[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XCZP0ER0S1JCWS97QF6BG0":3,"skill-same:01M6XCZP0ER0S1JCWS97QF6BG0":63},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":13,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":47,"updatedAt":48,"install":49,"resources":62},"01M6XCZP0ER0S1JCWS97QF6BG0","seismic-picker-selection",null,"This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.","Waaaalnut\u002Fskillsbench","Waaaalnut","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F103995656?v=4","01M6XCS9K1V51EY3JMB7DGMX24","01M6XCS9Q4WPW88W62C55G1H44",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks-no-skills\u002Fearthquake-phase-association\u002Fenvironment\u002Fskills\u002Fseismic-picker-selection\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"selection","seismic picker selection","seismic","seismology","when","phase","at","hand","you","event","2025","it","detection","shared","summary","task","methods","use","catalog","2026-11-10 10:12:30",false,"---\nname: seismic-picker-selection\ndescription: This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.\n---\n\n\n# Seismic Event Detection & Phase Picking Method Selection Guide\n\n## Overview: Method Tradeoffs\n\nWhen choosing an event detection and phase picking method, consider these key tradeoffs:\n\n| Method | Generalizability | Sensitivity | Speed, Ease-of-Use | False Positives |\n|--------|------------------|-------------|-------------------|-----------------|\n| STA\u002FLTA | High | Low | Fast, Easy | Many |\n| Manual | High | High | Slow, Difficult | Few |\n| Deep Learning | High | High | Fast, Easy | Medium |\n| Template Matching | Low | High | Slow, Difficult | Few |\n\n- **Generalizability**: Ability to find arbitrary earthquake signals\n- **Sensitivity**: Ability to find small earthquakes\n\n\n**Key insight:** Each method has strengths and weaknesses. Purpose and resources should guide your choice.\n\n## STA\u002FLTA (Short-Term Average \u002F Long-Term Average)\n\n### Advantages\n- Runs very fast: Automatically operates in real-time\n- Easy to understand & implement: Can optimize for different window lengths and ratios\n- No prior knowledge needed: Does not require information about earthquake sources or waveforms\n- Amplitude-based detector: Reliably detects large earthquake signals\n\n### Limitations\n- High rate of false detections during active sequences\n- Automatic picks not as precise\n- Requires manual review and refinement of picks for a quality catalog\n\n---\n\n## Template Matching\n\n### Advantages\n- Optimally sensitive detector (more sensitive than deep-learning): Can find smallest earthquakes buried in noise, if similar enough to template waveform\n- Excellent for improving temporal resolution of earthquake sequences\n- False detections are not as concerning when using high detection threshold\n\n### Limitations\n- Requires prior knowledge about earthquake sources: Need **template waveforms** with good picks from a preexisting catalog\n- Does not improve spatial resolution: Unknown earthquake sources that are not similar enough to templates cannot be found\n- Setup effort required: Must extract template waveforms and configure processing\n- Computationally intensive\n\n---\n\n## Deep Learning Pickers\n\n### When to Use\n- Adds most value when existing seismic networks are sparse or nonexistent\n- Automatically and rapidly create more complete catalog during active sequences\n- Requires continuous seismic data\n- Best on broadband stations, but also produces usable picks on accelerometers, nodals, and Raspberry Shakes\n- **Use case**: Temporary deployment of broadband or nodal stations where you want an automatically generated local earthquake catalog\n\n### Advantages\n- No prior knowledge needed about earthquake sources or waveforms\n- Finds lots of small local earthquakes (lower magnitude of completeness, Mc) with fewer false detections than STA\u002FLTA\n- Relatively easy to set up and run: Reasonable runtime with parallel processing. SeisBench provides easy-to-use model APIs and pretrained models.\n\n### Limitations\n- Out-of-distribution data issues: For datasets not represented in training data, expect larger automated pick errors (0.1-0.5 s) and missed picks\n- Cannot pick phases completely buried in noise - Not quite as sensitive as template-matching\n- Sometimes misses picks from larger earthquakes that are obvious to humans, for unexplained\nreason\n\n## References\n- This skill is a derivative of Beauce, Eric and Tepp, Gabrielle and Yoon, Clara and Yu, Ellen and Zhu, Weiqiang. _Building a High Resolution Earthquake Catalog from Raw Waveforms: A Step-by-Step Guide_ Seismological Society of America (SSA) Annual Meeting, 2025. https:\u002F\u002Fai4eps.github.io\u002FEarthquake_Catalog_Workshop\u002F\n- Allen (1978) - STA\u002FLTA method\n- Perol et al. (2018) - Deep learning for seismic detection\n- Huang & Beroza (2015) - Template matching methods\n- Yoon and Shelly (2024), TSR - Deep learning vs template matching comparison\n","# Seismic Event Detection & Phase Picking Method Selection Guide\n\n## Overview: Method Tradeoffs\n\nWhen choosing an event detection and phase picking method, consider these key tradeoffs:\n\n| Method | Generalizability | Sensitivity | Speed, Ease-of-Use | False Positives |\n|--------|------------------|-------------|-------------------|-----------------|\n| STA\u002FLTA | High | Low | Fast, Easy | Many |\n| Manual | High | High | Slow, Difficult | Few |\n| Deep Learning | High | High | Fast, Easy | Medium |\n| Template Matching | Low | High | Slow, Difficult | Few |\n\n- **Generalizability**: Ability to find arbitrary earthquake signals\n- **Sensitivity**: Ability to find small earthquakes\n\n\n**Key insight:** Each method has strengths and weaknesses. Purpose and resources should guide your choice.\n\n## STA\u002FLTA (Short-Term Average \u002F Long-Term Average)\n\n### Advantages\n- Runs very fast: Automatically operates in real-time\n- Easy to understand & implement: Can optimize for different window lengths and ratios\n- No prior knowledge needed: Does not require information about earthquake sources or waveforms\n- Amplitude-based detector: Reliably detects large earthquake signals\n\n### Limitations\n- High rate of false detections during active sequences\n- Automatic picks not as precise\n- Requires manual review and refinement of picks for a quality catalog\n\n---\n\n## Template Matching\n\n### Advantages\n- Optimally sensitive detector (more sensitive than deep-learning): Can find smallest earthquakes buried in noise, if similar enough to template waveform\n- Excellent for improving temporal resolution of earthquake sequences\n- False detections are not as concerning when using high detection threshold\n\n### Limitations\n- Requires prior knowledge about earthquake sources: Need **template waveforms** with good picks from a preexisting catalog\n- Does not improve spatial resolution: Unknown earthquake sources that are not similar enough to templates cannot be found\n- Setup effort required: Must extract template waveforms and configure processing\n- Computationally intensive\n\n---\n\n## Deep Learning Pickers\n\n### When to Use\n- Adds most value when existing seismic networks are sparse or nonexistent\n- Automatically and rapidly create more complete catalog during active sequences\n- Requires continuous seismic data\n- Best on broadband stations, but also produces usable picks on accelerometers, nodals, and Raspberry Shakes\n- **Use case**: Temporary deployment of broadband or nodal stations where you want an automatically generated local earthquake catalog\n\n### Advantages\n- No prior knowledge needed about earthquake sources or waveforms\n- Finds lots of small local earthquakes (lower magnitude of completeness, Mc) with fewer false detections than STA\u002FLTA\n- Relatively easy to set up and run: Reasonable runtime with parallel processing. SeisBench provides easy-to-use model APIs and pretrained models.\n\n### Limitations\n- Out-of-distribution data issues: For datasets not represented in training data, expect larger automated pick errors (0.1-0.5 s) and missed picks\n- Cannot pick phases completely buried in noise - Not quite as sensitive as template-matching\n- Sometimes misses picks from larger earthquakes that are obvious to humans, for unexplained\nreason\n\n## References\n- This skill is a derivative of Beauce, Eric and Tepp, Gabrielle and Yoon, Clara and Yu, Ellen and Zhu, Weiqiang. _Building a High Resolution Earthquake Catalog from Raw Waveforms: A Step-by-Step Guide_ Seismological Society of America (SSA) Annual Meeting, 2025. https:\u002F\u002Fai4eps.github.io\u002FEarthquake_Catalog_Workshop\u002F\n- Allen (1978) - STA\u002FLTA method\n- Perol et al. (2018) - Deep learning for seismic detection\n- Huang & Beroza (2015) - Template matching methods\n- Yoon and Shelly (2024), TSR - Deep learning vs template matching comparison\n",{"name":5,"description":7},"main","7eeb258ada72b7f58119b0e7a2c00ea89d9beedd","tasks-no-skills\u002Fearthquake-phase-association\u002Fenvironment\u002Fskills\u002Fseismic-picker-selection","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench","https:\u002F\u002Fgithub.com\u002FWaaaalnut","student of Zhejiang University","2026-04-21 23:16:23",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add Waaaalnut\u002Fskillsbench --skill seismic-picker-selection",{"tool":55,"command":56},"bunx","bunx skills add Waaaalnut\u002Fskillsbench --skill seismic-picker-selection",{"tool":58,"command":59},"pnpm","pnpm dlx skills add Waaaalnut\u002Fskillsbench --skill seismic-picker-selection","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks-no-skills\u002Fearthquake-phase-association\u002Fenvironment\u002Fskills\u002Fseismic-picker-selection\u002FSKILL.md",true,[],[64,78,93,118,139,157],{"skillNo":65,"name":66,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":74,"publishedAt":37},"01M6XEWMJ6VY3ZC53MPDCPG3GH","data-reconciliation","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fxlsx-recover-data\u002Fenvironment\u002Fskills\u002Fdata-reconciliation\u002FSKILL.md",[69],{"categoryNo":70,"categoryName":71,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[75,76,77],"data reconciliation","reconciliation","data",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":37},"01M6XEW8MNPMF1KRXV6ATGFRQF","speech-to-text","Transcribe video to timestamped text using Whisper tiny model (pre-installed).","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-tutorial-indexer\u002Fenvironment\u002Fskills\u002Fspeech-to-text\u002FSKILL.md",[],[85,86,87,88,89,90,91,92],"speech","text","speech to text","transcribe video","whisper","model","Video","transcribe",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":104,"publishedAt":37},"01M6XEW3FQZP0T1RKQQJFXVY9T","video-processor","Process videos by removing segments and concatenating remaining parts. Use when you need to remove detected pauses\u002Fopenings from videos, create highlight reels, or batch process segment removals using ffmpeg filter_complex.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fvideo-processor\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[91,105,106,107,108,109,22,110,111,112,113,26,114,115,116,35,117],"processor","video processor","batch","create","reels","parts","segments","process","videos","segment","filter","ffmpeg","remove",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":125,"publishedAt":37},"01M6XEVXJ5KZ0NTY3XZKRY4AS1","silence-detector","Detect initial silence segments in audio\u002Fvideo using energy-based analysis. Use when you need to find low-energy periods at the start of recordings (title slides, setup time, pre-roll silence).","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fsilence-detector\u002FSKILL.md",[124],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},[126,127,128,129,22,26,130,131,24,132,133,111,134,135,91,136,137,138,35],"detector","silence detector","title","slides","time","energy","recordings","detect","find","Audio","analysis","start","setup",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":145,"publishedAt":37},"01M6XEVT3NGRN1FT7JPMAPXS6M","segment-combiner","Combine multiple segment detection results into a unified list. Use when you need to merge segments from different detectors, prepare removal lists for video processing, or consolidate detection outputs.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fsegment-combiner\u002FSKILL.md",[],[114,146,91,30,147,148,149,150,22,151,152,111,26,153,154,35,155,156],"segment combiner","prepare","merge","video processing","for","outputs","combine","processing","consolidate","results","list",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":166,"publishedAt":37},"01M6XEVMPVASGS2KX86QK4FMT7","report-generator","Generate compression reports for video processing. Use when you need to create structured JSON reports with duration statistics, compression ratios, and segment details after video processing.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Freport-generator\u002FSKILL.md",[163],{"categoryNo":164,"categoryName":165,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000034","行政与办公人员",[167,168,169,114,22,150,26,170,153,171,172,173,35,149,174,91,175,176,108],"generator","report generator","report","with","statistics","structured","reports","json","compression","generate"]