A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

Zhou, Qianwei; Tong, Guanjun; Xie, Dizhi; Li, Baoqing

doi:10.1109/lsp.2012.2209870

Cited by 24 publications

(13 citation statements)

References 5 publications

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“…In the literature, the use of the cepstral domain has provided a relevant impact in the classification of seismic signals, e.g. [8] and [9]. Due to this fact, attributes in the cepstral domain are also considered in the proposed method.…”

Section: B Description Of the Volcano Classesmentioning

confidence: 99%

“…Moreover, [6] used neural networks to classify earthquakes and quarry blasts, while [7] presented a method for automatic identification of noisy seismic events. Also, new techniques are found in the literature for classifying seismic signals with machine learning models, such as the use of the cepstral domain with support vector machine (SVM) in [8] or with Hidden Markov Model (HMM) in [9]. Besides, a three-channel seismic signal decomposition using wavelets with kernel ridge regression is presented in [10].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Lara

Fernandes

Inza

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral and cepstral domains, extracted from the EMD of the signals, as well as a set of pre-processing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis (PCA) method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine (SVM) providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.

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Section: B Description Of the Volcano Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Lara

Fernandes

Inza

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…As is known to us, the features of seismic target are strongly influenced by two environmental factors which are environmental noise and environmental underlying geology [5]. Therefore, the aim of this section is to verify that WPM of seismic targets is more insensitive to the variations of environment than other traditional time-frequency features by an experimental approach.…”

Section: Wpm Signature Analysismentioning

confidence: 99%

“…In addition, the seismic signal generated by a moving target is characterized by target velocity, target structure, the signal's propagation distance and local underlying geology, etc. [4,5], and is not a simply linear combination of these factors. Therefore, the seismic signal of a moving target is usually considered as a kind of signal with a high-dimensional feature space [4,6,7].…”

Section: Introductionmentioning

confidence: 99%

Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

Huang

Zhou

Zhang

et al. 2013

Sensors

Self Cite

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One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity.

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“…Since the classes are determined before applying the real data, this method is known as a supervised learning algorithm. Classification is used in many fields and sciences such as, image segmentation [1,2], geology [3], robot control [4,5], bio-informatics [6], genetics [8], biology [7] and healthcare [9].…”

Section: Introductionmentioning

confidence: 99%

Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

Nouri

Akbarzadeh²,

Rowhanimanesh³

2014

IJIIS

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Abstract:In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in "Big Data" where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.

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A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

Cited by 24 publications

References 5 publications

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

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