Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction Article Swipe
Ashok Dahal
,
Hakan Tanyaş
,
Luigi Lombardo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s43247-024-01243-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s43247-024-01243-8
Seismic waves can shake mountainous landscapes, triggering thousands of landslides. Regional-scale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. Such an approach neglects the contribution of ground motion phase and amplitude and their variations over space and time. Here, we address this problem by developing an explainable deep-learning model able to treat the entire wavefield and benchmark it against a model equipped with scalar intensity parameters. The experiments run on the area affected by the 2015 M w 7.8 Gorkha, Nepal earthquake reveal a 16% improvement in predictive capacity when incorporating full waveforms. This improvement is achieved mainly on gentle (~25°) hillslopes exposed to low ground shaking (~0.2 m/s). Moreover, we can largely attribute this improvement to the ground motion before and much after the peak velocity arrival. This underscores the limits of single-intensity measures and the untapped potential of full waveform information.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s43247-024-01243-8
- https://www.nature.com/articles/s43247-024-01243-8.pdf
- OA Status
- gold
- Cited By
- 20
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391677465
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4391677465Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s43247-024-01243-8Digital Object Identifier
- Title
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Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-09Full publication date if available
- Authors
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Ashok Dahal, Hakan Tanyaş, Luigi LombardoList of authors in order
- Landing page
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https://doi.org/10.1038/s43247-024-01243-8Publisher landing page
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https://www.nature.com/articles/s43247-024-01243-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s43247-024-01243-8.pdfDirect OA link when available
- Concepts
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Geology, Waveform, Seismology, Artificial neural network, Landslide, Transformer, Geodesy, Computer science, Voltage, Machine learning, Engineering, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 9Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1971422826, https://openalex.org/W2943844914, https://openalex.org/W4229440182, https://openalex.org/W1895390467, https://openalex.org/W2077337112, https://openalex.org/W2166095666, https://openalex.org/W2097596572, https://openalex.org/W3176795589, https://openalex.org/W2565774639, https://openalex.org/W2096845398, https://openalex.org/W2159398439, https://openalex.org/W4245295309, https://openalex.org/W2063987149, https://openalex.org/W4386813022, https://openalex.org/W2157331557, https://openalex.org/W2064675550, https://openalex.org/W4224211827, https://openalex.org/W2524611247, https://openalex.org/W4366774873, https://openalex.org/W4386969082, https://openalex.org/W2530887700, https://openalex.org/W3047855151, https://openalex.org/W6763509872, https://openalex.org/W3096609285, https://openalex.org/W3199638386, https://openalex.org/W2588237346, https://openalex.org/W2017496690, https://openalex.org/W1902845573, https://openalex.org/W2070665913, https://openalex.org/W2936566957, https://openalex.org/W2770477935, https://openalex.org/W3005238662, https://openalex.org/W3008517453, https://openalex.org/W2097251745, https://openalex.org/W4299689471, https://openalex.org/W2899725189, https://openalex.org/W1971414292, https://openalex.org/W4309526584, https://openalex.org/W2883540819, https://openalex.org/W2795101470, https://openalex.org/W3183149794, https://openalex.org/W2592389775, https://openalex.org/W2258577332, https://openalex.org/W2031950708, https://openalex.org/W4386897466, https://openalex.org/W2896048901, https://openalex.org/W3156176292, https://openalex.org/W3024808444, https://openalex.org/W2275466902, https://openalex.org/W6739901393, https://openalex.org/W4291142580, https://openalex.org/W2089611415, https://openalex.org/W2964121744, https://openalex.org/W2521029800, https://openalex.org/W2882999202, https://openalex.org/W4366086142, https://openalex.org/W4390959482, https://openalex.org/W6910741483 |
| referenced_works_count | 58 |
| abstract_inverted_index.M | 87 |
| abstract_inverted_index.a | 70, 94 |
| abstract_inverted_index.w | 88 |
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| abstract_inverted_index.is | 106 |
| abstract_inverted_index.it | 68 |
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| abstract_inverted_index.we | 50, 121 |
| abstract_inverted_index.16% | 95 |
| abstract_inverted_index.7.8 | 89 |
| abstract_inverted_index.The | 77 |
| abstract_inverted_index.and | 40, 42, 47, 66, 132, 146 |
| abstract_inverted_index.can | 3, 122 |
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| abstract_inverted_index.run | 79 |
| abstract_inverted_index.the | 34, 63, 81, 85, 128, 135, 141, 147 |
| abstract_inverted_index.2015 | 86 |
| abstract_inverted_index.Such | 30 |
| abstract_inverted_index.This | 104, 139 |
| abstract_inverted_index.able | 60 |
| abstract_inverted_index.area | 82 |
| abstract_inverted_index.full | 102, 151 |
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| abstract_inverted_index.much | 133 |
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| abstract_inverted_index.peak | 27, 136 |
| abstract_inverted_index.rely | 15 |
| abstract_inverted_index.this | 52, 125 |
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| abstract_inverted_index.with | 73 |
| abstract_inverted_index.(~0.2 | 118 |
| abstract_inverted_index.Here, | 49 |
| abstract_inverted_index.Nepal | 91 |
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| abstract_inverted_index.m/s). | 119 |
| abstract_inverted_index.model | 59, 71 |
| abstract_inverted_index.phase | 39 |
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| abstract_inverted_index.their | 43 |
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| abstract_inverted_index.entire | 64 |
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| abstract_inverted_index.limits | 142 |
| abstract_inverted_index.mainly | 108 |
| abstract_inverted_index.models | 13 |
| abstract_inverted_index.motion | 24, 38, 130 |
| abstract_inverted_index.reveal | 93 |
| abstract_inverted_index.scalar | 28, 74 |
| abstract_inverted_index.(~25°) | 111 |
| abstract_inverted_index.Gorkha, | 90 |
| abstract_inverted_index.Seismic | 1 |
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| abstract_inverted_index.exposed | 113 |
| abstract_inverted_index.largely | 123 |
| abstract_inverted_index.problem | 53 |
| abstract_inverted_index.shaking | 17, 117 |
| abstract_inverted_index.values. | 29 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.achieved | 107 |
| abstract_inverted_index.affected | 83 |
| abstract_inverted_index.approach | 32 |
| abstract_inverted_index.arrival. | 138 |
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| abstract_inverted_index.equipped | 72 |
| abstract_inverted_index.measures | 145 |
| abstract_inverted_index.neglects | 33 |
| abstract_inverted_index.obtained | 20 |
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| abstract_inverted_index.experiments | 78 |
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| abstract_inverted_index.landscapes, | 6 |
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| abstract_inverted_index.information. | 153 |
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| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5086534279 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I94624287 |
| citation_normalized_percentile.value | 0.99292799 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |