative changes to the depth extent of the model. At the uranium site, the observed magnetic signal is weak, but the inversion provided some insights that could be interpreted in terms of an already known complexly folded geological model.

scholarly journals Seismic methods in mineral exploration and mine planning: A general overview of past and present case histories and a look into the future

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. WC173-WC190 ◽  
Author(s):  
Alireza Malehmir ◽  
Raymond Durrheim ◽  
Gilles Bellefleur ◽  
Milovan Urosevic ◽  
Christopher Juhlin ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Mining Industry ◽  
Mineral Resources ◽  
Great Depth ◽  
Mine Planning ◽  
Mineral Deposits ◽  
Mining Areas ◽  
Seismic Methods ◽  
Wide Range ◽  
Borehole Seismic

Due to high metal prices and increased difficulties in finding shallower deposits, the exploration for and exploitation of mineral resources is expected to move to greater depths. Consequently, seismic methods will become a more important tool to help unravel structures hosting mineral deposits at great depth for mine planning and exploration. These methods also can be used with varying degrees of success to directly target mineral deposits at depth. We review important contributions that have been made in developing these techniques for the mining industry with focus on four main regions: Australia, Europe, Canada, and South Africa. A wide range of case studies are covered, including some that are published in the special issue accompanying this article, from surface to borehole seismic methods, as well as petrophysical data and seismic modeling of mineral deposits. At present, high-resolution 2D surveys mostly are performed in mining areas, but there is a general increasing trend in the use of 3D seismic methods, especially in mature mining camps.

Multi-scale Numerical Simulation and 3D Modeling for Deep Mineral Exploration in the Jiaojia Gold District, China

2019 ◽  
Vol 29 (1) ◽  
pp. 415-438 ◽  
Author(s):  
Leilei Huang ◽  
Gongwen Wang ◽  
Emmanuel John M. Carranza ◽  
Jingguo Du ◽  
Junjian Li ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
3D Modeling ◽  
Mineral Exploration ◽  
Multi Scale

scholarly journals mtDNAcombine: tools to combine sequences from multiple studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica
Keyword(s):  
Sequence Data ◽  
Data Extraction ◽  
Bayesian Skyline Plot ◽  
Model Organisms ◽  
Data Sets ◽  
Data Handling ◽  
Online Database ◽  
Genetic Studies ◽  
Wide Range ◽  
Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra

Minerals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 347
Author(s):  
Carsten Laukamp ◽  
Andrew Rodger ◽  
Monica LeGras ◽  
Heta Lampinen ◽  
Ian C. Lau ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Relevant Information ◽  
Ore Deposits ◽  
Reflectance Spectra ◽  
Full Potential ◽  
Infrared Reflectance ◽  
Vibrational Modes ◽  
Diagnostic Features ◽  
Wide Range ◽  
Mineral Assemblages

Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

scholarly journals Tracking late Pleistocene Neandertals on the Iberian coast

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eduardo Mayoral ◽  
Ignacio Díaz-Martínez ◽  
Jéremy Duveau ◽  
Ana Santos ◽  
Antonio Rodríguez Ramírez ◽  
...  
Keyword(s):  
High Resolution ◽  
Iberian Peninsula ◽  
Social Group ◽  
Microbial Mats ◽  
3D Models ◽  
Age Classes ◽  
Upper Pleistocene ◽  
Wide Range ◽  
The World ◽  
Flooded Area

AbstractHere, we report the recent discovery of 87 Neandertal footprints on the Southwest of the Iberian Peninsula (Doñana shoreline, Spain) located on an upper Pleistocene aeolian littoral setting (about 106 ± 19 kyr). Morphometric comparisons, high resolution digital photogrammetric 3D models and detailed sedimentary analysis have been provided to characterized the footprints and the palaeoenvironment. The footprints were impressed in the shoreline of a hypersaline swamped area related to benthic microbial mats, close to the coastline. They have a rounded heel, a longitudinal arch, relatively short toes, and adducted hallux, and represent the oldest upper Pleistocene record of Neandertal footprints in the world. Among these 87 footprints, 31 are longitudinally complete and measure from 14 to 29 cm. The calculated statures range from 104 to 188 cm, with half of the data between 130 and 150 cm. The wide range of sizes of the footprints suggests the existence of a social group integrated by individuals of different age classes but dominated, however, by non-adult individuals. The footprints, which are outside the flooded area are oriented perpendicular to the shoreline. These 87 footprints reinforce the ecological scenario of Neandertal groups established in coastal areas.

scholarly journals A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Modeling ◽  
Critical Evaluation ◽  
Particle Swarm ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Swarm Optimization ◽  
Geophysical Modeling ◽  
Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

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