Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone
Abstract Tectonic tremors are key indicators of slow-slip phenomena, and detecting them accurately is a challenging task. Conventional techniques often fail to detect tremors during periods of intense tremor activity. We present here a deep learning approach for detecting and locating tremors in the...
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| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Earth, Planets and Space |
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| Online Access: | https://doi.org/10.1186/s40623-025-02257-y |
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| author | Yuya Jinde Amane Sugii Yoshihiro Hiramatsu |
| author_facet | Yuya Jinde Amane Sugii Yoshihiro Hiramatsu |
| author_sort | Yuya Jinde |
| collection | DOAJ |
| description | Abstract Tectonic tremors are key indicators of slow-slip phenomena, and detecting them accurately is a challenging task. Conventional techniques often fail to detect tremors during periods of intense tremor activity. We present here a deep learning approach for detecting and locating tremors in the Nankai subduction zone that is more effective than conventional techniques. We utilized two convolutional neural networks (CNNs): a CNN for classification of seismic waveforms into noise, tremors, or earthquakes, and a CNN for regression prediction of tremor epicenters from amplitude data. The accuracy, recall, and precision of the CNN for classification all exceeded 95%. The CNN for regression used ensemble predictions to produce estimates of tremor locations with a median error of 3.2 km. When this approach was applied to continuous data, it successfully mapped key features of tremor activity and improved the detection and location of tremors, especially during high-activity periods. Graphical Abstract |
| format | Article |
| id | doaj-art-8093611111ad484f82f8bd007b2aa45d |
| institution | DOAJ |
| issn | 1880-5981 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Earth, Planets and Space |
| spelling | doaj-art-8093611111ad484f82f8bd007b2aa45d2025-08-20T03:04:26ZengSpringerOpenEarth, Planets and Space1880-59812025-07-0177111210.1186/s40623-025-02257-yEnhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zoneYuya Jinde0Amane Sugii1Yoshihiro Hiramatsu2Graduate School of Natural Science and Technology, Kanazawa UniversityGraduate School of Natural Science and Technology, Kanazawa UniversityFaculty of Geosciences and Civil Engineering, Institute of Science and Engineering, Kanazawa UniversityAbstract Tectonic tremors are key indicators of slow-slip phenomena, and detecting them accurately is a challenging task. Conventional techniques often fail to detect tremors during periods of intense tremor activity. We present here a deep learning approach for detecting and locating tremors in the Nankai subduction zone that is more effective than conventional techniques. We utilized two convolutional neural networks (CNNs): a CNN for classification of seismic waveforms into noise, tremors, or earthquakes, and a CNN for regression prediction of tremor epicenters from amplitude data. The accuracy, recall, and precision of the CNN for classification all exceeded 95%. The CNN for regression used ensemble predictions to produce estimates of tremor locations with a median error of 3.2 km. When this approach was applied to continuous data, it successfully mapped key features of tremor activity and improved the detection and location of tremors, especially during high-activity periods. Graphical Abstracthttps://doi.org/10.1186/s40623-025-02257-yConvolutional neural networkSlow slipTremor migrationRapid tremor reversal |
| spellingShingle | Yuya Jinde Amane Sugii Yoshihiro Hiramatsu Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone Earth, Planets and Space Convolutional neural network Slow slip Tremor migration Rapid tremor reversal |
| title | Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone |
| title_full | Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone |
| title_fullStr | Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone |
| title_full_unstemmed | Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone |
| title_short | Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone |
| title_sort | enhanced deep learning approach for detecting and locating tectonic tremors in the nankai subduction zone |
| topic | Convolutional neural network Slow slip Tremor migration Rapid tremor reversal |
| url | https://doi.org/10.1186/s40623-025-02257-y |
| work_keys_str_mv | AT yuyajinde enhanceddeeplearningapproachfordetectingandlocatingtectonictremorsinthenankaisubductionzone AT amanesugii enhanceddeeplearningapproachfordetectingandlocatingtectonictremorsinthenankaisubductionzone AT yoshihirohiramatsu enhanceddeeplearningapproachfordetectingandlocatingtectonictremorsinthenankaisubductionzone |