scholarly journals Study of the Influence of On-Deposit Locations in Data-Driven Mineral Prospectivity Mapping: A Case Study on the Iskut Project in Northwestern British Columbia, Canada

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 597
Author(s):  
Alix Lachaud ◽  
Adam Marcus ◽  
Slobodan Vučetić ◽  
Ilija Mišković
Keyword(s):  
British Columbia ◽  
Selection Criterion ◽  
Study Data ◽  
Random Point ◽  
Data Driven ◽  
Point Pattern ◽  
Point Patterns ◽  
Multiple Data Sets ◽  
Mineral Prospectivity ◽  
Epithermal Au

The accuracy of data-driven predictive mineral prospectivity models relies heavily on the training datasets used. These models are usually trained using data for “known” deposit locations as well as “non-deposit” locations that are based on randomly generated point patterns. In this study, data related to the Seabridge Gold Inc Iskut project, an epithermal Au deposit in northwestern British Columbia (BC), Canada, are used to test the utility of data-driven mineral prospectivity modeling. The input spatial dataset is comprised mostly of publicly available data. Data for 18 vein and epithermal Au known mineral occurrences (KMO) are obtained from the BC Geological Survey’s MINFILE repository and selected as training deposit locations. A total of eleven sets of non-deposit locations (NDL) were also created, including one set of selected non-prospective KMO for Au deposits from the MINFILE and ten sets of random point patterns. Given the scale of this study, most of the KMO recorded on the property are of the epithermal deposit type. Hence, they could not be used as a selection criterion. Data-driven mineral potential models are generated using the random forest (RF) algorithm and trained on multiple data sets. The comparison of RF models demonstrated that using non-prospective KMO generates more accurate predictions than the random point pattern. The produced mineral prospectivity maps delineated multiple areas with higher discovery potential, which matched viable targets for the Au-Cu epithermal-porphyry system identified through previous Seabridge Gold Inc. (Toronto, ON, Canada) field reconnaissance and drilling programs.

scholarly journals Does fluvial channel-belt clustering predict net sand to gross rock volume? Architectural metrics and point-pattern analysis of a digital outcrop model

2019 ◽  
Vol 89 (11) ◽  
pp. 1109-1126
Author(s):  
Alexander R. Koch ◽  
Cari L. Johnson ◽  
Lisa Stright
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Point Pattern ◽  
Point Patterns ◽  
Spatial Point ◽  
Channel Belt ◽  
John Henry Member ◽  
Scale Point ◽  
Belt Width ◽  
Digital Outcrop

ABSTRACT Spatial point-pattern analyses (PPAs) are used to quantify clustering, randomness, and uniformity of the distribution of channel belts in fluvial strata. Point patterns may reflect end-member fluvial architecture, e.g., uniform compensational stacking and avulsion-generated clustering, which may change laterally, especially at greater scales. To investigate spatial and temporal changes in fluvial systems, we performed PPA and architectural analyses on extensive outcrops of the Cretaceous John Henry Member of the Straight Cliffs Formation in southern Utah, USA. Digital outcrop models (DOMs) produced using unmanned aircraft system-based stereophotogrammetry form the basis of detailed interpretations of a 250-m-thick fluvial succession over a total outcrop length of 4.5 km. The outcrops are oriented roughly perpendicular to fluvial transport direction. This transverse cross-sectional exposure of the fluvial system allows a study of the system's variation along depositional strike. We developed a workflow that examines spatial point patterns using the quadrat method, and architectural metrics such as net sand to gross rock volume (NTG), amalgamation index, and channel-belt width and thickness within moving windows. Quadrat cell sizes that are ∼ 50% of the average channel-belt width-to-thickness ratio (16:1 aspect ratio) provide an optimized scale to investigate laterally elongate distributions of fluvial-channel-belt centroids. Large-scale quadrat point patterns were recognized using an array of four quadrat cells, each with 237× greater area than the median channel belt. Large-scale point patterns and NTG correlate negatively, which is a result of using centroid-based PPA on a dataset with disparately sized channel belts. Small-scale quadrat point patterns were recognized using an array of 16 quadrat cells, each with 21× greater area than the median channel belt. Small-scale point patterns and NTG correlate positively, and match previously observed stratigraphic trends in the fluvial John Henry Member, suggesting that these are regional trends. There are deviations from these trends in architectural statistics over small distances (hundreds of meters) which are interpreted to reflect autogenic avulsion processes. Small-scale autogenic processes result in architecture that is difficult to correlate between 1D datasets, for example when characterizing a reservoir using well logs. We show that 1D NTG provides the most accurate prediction for surrounding 2D architecture.

scholarly journals COMBINING DATA-DRIVEN AND NUMERICAL MODELLING APPROACHES TO STORM EROSION PREDICTION

2020 ◽  
pp. 38
Author(s):  
Joshua Simmons ◽  
Kristen Splinter
Keyword(s):  
Numerical Models ◽  
Individual Performance ◽  
Study Data ◽  
Data Driven ◽  
Erosion Prediction ◽  
Trade Offs ◽  
Combining Data ◽  
Erosion Models ◽  
Using Data ◽  
The Individual

Physics-based numerical models play an important role in the estimation of storm erosion, particularly at beaches for which there is little historical data. However, the increasing availability of pre-and post-storm data for multiple events and at a number of beaches around the world has opened the possibility of using data-driven approaches for erosion prediction. Both physics-based and purely data-driven approaches have inherent strengths and weaknesses in their ability to predict storm-induced erosion. It is vital that coastal managers and modelers are aware of these trade-offs as well as methods to maximise the value from each modelling approach in an increasingly data-rich environment. In this study, data from approximately 40 years of coastal monitoring at Narrabeen-Collaroy Beach (SE Australia)has been used to evaluate the individual performance of the numerical erosion models SBEACH and XBeach, and a data-driven modelling technique. The models are then combined using a simple weighting technique to provide a hybrid estimate of erosion.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/v53dZiO8Y60

scholarly journals Perspective: US Adult Magnesium Requirements Need Updating: Impacts of Rising Body Weights and Data-Derived Variance

2020 ◽  
Author(s):  
Andrea Rosanoff
Keyword(s):  
Study Data ◽  
Healthy Adults ◽  
Data Driven ◽  
Cvd Risk ◽  
Balance Study ◽  
Men And Women ◽  
Body Weights ◽  
Adult Body ◽  
Reference Body ◽  
Traditional Diets

ABSTRACT Adequate magnesium intakes are associated with lower diabetes, hypertension, and cardiovascular disease (CVD) risk but are low in modern diets. Magnesium DRIs, estimated using standard reference body weights (SRBWs) lower than current mean US adult body weights (BWs), need revision. Magnesium DRIs assume variance at 10% CV, whereas balance study data suggests 20–30% CV. Here, estimated average requirements (EARs), the DRI measure estimating average magnesium requirements for healthy adults, were corrected using 2011–2014 mean US adult BWs. Magnesium EARs (in mg magnesium/d) increased 17% for men (330–350 to 386–409) and 25% for women (255–265 to 319–332). RDAs, the DRI measure meant to cover the magnesium needs of 98% of healthy adults, were calculated using BW-corrected EARs given 3 CV levels: 1) 10% (assumed in 1997 DRIs), 2) 20% (model-derived variance from USDA magnesium studies), and 3) 30% (using USDA plus older human magnesium balance data). BW-corrected magnesium RDAs (in mg magnesium/d) rose from 400–420 and 310–320 for men and women, respectively, to 1) 463–491 and 383–398 (16.5% and 23.5% increases), 2) 540–573 and 447–465 (35.5% and 44.5% increases), and 3) 617–654 and 511–531 (55% and 65.5% increases). These recalculations move magnesium intakes estimated to prevent disease into ranges found in traditional diets and to intake levels shown to lower hypertension, diabetes, and CVD risk. In conclusion, mean BW rises over the last ≥20 y and data-driven estimates of CV indicate that reliable US adult magnesium RDAs are ≥60–235 and 70–210 mg magnesium/d higher for men and women, respectively, than the current 1997 RDAs. US adult BMIs are <25 kg/m2 when calculated with SRBWs but >25 with actual mean BWs. Adjustments for rising BW are necessary for magnesium DRIs to remain useful tools for defining magnesium intake adequacy/deficiency.

scholarly journals Spatial analysis of phylogenetic community structure: New version of a classical method

2015 ◽  
Author(s):  
Carlo Ricotta ◽  
Eszter EA Ari ◽  
Giuliano Bonanomi ◽  
Francesco Giannino ◽  
Duncan Heathfield ◽  
...  
Keyword(s):  
Community Structure ◽  
Spatial Scales ◽  
Point Pattern ◽  
Maximum Deviation ◽  
Individual Plant ◽  
Phylogenetic Distance ◽  
Phylogenetic Structure ◽  
Phylogenetic Community Structure ◽  
Point Patterns ◽  
Wide Range

The increasing availability of phylogenetic information facilitates the use of evolutionary methods in community ecology to reveal the importance of evolution in the species assembly process. However, while several methods have been applied to a wide range of communities across different spatial scales with the purpose of detecting non-random phylogenetic patterns, the spatial aspects of phylogenetic community structure have received far less attention. Accordingly, the question for this study is: can point pattern analysis be used for revealing the phylogenetic structure of multi-species assemblages? We introduce a new individual-centered procedure for analyzing the scale-dependent phylogenetic structure of multi-species point patterns based on digitized field data. The method uses nested circular plots with increasing radii drawn around each individual plant and calculates the mean phylogenetic distance between the focal individual and all individuals located in the circular ring delimited by two successive radii. This scale-dependent value is then averaged over all individuals of the same species and the observed mean is compared to a null expectation with permutation procedures. The method detects particular radius values at which the point pattern of a single species exhibits maximum deviation from the expectation towards either phylogenetic aggregation or segregation. Its performance is illustrated using data from a grassland community in Hungary and simulated point patterns. The proposed method can be extended to virtually any distance function for species pairs, such as functional distances.

Multi-instance Classification Based on Random Point Pattern

2021 ◽  
Author(s):  
Haojie Yang ◽  
Weifeng Liu ◽  
Zhangming Peng
Keyword(s):  
Random Point ◽  
Point Pattern
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