Automations in Chemostratigraphy: Toward Robust Chemical Data Analysis and Interpretation

2021 ◽  
Author(s):  
Nikolaos A. Michael ◽  
Christian Scheibe ◽  
Neil W. Craigie
Keyword(s):  
Machine Learning ◽  
Function Analysis ◽  
Analytical Techniques ◽  
Machine Learning Techniques ◽  
Geochemical Data ◽  
Correlation Technique ◽  
Stratigraphic Correlation ◽  
Geological Processes ◽  
Early Results ◽  
Geological Formations

Abstract Elemental chemostratigraphy has become an established stratigraphic correlation technique over the last 15 years. Geochemical data are generated from rock samples (e.g., ditch cuttings, cores or hand specimens) for up to c. 50 elements in the range Na-U in the periodic table using various analytical techniques. The data are commonly displayed and interpreted as ratios, indices and proxy values in profile form against depth. The large number of possible combinations between the determined elements (more than a thousand combinations), makes it a time-consuming effort to identify meaningful variations that resulted in correlative chemostratigraphic boundaries and zones between wells. The large number of combination means that 30-40% of the information is not used for the correlations that maybe crucial to understand the geological processes. Automation and artificial intelligence (AI) are envisaged as likely solutions to this challenge. Statistical and machine learning techniques are tested as a first step to automate and establish a workflow to define (chemo-) stratigraphic boundaries, and to identify geological formations. The workflow commences with a quality check of the input data and then with principle component analysis (PCA) as a multivariate statistical method. PCA is used to minimize the number of elements/ratios plotted in profile form, whilst simultaneously identifying multidimensional relationships between them. A statistical boundary picking method is then applied define chemostratigraphic zones, for which reliability is determined utilizing quartile analysis, which tests the overlap of chemical signals across these statistical boundaries. Machine learning via discriminant function analysis (DFA) has been developed to predict the placement of correlative boundaries between adjacent sections/wells. The proposed workflow has been tested on various geological formations and areas in Saudi Arabia. The chemostratigraphic correlations proposed using this workflow broadly correspond to those defined in the standard workflow by experienced chemostratigraphers, while interpretation times and subjectivity are reduced. While machine learning via DFA is currently further researched, early results of the workflow are very encouraging. A user-friendly software application with workflows and algorithms ultimately leading to automation of the processes is under development.

Development of a method for in vivo mechanical characterization of articular cartilage

2017 ◽  
Author(s):  
◽  
Joe Rexwinkle
Keyword(s):  
Machine Learning ◽  
Hospital Setting ◽  
The United States ◽  
Machine Learning Techniques ◽  
Mechanical State ◽  
University Of Missouri ◽  
Orthopaedic Research ◽  
Early Results ◽  
Number Of Patients

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Arthritis is one of the leading causes of disability in the United States and the second most expensive to treat according to the CDC. One of the key difficulties in diagnosing and treating arthritis, in particular osteoarthritis, is that the mechanisms for progression of the disease are poorly characterized. Mechanical engineer Joe Rexwinkle, working with Dr. Ferris Pfeiffer and the Thompson Lab for Regenerative Orthopaedics, aimed to shed some light on the links between cartilage biology and the degradation seen in osteoarthritis. The study began with obtaining cartilage samples from six patients undergoing total knee replacements and collecting information on several biomarkers with known relevance to osteoarthritis. Specifically, the concentrations of several proteins which may be determined in a standard hospital lab were analyzed. The samples were then tested to determine their mechanical properties, since the progression of osteoarthritis is always accompanied by the physical degradation of the tissue. Machine learning techniques, which are gaining increasing popularity in the field of orthopaedic research, were then used to model the relationships between these biomarkers and the mechanical state of the tissue. These models were found to be highly accurate in characterizing the mechanical state of the tissue, even when limited only to the protein concentrations that one could find in a standard hospital lab. This study has not yet produced a tool which may be used in a hospital setting, considering the low number of patients included in this study, but it does reveal promising early results in using machine learning to characterize osteoarthritis, a task which has thus far eluded the orthopaedic research community.

scholarly journals Machine learning techniques to predict daily rainfall amount

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Chalachew Muluken Liyew ◽  
Haileyesus Amsaya Melese
Keyword(s):  
Machine Learning ◽  
Pearson Correlation ◽  
Daily Rainfall ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Correlation Technique ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting

AbstractPredicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. The dataset was collected from the local meteorological office at Bahir Dar City, Ethiopia to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost). Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. The result of the study revealed that the Extreme Gradient Boosting machine learning algorithm performed better than others.

Use of machine learning on SCADA data for asset's prognostics health management

2020 ◽  
Vol 60 (2) ◽  
pp. 602
Author(s):  
Alexandre Cesa ◽  
Elliot Press
Keyword(s):  
Machine Learning ◽  
Health Management ◽  
Analytical Techniques ◽  
Root Cause Analysis ◽  
Machine Learning Techniques ◽  
Safe Operation ◽  
Cause Analysis ◽  
Root Cause ◽  
Learning Techniques ◽  
Learning Frameworks

The timely detection of anomalies in the process industry is paramount to ensure effective and safe operation of plant. There typically exists an abundance of historical data recorded in Supervisory Control and Data Acquisition (SCADA) systems, which is most often used for understanding past events through, for example, root cause analysis. It is envisaged that higher levels of insight could be achieved from the same datasets by utilising more advanced analytical techniques such as machine learning frameworks. This would enable moving from a ‘diagnosis–mitigation’ (i.e. a root cause analysis) paradigm to a more desirable ‘detection–prediction–prognosis–prevention’ paradigm. Machine learning techniques can be used on SCADA data to support the detection of plant anomaly conditions that do not necessary manifest as process alarms for example. We used a Bayesian network framework on the Tennessee Eastman Plant benchmark problem to demonstrate the technique’s capability. Our model proved to be effective in detecting anomalous plant conditions in most situations.

scholarly journals Machine Learning in Volcanology: A Review

2020 ◽  
Author(s):  
Roberto Carniel ◽  
Silvina Raquel Guzmán
Keyword(s):  
Machine Learning ◽  
Volcanic Rocks ◽  
Machine Learning Techniques ◽  
Geochemical Data ◽  
Learning Techniques ◽  
Static Data ◽  
Ideal Situation ◽  
Applications Of Machine Learning ◽  
Different Origins

A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches.

scholarly journals Machine Learning Techniques to Predict Daily Rainfall Amount

2021 ◽  
Author(s):  
Chalachew Muluken Liyew ◽  
Haileyesus Amsaya Melese
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Daily Rainfall ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
Correlation Technique ◽  
Learning Techniques ◽  
Machine Learning Model

Abstract It is crucial to predict the amount of daily rainfall to improve agricultural productivities to secure food, and water quality supply to keep the citizen healthy. To predict rainfall, various researches are conducted using data mining and machine learning techniques of different countries’ environmental datasets. The Pearson correlation technique is used to select relevant environmental variables which are used as an input for the machine learning model of this study. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The dataset is collected from the local meteorological office to measure the performance of three machine learning techniques as Multivariate Linear Regression, Random Forest and Extreme Gradient Boost. Root mean squared error and Mean absolute Error are used to measure the performance of the machine learning model for this study. The result of the study shows that the Extreme Gradient Boost gradient descent machine learning algorithm performs better than others.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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