Seismic characterization of a Triassic-Jurassic deep geothermal sandstone reservoir, onshore Denmark, using unsupervised machine learning techniques

2021 ◽  
pp. 1-48
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Kenneth Bredesen ◽  
Thang Ha ◽  
Kurt J. Marfurt
Keyword(s):  
Machine Learning ◽  
Bayesian Framework ◽  
Green Energy ◽  
Machine Learning Techniques ◽  
Generative Topographic Mapping ◽  
Reservoir Properties ◽  
Facies Classification ◽  
Learning Techniques ◽  
Sandstone Reservoir

The Triassic-Jurassic deep sandstone reservoirs in onshore Denmark are known geothermal targets that can be exploited for sustainable and green energy for the next several decades. The economic development of such resources requires accurate characterization of the sandstone reservoir properties, namely, volume of clay, porosity, and permeability. The classic approach to achieving such objectives has been to integrate prestack seismic data and well logs with geologic information to obtain facies and reservoir property predictions in a Bayesian framework. Using this prestack inversion approach, we can obtain superior spatial and temporal variations within the target formation. We then examine whether unsupervised facies classification in the target units can provide additional information. We evaluated several machine learning techniques and find that generative topographic mapping further subdivided intervals mapped by the Bayesian framework into additional subunits.

scholarly journals Delving into Android Malware Families with a Novel Neural Projection Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Rafael Vega Vega ◽  
Héctor Quintián ◽  
Carlos Cambra ◽  
Nuño Basurto ◽  
Álvaro Herrero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Projection Method ◽  
Hebbian Learning ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Learning Techniques ◽  
First Time

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

scholarly journals Characterization of clot composition in acute cerebral infarct using machine learning techniques

2019 ◽  
Vol 6 (4) ◽  
pp. 739-747 ◽  
Author(s):  
Jong‐Won Chung ◽  
Yoon‐Chul Kim ◽  
Jihoon Cha ◽  
Eun‐Hyeok Choi ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Infarct ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Acute Cerebral Infarct

scholarly journals Seismic facies analysis using machine learning

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. O83-O95 ◽  
Author(s):  
Thilo Wrona ◽  
Indranil Pan ◽  
Robert L. Gawthorpe ◽  
Haakon Fossen
Keyword(s):  
Machine Learning ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Seismic Section ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Facies Classification ◽  
Learning Techniques ◽  
Seismic Facies Analysis ◽  
Northern North Sea

Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We are convinced that machine-learning techniques can help address these problems by performing seismic facies analyses in a rigorous, repeatable way. For this purpose, we use state-of-the-art 3D broadband seismic reflection data of the northern North Sea. Our workflow includes five basic steps. First, we extract seismic attributes to highlight features in the data. Second, we perform a manual seismic facies classification on 10,000 examples. Third, we use some of these examples to train a range of models to predict seismic facies. Fourth, we analyze the performance of these models on the remaining examples. Fifth, we select the “best” model (i.e., highest accuracy) and apply it to a seismic section. As such, we highlight that machine-learning techniques can increase the efficiency of seismic facies analyses.

Machine learning distributions of quantum ansatz with hierarchical structure

2020 ◽  
Vol 34 (20) ◽  
pp. 2050196
Author(s):  
Haozhen Situ ◽  
Zhimin He
Keyword(s):  
Machine Learning ◽  
Quantum Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Variational Autoencoder ◽  
Systems Learning ◽  
Near Term ◽  
Learning Measurement

Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.

Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques

2020 ◽  
Vol 246 (6) ◽  
pp. 1193-1205 ◽  
Author(s):  
Nattane Luíza da Costa ◽  
Joao Paulo Bianchi Ximenez ◽  
Jairo Lisboa Rodrigues ◽  
Fernando Barbosa ◽  
Rommel Barbosa
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Cabernet Sauvignon ◽  
Ms Analysis ◽  
Learning Techniques ◽  
Icp Ms

scholarly journals Exploiting the Symmetry of Integral Transforms for Featuring Anuran Calls

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 405 ◽  
Author(s):  
Amalia Luque ◽  
Jesús Gómez-Bellido ◽  
Alejandro Carrasco ◽  
Julio Barbancho
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Discrete Cosine Transform ◽  
Integral Transforms ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Cepstral Coefficients

The application of machine learning techniques to sound signals requires the previous characterization of said signals. In many cases, their description is made using cepstral coefficients that represent the sound spectra. In this paper, the performance in obtaining cepstral coefficients by two integral transforms, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT), are compared in the context of processing anuran calls. Due to the symmetry of sound spectra, it is shown that DCT clearly outperforms DFT, and decreases the error representing the spectrum by more than 30%. Additionally, it is demonstrated that DCT-based cepstral coefficients are less correlated than their DFT-based counterparts, which leads to a significant advantage for DCT-based cepstral coefficients if these features are later used in classification algorithms. Since the DCT superiority is based on the symmetry of sound spectra and not on any intrinsic advantage of the algorithm, the conclusions of this research can definitely be extrapolated to include any sound signal.

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 Fast Characterization of Input-Output Behavior of Non-Charge-Based Logic Devices by Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1381
Author(s):  
Arun Kaintura ◽  
Kyle Foss ◽  
Odysseas Zografos ◽  
Ivo Couckuyt ◽  
Adrien Vaysset ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adaptive Sampling ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Input Output ◽  
Micromagnetic Simulations ◽  
Logic Devices ◽  
Learning Techniques ◽  
Output Behavior

Non-charge-based logic devices are promising candidates for the replacement of conventional complementary metal-oxide semiconductors (CMOS) devices. These devices utilize magnetic properties to store or process information making them power efficient. Traditionally, to fully characterize the input-output behavior of these devices a large number of micromagnetic simulations are required, which makes the process computationally expensive. Machine learning techniques have been shown to dramatically decrease the computational requirements of many complex problems. We use state-of-the-art data-efficient machine learning techniques to expedite the characterization of their behavior. Several intelligent sampling strategies are combined with machine learning (binary and multi-class) classification models. These techniques are applied to a magnetic logic device that utilizes direct exchange interaction between two distinct regions containing a bistable canted magnetization configuration. Three classifiers were developed with various adaptive sampling techniques in order to capture the input-output behavior of this device. By adopting an adaptive sampling strategy, it is shown that prediction accuracy can approach that of full grid sampling while using only a small training set of micromagnetic simulations. Comparing model predictions to a grid-based approach on two separate cases, the best performing machine learning model accurately predicts 99.92% of the dense test grid while utilizing only 2.36% of the training data respectively.

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