Mineral prospectivity mapping using a VNet convolutional neural network

2021 ◽  
Vol 40 (2) ◽  
pp. 99-105
Author(s):  
Michael McMillan ◽  
Eldad Haber ◽  
Bas Peters ◽  
Jennifer Fohring
Keyword(s):  
Artificial Intelligence ◽  
Gold Mineralization ◽  
Spatial Scales ◽  
Canadian Arctic ◽  
Novel Technologies ◽  
Regional Trends ◽  
Resource Industry ◽  
Mineral Prospectivity ◽  
Efficient Exploration ◽  
Drill Holes

Major mineral discoveries have declined in recent decades, and the natural resource industry is in the process of adapting and incorporating novel technologies such as machine learning and artificial intelligence to help guide the next generation of exploration. One such development is an artificial intelligence architecture called VNet that uses deep learning and convolutional neural networks. This method is designed specifically for use with geoscience data and is suitable for a multitude of exploration applications. One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses gold values from sparse drill holes for training purposes to predict gold mineralization elsewhere in the region. The prospectivity results highlight new target areas, and one such target was followed up with a direct-current induced polarization survey. A chargeability anomaly was discovered wherein the VNet had predicted gold mineralization, and subsequent drilling encountered a 6 g/t Au intercept within 10 m of drilling that averaged more than 1.0 g/t Au. Although most of the prospectivity targets generated from VNet were not drill tested, this first intercept helps validate the approach. We believe this method can help maximize the use of existing geoscience data for successful and efficient exploration programs in the future.

scholarly journals Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3162
Author(s):  
Pierfrancesco Visaggi ◽  
Brigida Barberio ◽  
Matteo Ghisa ◽  
Mentore Ribolsi ◽  
Vincenzo Savarino ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Esophageal Cancer ◽  
Clinical Symptoms ◽  
Survival Rates ◽  
Screening Strategy ◽  
Cancer Surveillance ◽  
Blood Biomarkers ◽  
Early Esophageal Cancer ◽  
Novel Technologies ◽  
Bolus Impaction

Esophageal cancer (EC) is the seventh most common cancer and the sixth cause of cancer death worldwide. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) account for up to 90% and 20% of all ECs, respectively. Clinical symptoms such as dysphagia, odynophagia, and bolus impaction occur late in the natural history of the disease, and the diagnosis is often delayed. The prognosis of ESCC and EAC is poor in advanced stages, being survival rates less than 20% at five years. However, when the diagnosis is achieved early, curative treatment is possible, and survival exceeds 80%. For these reasons, mass screening strategies for EC are highly desirable, and several options are currently under investigation. Blood biomarkers offer an inexpensive, non-invasive screening strategy for cancers, and novel technologies have allowed the identification of candidate markers for EC. The esophagus is easily accessible via endoscopy, and endoscopic imaging represents the gold standard for cancer surveillance. However, lesion recognition during endoscopic procedures is hampered by interobserver variability. To fill this gap, artificial intelligence (AI) has recently been explored and provided encouraging results. In this review, we provide a summary of currently available options to achieve early diagnosis of EC, focusing on blood biomarkers, advanced endoscopy, and AI.

Porphyry Copper Discovery Beneath the Valeriano Lithocap, Chile

SEG Discovery ◽  
2016 ◽  
pp. 1-20
Author(s):  
Richard H. Sillitoe ◽  
Claudio Burgoa ◽  
David R. Hopper
Keyword(s):  
Gold Mineralization ◽  
Porphyry Copper ◽  
Northern Chile ◽  
Porphyry Copper Deposits ◽  
Copper Deposits ◽  
Alteration Zones ◽  
Copper Gold ◽  
Drill Holes

ABSTRACT Exploration for porphyry copper deposits beneath barren or poorly mineralized, advanced argillic lithocaps is becoming common­place; however, there have been few discoveries except in cases where the copper ± gold ± molybdenum mineralization has been partly exposed, typically as a result of partial lithocap erosion. At Valeriano, in the high Andes of northern Chile, completely concealed Miocene porphyry copper-gold mineralization was recently discovered beneath a lithocap. Here, the results of the staged drilling program that led to the discovery are summarized, with emphasis on the key geologic, alteration, and mineralization features that provided guidance. The final deep drill holes of the 16-hole program cut well-defined advanced argillic and sericitic alteration zones before entering chalcopyrite ± bornite–bearing, potassic-altered porphyry, with grades of 0.7 to 1.2% Cu equiv, at depths of ~1,000 to >1,800 m.

scholarly journals AI in the headlines: the portrayal of the ethical issues of artificial intelligence in the media

AI & Society ◽  
2020 ◽  
Vol 35 (4) ◽  
pp. 927-936 ◽  
Author(s):  
Leila Ouchchy ◽  
Allen Coin ◽  
Veljko Dubljević
Keyword(s):  
Artificial Intelligence ◽  
Media Coverage ◽  
Public Discourse ◽  
Ethical Issues ◽  
Public Debate ◽  
Daily Lives ◽  
Novel Technologies ◽  
Regulatory Frameworks ◽  
The Social ◽  
The Media

Abstract As artificial intelligence (AI) technologies become increasingly prominent in our daily lives, media coverage of the ethical considerations of these technologies has followed suit. Since previous research has shown that media coverage can drive public discourse about novel technologies, studying how the ethical issues of AI are portrayed in the media may lead to greater insight into the potential ramifications of this public discourse, particularly with regard to development and regulation of AI. This paper expands upon previous research by systematically analyzing and categorizing the media portrayal of the ethical issues of AI to better understand how media coverage of these issues may shape public debate about AI. Our results suggest that the media has a fairly realistic and practical focus in its coverage of the ethics of AI, but that the coverage is still shallow. A multifaceted approach to handling the social, ethical and policy issues of AI technology is needed, including increasing the accessibility of correct information to the public in the form of fact sheets and ethical value statements on trusted webpages (e.g., government agencies), collaboration and inclusion of ethics and AI experts in both research and public debate, and consistent government policies or regulatory frameworks for AI technology.

scholarly journals Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity (Preprint)

2020 ◽  
Author(s):  
Hassane Alami ◽  
Pascale Lehoux ◽  
Yannick Auclair ◽  
Michèle de Guise ◽  
Marie-Pierre Gagnon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Technology Assessment ◽  
Health System ◽  
Technology Assessment ◽  
Real World ◽  
Health Sector ◽  
Health Technology ◽  
System Transformation ◽  
Novel Technologies ◽  
Technical Performance

UNSTRUCTURED Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI’s value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.

Reflection seismic surveys to site the Drilling the Ivrea Verbano zonE (DIVE) proposed drill-holes, Val Sesia and Val d’Ossola, Italy.

2020 ◽  
Author(s):  
Andrew Greenwood ◽  
Ludovic Baron ◽  
Yu Liu ◽  
György Hetényi ◽  
Klaus Holliger ◽  
...  
Keyword(s):  
Continental Crust ◽  
Transition Zone ◽  
Lower Crust ◽  
Seismic Reflection ◽  
Spatial Scales ◽  
Seismic Reflection Data ◽  
Seismic Surveys ◽  
Crustal Rocks ◽  
Drill Holes ◽  
Lower Crustal

<p>The Ivrea-Verbano Zone in the Italian Alps represents one of the most complete and best-studied cross-sections of the continental crust. Here, geological and geophysical observations indicate the presence of the Moho transition zone at shallow depth, possibly as shallow as 3 km in the location of Balmuccia in Val Sesia. Correspondingly, the Ivrea-Verbano Zone is a primary target for assembling data on the deep continental crust as well as for testing several hypotheses regarding its formation and evolution.</p><p>            Within the context of a project submitted to the International Continental Scientific Drilling Program (ICDP), the Drilling the Ivrea-Verbano zonE (DIVE) team proposes to establish three drill holes across pertinent structures within the Ivrea-Verbano Zone. Two of the planned drill holes, each with a length of ~1000 m, are within Val d’Ossola and target the Pre-Permian lower and upper section of the lower crust. The third proposed drill hole, with a length of ~4000 m, is targeting the lower most crust of the Permian magmatic system of the Ivrea-Verbano Zone in the Val Sesia, close to the Insubric Line. Combined, the three drill holes will compose a complete section of the lower crust and the Moho transition zone, and will reveal the associated structural and composition characteristics at different scales.</p><p>To bridge across the range of spatial scales and to support the drilling proposal, we have carried out active seismic surveys using an EnviroVibe source in the Val d’Ossola. These surveys combined 2D transects (in-line) with the simultaneous collection of short cross-lines, and spatially varied source points, to collect sparse 3D data with a preferential CMP coverage across strike. This survey geometry was largely controlled by environmental considerations and access for the vibrator. Accordingly, 2D profiles, both in-line and cross-line, have been processed using crooked-line geometries, which include CMPs from the 3D infill.</p><p>The very high acoustic impedance contrast of the Quaternary valley infill sediments with respect to the predominant metapelitic and gabbroic lower crustal rocks, as well as the highly attenuative nature of the sediments, were both beneficial and problematic. The former enables mapping of the valley structure, while the latter largely prevents the detection of low-amplitude reflections from within the underlying lower crustal rocks.</p><p>Here, we present the latest results of these seismic reflection surveys and discuss the observations with respect to the prevailing structure and the planning of the drilling operations. Beyond the specific objectives pursued in this study, our results have important implications with regard to the acquisition and processing of high-resolution seismic reflection data in crystalline terranes and their capacity for resolving complex, steeply dipping structures.</p>

scholarly journals Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites

2020 ◽  
Author(s):  
Jakob Johann Assmann ◽  
Isla Heather Myers-Smith ◽  
Jeff Kerby ◽  
Andrew M. Cunliffe ◽  
Gergana N. Daskalova
Keyword(s):  
Vegetation Index ◽  
Leaf Growth ◽  
Spatial Scales ◽  
Yukon Territory ◽  
Ecological Impacts ◽  
The Arctic ◽  
Landsat 8 ◽  
Novel Technologies ◽  
Tundra Ecosystem ◽  
Normalised Difference Vegetation Index

Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman’s ⍴ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2 - 63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10 – 30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman’s ⍴ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.

scholarly journals PROSPECTS OF NEWLY DISCOVERED UGUR AREA IN THE NORTHWEST OF THE GEDABEY ORE DISTRICT (LESSER CAUCASUS, AZERBAIJAN)

2021 ◽  
pp. 53-63
Author(s):  
N. Imamverdiyev ◽  
V. Baba-zadeh ◽  
S. Mursalov ◽  
A. Valiyev ◽  
M. Mansurov ◽  
...  
Keyword(s):  
Gold Mineralization ◽  
Gold Silver ◽  
Rock Chip ◽  
Hydrous Ferric Oxides ◽  
Lesser Caucasus ◽  
Exploration Area ◽  
Drill Holes ◽  
Positive Results ◽  
East West ◽  
Ore District

The article describes Ugur exploration area located in Gedabey Ore District of the Lesser Caucasus in NW of Azerbaijan. Results of trenches and channels sampling on the surface, RC bore holes and summary of significant drill intercepts (>0.29 ppm Au) of Ugur Exploration area are presented. It has been established that The deposit is enlarged by highly gold-silver result of surface outcrop rock chip samples over an area of 2.5 kms North-South by 2 kms East-West, with the Reza gold deposit located in the central part. Out of metallic minerals crystalline hematite was observed. On surface intensive barite and barite-hematite vein and veinlets, also gossan zones were observed. The main mineralization zones have been sampled in three trenches at a distance up to 270 m by trenches #1, #2 and #3 and received positive results for gold and silver. Also there have taken approximately 550 samples from outcrop #1 and #2. On the main orebody at surface centre there occured secondary quartzites with vein-veinlets barite-hematite mineralization over which there remain accumulations of hydrous ferric oxides cementing breccias of quartz and quartzites. And in erosion parts "reddish mass" being oxidation product of stock and stockverk hematite ores were observed. Representing typical gossans, these accumulations by the data of trenches for thickness about 5-10 m contain gold 0.3-2.0 ppm and silver 1.0-15.0 ppm. Ten diamond drill holes, named UGDD 01-10 were drilled in the center part of the deposit. The drill holes were sampled mainly in 1 meter lengths from the top of the hole to the bottom. The core samples were marked and placed into standard boxes. Significant intervals of weighted averages greater than 0.29 ppm over down hole intervals of 1 metres or greater (>0.29 ppm Au and >0.9 m) are summarized in table 3 below. In conclusion, the outcropping alteration at the deposit is typical of the upper steam-heated levels of high-sulfidation epithermal (HSE) deposits, which in most mineralized systems of this type, may cap higher-grade gold mineralization which is hosted by underlying vuggy and oxide zones.

scholarly journals Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

2021 ◽  
Vol 8 ◽  
Author(s):  
Frank Ursin ◽  
Cristian Timmermann ◽  
Marcin Orzechowski ◽  
Florian Steger
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Informed Consent ◽  
Care Setting ◽  
Primary Care Setting ◽  
Information Process ◽  
Novel Technologies ◽  
Ethical Implications ◽  
Aided Diagnosis

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

scholarly journals A Study on Optimistic need of Artificial Intelligence in Fashion Retail Industry

2020 ◽  
Vol 06 (09) ◽  
pp. 211-214
Author(s):  
Niveditha A S
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Structural Change ◽  
Economic Factors ◽  
Body Type ◽  
Average Order ◽  
The Past ◽  
Regional Trends ◽  
Purchase Rate ◽  
The Right

The fashion e-commerce market has been growing steadily in the past few years accounting for USD 371 billion or 21% retail sales of apparel and footwear globally in 2019. But as most of the worlds are experiencing self-isolation and lockdown measures, the corona virus crisis is pushing brands to digitalize even faster to survive, engage with customers, designers, manufactures and redesign their supply chain operations. Many sectors are reeling from the fallout of the COVID-19 pandemic as they stare into the abyss of the impending recession and fashion has not been immune. But aside from economic factors, the industry is also facing lasting structural change. Artificial Intelligence optimizes conversion, Average Order Value (AOV) and repeat purchase rate by understanding a customer’s preferences and suggesting the right products and outfits for them. Recommendations are tailored to the physical stores with latest technologies by implementing virtual trail room, regional trends, as well as the customers’ body type, color, desired occasions and personal style.

Postglacial rebound and the focal mechanisms of eastern Canadian earthquakes

1984 ◽  
Vol 21 (9) ◽  
pp. 1018-1023 ◽  
Author(s):  
Garry Quinlan
Keyword(s):  
Numerical Model ◽  
Temporal Evolution ◽  
Canadian Arctic ◽  
Focal Mechanisms ◽  
Thin Elastic Plate ◽  
Baffin Island ◽  
Postglacial Rebound ◽  
Regional Trends ◽  
Uplift History ◽  
Lithospheric Stress

A detailed numerical model of postglacial rebound for the eastern Canadian Arctic is described with special attention being paid to Baffin Island. This numerical model is able to account for the observed uplift patterns as recorded by the relative sea-level histories of discrete sites distributed throughout the study area. These uplift patterns show regional trends, so the same model can be used to interpolate between data sites and to estimate the uplift history at any arbitrary site within the study area.By treating the lithosphere as a thin elastic plate, spatial variations in the uplift pattern can be translated into estimates of lithospheric stress. The model predicts the variations in uplift as a function of both space and time since deglaciation and can therefore be used to estimate the temporal evolution of lithospheric stress following deglaciation.The stress so calculated is treated as a perturbation to an ambient stress field having its origin in other processes. Postglacial rebound is shown to be capable of triggering earthquakes in prestressed regions but rarely capable of dictating the focal mechanisms of these earthquakes.

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