Dataset Information

Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification.

ABSTRACT: This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the Observatorio Vulcanológico de los Andes Sur (OVDAS) classified them into four class according to their event source: i) Volcano-Tectonic (VT); ii) Long Period (LP); iii) Tremor (TR), and iv) Tectonic (TC). The dataset is composed of 3592 signals separated by class and filtered to select the segment that contains the most representative part of the seismic event. This dataset is important to support researchers interested in studying seismic signals from active volcanoes and developing new methods to model time-dependent data. In this sense, we have published the manuscript "In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification" [1] analyzing such signals with different Deep Neural Networks (DNN). The main contribution of such manuscript is a new DNN architecture called SeismicNet, which provided classification results among the best in the literature without demanding explicit signal pre-processing steps. Therefore, the reader is referred to such manuscript for the interpretation of the data.

SUBMITTER: Canario JP

PROVIDER: S-EPMC7206203 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification.

Canário João Paulo JP de Mello Rodrigo Fernandes RF Curilem Millaray M Huenupan Fernando F Rios Ricardo Araujo RA

Data in brief 20200430

This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the Observatorio Vulcanológico de los Andes Sur (OVDAS) classified them into four class according to their event source: i) Volcano-Tectonic (VT); ii) Long Period (LP); iii) Tremor (TR), and iv) Tectonic ( ...[more]

PMID: 32395588

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Project description:BackgroundDeep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as transcriptomes and metabolomes, have long existed as a valuable resource for facilitating diagnosis of patient statuses/stages. It is timely imperative to compare deep learning neural networks against classical machine learning methods in the setting of matrix-formed omics data in terms of classification accuracy and robustness.ResultsUsing 37 high throughput omics datasets, covering transcriptomes and metabolomes, we evaluated the classification power of deep learning compared to traditional machine learning methods. Representative deep learning methods, Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN), were deployed and explored in seeking optimal architectures for the best classification performance. Together with five classical supervised classification methods (Linear Discriminant Analysis, Multinomial Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machine), MLP and CNN were comparatively tested on the 37 datasets to predict disease stages or to discriminate diseased samples from normal samples. MLPs achieved the highest overall accuracy among all methods tested. More thorough analyses revealed that single hidden layer MLPs with ample hidden units outperformed deeper MLPs. Furthermore, MLP was one of the most robust methods against imbalanced class composition and inaccurate class labels.ConclusionOur results concluded that shallow MLPs (of one or two hidden layers) with ample hidden neurons are sufficient to achieve superior and robust classification performance in exploiting numerical matrix-formed omics data for diagnosis purpose. Specific observations regarding optimal network width, class imbalance tolerance, and inaccurate labeling tolerance will inform future improvement of neural network applications on functional genomics data.

Dataset Information

Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification.

Publications

Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification.

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