An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events
To improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolution Autoencoder (MDCAE) combined with a Constraint Shape-Based Distance algorithm inco...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7891 |
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| author | Hao Luo Ziyu Liu Song Ge Linlin Ding Li Zhang |
| author_facet | Hao Luo Ziyu Liu Song Ge Linlin Ding Li Zhang |
| author_sort | Hao Luo |
| collection | DOAJ |
| description | To improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolution Autoencoder (MDCAE) combined with a Constraint Shape-Based Distance algorithm incorporating volatility (CSBD-Vol). MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. Experimental results on a microseismic dataset from the 802 working faces of a mining site demonstrate that the CSBD-Vol algorithm significantly outperforms SBD, Shape-Based Distance with volatility (SBD-Vol), and Constraint Shape-Based Distance (CSBD) in classification accuracy, verifying the effectiveness of constrained time windows and volatility in enhancing performance. The proposed clustering algorithm reduces time complexity from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><msup><mi>n</mi><mn>2</mn></msup><mo>)</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mo form="prefix">log</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula>, achieving substantial improvements in computational efficiency. Furthermore, the MDCAE-CSBD-Vol approach achieves 87% accuracy in microseismic time-series waveform classification. These findings highlight that MDCAE-CSBD-Vol offers a novel, precise, and efficient solution for detecting anomalous events in microseismic systems, providing valuable support for accurate and high-efficiency monitoring in mining and related applications. |
| format | Article |
| id | doaj-art-7299dfdab5a945cd91aa58e3c00733ce |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7299dfdab5a945cd91aa58e3c00733ce2025-08-20T03:58:30ZengMDPI AGApplied Sciences2076-34172025-07-011514789110.3390/app15147891An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining EventsHao Luo0Ziyu Liu1Song Ge2Linlin Ding3Li Zhang4College of Information, Liaoning University, Shenyang 110036, ChinaCollege of Information, Liaoning University, Shenyang 110036, ChinaCollege of Information, Liaoning University, Shenyang 110036, ChinaCollege of Information, Liaoning University, Shenyang 110036, ChinaCollege of Information, Liaoning University, Shenyang 110036, ChinaTo improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolution Autoencoder (MDCAE) combined with a Constraint Shape-Based Distance algorithm incorporating volatility (CSBD-Vol). MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. Experimental results on a microseismic dataset from the 802 working faces of a mining site demonstrate that the CSBD-Vol algorithm significantly outperforms SBD, Shape-Based Distance with volatility (SBD-Vol), and Constraint Shape-Based Distance (CSBD) in classification accuracy, verifying the effectiveness of constrained time windows and volatility in enhancing performance. The proposed clustering algorithm reduces time complexity from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><msup><mi>n</mi><mn>2</mn></msup><mo>)</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mo form="prefix">log</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula>, achieving substantial improvements in computational efficiency. Furthermore, the MDCAE-CSBD-Vol approach achieves 87% accuracy in microseismic time-series waveform classification. These findings highlight that MDCAE-CSBD-Vol offers a novel, precise, and efficient solution for detecting anomalous events in microseismic systems, providing valuable support for accurate and high-efficiency monitoring in mining and related applications.https://www.mdpi.com/2076-3417/15/14/7891microseismic event time seriesmulti-scale fusion convolutionfeature extractionvolatilityshape similarity algorithmunsupervised clustering |
| spellingShingle | Hao Luo Ziyu Liu Song Ge Linlin Ding Li Zhang An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events Applied Sciences microseismic event time series multi-scale fusion convolution feature extraction volatility shape similarity algorithm unsupervised clustering |
| title | An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events |
| title_full | An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events |
| title_fullStr | An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events |
| title_full_unstemmed | An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events |
| title_short | An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events |
| title_sort | algorithm for the shape based distance of microseismic time series waveforms and its application in clustering mining events |
| topic | microseismic event time series multi-scale fusion convolution feature extraction volatility shape similarity algorithm unsupervised clustering |
| url | https://www.mdpi.com/2076-3417/15/14/7891 |
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