2_use_case_name: [Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely recorded seismic sources like earthquakes and explosions. However, overlaps between the characteristics of these signals can still make it difficult to discriminate between source types during operational seismic monitoring. This ambiguity motivates the development of automated techniques for seismic signal classification. Furthermore, seismic differentiation within the broad class of SMMs has additional scientific and rapid response value. Examination of SMM seismic waveforms highlights a particularly strong contrast between the signals produced by primarily vertical processes versus processes that have a non-negligible horizontal component such as landslides and avalanches. A machine learning (ML) classification scheme is being investigated for differentiating between seismic signals generated by falls versus slides. Additionally included are shallow earthquakes and blasts because these are most similar to SMM signals and are commonly recorded on regional seismic networks. These classes, therefore, are the most useful for automated classification. Test datasets derive from waveform picks in the Exotic Seismic Events Catalog, a diverse collection of non-earthquake seismogenic surface events, and the Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog. In development is a shallow, feature-based approach to classification using statistical metrics extracted from waveforms. Feature importance metrics provide insight into the ML method, which leverage both unsupervised techniques and supervised techniques.
12_outputs: see description
16_dev_stage: Initiated
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Science Support System (SSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.