Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection
Rainfall-triggered landslides are one of the most deadly natural hazards in many regions. Seismic recordings have been used to examine source mechanisms and to develop monitoring systems of landslides. We present a semiautomatic algorithm for detecting and locating landslide events using both broadb...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2019/1426019 |
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| author | En-Jui Lee Wu-Yu Liao Guan-Wei Lin Po Chen Dawei Mu Ching-Weei Lin |
| author_facet | En-Jui Lee Wu-Yu Liao Guan-Wei Lin Po Chen Dawei Mu Ching-Weei Lin |
| author_sort | En-Jui Lee |
| collection | DOAJ |
| description | Rainfall-triggered landslides are one of the most deadly natural hazards in many regions. Seismic recordings have been used to examine source mechanisms and to develop monitoring systems of landslides. We present a semiautomatic algorithm for detecting and locating landslide events using both broadband and short-period recordings and have successfully applied our system to landslides in Taiwan. Compared to local earthquake recordings, the recordings of landslides usually show longer durations and lack distinctive P and S wave arrivals; therefore, the back projection method is adopted for the landslide detection and location. To identify the potential landslide events, the seismic recordings are band-passed from 1 to 3 Hz and the spectrum pattern in the time-frequency domain is used to distinguish landslides from other types of seismic sources based upon carefully selected empirical criteria. Satellite images before and after the detected and located landslide events are used for final confirmation. Our landslide detection and spatial-temporal location system could potentially benefit the establishment of rainfall-triggered landslide forecast models and provide more reliable constraints for physics-based landslide modeling. The accumulating seismic recordings of landslide events could be used as a training dataset for machine learning techniques, which will allow us to fully automate our system in the near future. |
| format | Article |
| id | doaj-art-9993a2a97a2a4f4da3d75b975227e758 |
| institution | OA Journals |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-9993a2a97a2a4f4da3d75b975227e7582025-08-20T02:18:24ZengWileyGeofluids1468-81151468-81232019-01-01201910.1155/2019/14260191426019Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back ProjectionEn-Jui Lee0Wu-Yu Liao1Guan-Wei Lin2Po Chen3Dawei Mu4Ching-Weei Lin5Department of Earth Sciences, National Cheng-Kung University, No. 1, University Rd., East Dist., Tainan City 70101, TaiwanDepartment of Earth Sciences, National Cheng-Kung University, No. 1, University Rd., East Dist., Tainan City 70101, TaiwanDepartment of Earth Sciences, National Cheng-Kung University, No. 1, University Rd., East Dist., Tainan City 70101, TaiwanDepartment of Geology and Geophysics, University of Wyoming, Dept. 3006, 1000 E University Ave, Laramie, WY 82071, USASan Diego Supercomputer Center, University of California, San Diego, 10100 Hopkins Drive, La Jolla, CA 92093, USADepartment of Earth Sciences, National Cheng-Kung University, No. 1, University Rd., East Dist., Tainan City 70101, TaiwanRainfall-triggered landslides are one of the most deadly natural hazards in many regions. Seismic recordings have been used to examine source mechanisms and to develop monitoring systems of landslides. We present a semiautomatic algorithm for detecting and locating landslide events using both broadband and short-period recordings and have successfully applied our system to landslides in Taiwan. Compared to local earthquake recordings, the recordings of landslides usually show longer durations and lack distinctive P and S wave arrivals; therefore, the back projection method is adopted for the landslide detection and location. To identify the potential landslide events, the seismic recordings are band-passed from 1 to 3 Hz and the spectrum pattern in the time-frequency domain is used to distinguish landslides from other types of seismic sources based upon carefully selected empirical criteria. Satellite images before and after the detected and located landslide events are used for final confirmation. Our landslide detection and spatial-temporal location system could potentially benefit the establishment of rainfall-triggered landslide forecast models and provide more reliable constraints for physics-based landslide modeling. The accumulating seismic recordings of landslide events could be used as a training dataset for machine learning techniques, which will allow us to fully automate our system in the near future.http://dx.doi.org/10.1155/2019/1426019 |
| spellingShingle | En-Jui Lee Wu-Yu Liao Guan-Wei Lin Po Chen Dawei Mu Ching-Weei Lin Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection Geofluids |
| title | Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection |
| title_full | Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection |
| title_fullStr | Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection |
| title_full_unstemmed | Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection |
| title_short | Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection |
| title_sort | towards automated real time detection and location of large scale landslides through seismic waveform back projection |
| url | http://dx.doi.org/10.1155/2019/1426019 |
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