Classifying Copper-Molybdenum-Gold Porphyry Deposit Alteration using Magnetic and Spectral data

2021 ◽  
Author(s):  
Oliver Dixon ◽  
William McCarthy ◽  
Nasser Madani ◽  
Michael Petronis ◽  
Steve McRobbie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Susceptibility ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Copper Production ◽  
Magnetic Data ◽  
Machine Learning Algorithm ◽  
Porphyry Deposit ◽  
Remanent Magnetisation ◽  
Magnetic Remanence

<p>Copper is one of the most important critical metal resources needed to achieve carbon neutrality with a projected increase in demand of >300% over the next half century from electronics and renewables.  Porphyry deposits account for most of the global copper production, but the discovery of new reserves is ever more challenging. Machine learning presents an opportunity to cross reference new and traditionally under-utilised data sets with a view to developing quantitative predictive models of hydrothermal alteration zones to guide new, ambitious exploration programs.</p><p>The aim of this study is to demonstrate a new alteration classification scheme driven by quantitative magnetic and spectral data to feed a machine learning algorithm. The benefits of an alteration model based on quantitative data rather than subjective observations by geologists, are that there is no bias in the data collected, the arising model is quantifiable and therefore easy to model and the process be fully automated. Ultimately, this approach aids more detailed exploration and mine modelling, in turn, reducing the extraction process carbon footprint and more effectively identifying new deposits.</p><p>Presented here are magnetic susceptibility and shortwave infrared (SWIR) data collected from the KazMinerals plc. owned Aktogay Cu-Mo giant porphyry deposit, eastern Kazakhstan, which has a throughput of 30Mtpa of ore. These data are cross referenced using a newly developed machine learning algorithm. Generated autonomously, our results reveal twelve statistically and geologically significant clusters that define a new alteration classification for porphyry style mineralisation. Results are entirely non-subjective, reproducible, quantitative and modellable.</p><p>Importantly, magnetic susceptibility measurements improve the algorithm’s ability to identify clusters by between 29-36%; enhancing the sophistication of the included magnetic data promises to yield substantially better statistical results. Magnetic remanence data are therefore being complied on representative samples from each of the twelve identified clusters, including hysteresis, isothermal remanent magnetisation (IRM) acquisition, FORC measurements, natural remanent magnetisation (NRM) and anhysteretic remanent magnetisation (ARM). Through collaboration with industry partners, we aim to develop an automated means of collecting these magnetic remanence data to accompany the machine learning algorithm.</p>

scholarly journals Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4299 ◽  
Author(s):  
Eui Jung Moon ◽  
Youngsik Kim ◽  
Yu Xu ◽  
Yeul Na ◽  
Amato J. Giaccia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Spectral Response ◽  
Machine Learning Algorithm ◽  
Simple Method ◽  
Machine Learning Model ◽  
Multiple Devices ◽  
Food Freshness

There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.

Non-Destructive Evaluation of Salmon and Tuna Freshness in a Room-Temperature Incubation Environment Using a Portable Visible/Near-Infrared Imaging Spectrometer

2021 ◽  
Vol 64 (2) ◽  
pp. 521-527
Author(s):  
Jinshi Cui ◽  
Chengxun Cui
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Spectral Response ◽  
List Type ◽  
Machine Learning Algorithm ◽  
Imaging Spectrometer ◽  
Nir Imaging ◽  
Food Freshness

HighlightsWhile freshness is a critical value of food quality, its assessment requires complex methods, which are costly and time-consuming.In this work, it is demonstrated that spectral responses obtained from a portable VIS/NIR imaging spectrometer can be used to predict food freshness using a CNN-based machine learning algorithm.In the food industry, the method can assess real-time food freshness nondestructively and cost-effectively.Abstract. There has been strong demand for the development of accurate but simple methods to assess the freshness of foods. In this study, a system is proposed to determine the freshness of fish by analyzing the spectral response with a portable visible/near-infrared (VIS/NIR) imaging spectrometer and a convolution neural network (CNN) machine learning algorithm. Spectral response data from salmon and tuna, which were incubated at 25°C, were obtained every minute for 30 h and were categorized into three stages (fresh, likely spoiled, or spoiled) based on the time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of the experimental samples. The accuracy of the spectral data in predicting the freshness was ~84% for salmon and ~88% for tuna. Keywords: CNN, Fish, Freshness, pH, Spectral data, VIS/NIR.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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