Sedimentary basins can strongly amplify seismic waves from earthquakes. To better predict future ground motions, detailed knowledge of the sediment thickness and internal structure of basins is required. We image the sediment-to-bedrock interface of the Kanto Basin in Japan using the P-wave reflectivity response from earthquake and ambient seismic noise autocorrelation functions (ACFs) at 286 shallow borehole stations. Earthquake ACFs are computed using P-wave records from 50 Mw 6+ teleseismic events. Noise ACFs are obtained using 1 month of continuous data. Both methods are used to retrieve P-wave traveltimes between the surface and the bedrock interface and map the basin basement geometry. Our prediction of the basement depth agrees generally well with that from a reference velocity model, except for smoother variations in the central part of the basin. Using full-wavefield simulations, we show that the nature of the autocorrelated wavefield has a significant impact on the shape of the ACF waveforms and that earthquake ACFs yield more accurate results in the Kanto Basin.
Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.
more » « less- Award ID(s):
- 1725344
- NSF-PAR ID:
- 10378293
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 47
- Issue:
- 15
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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