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Abstract Multiple physical mechanisms have been proposed to explain the cause of intermediate‐depth and deep earthquakes, but they are still under debate. Source parameters such as stress drop, have the potential to provide insight into their physical mechanisms. We develop a modified spectral decomposition method to analyze 1‐year seismic data from temporary land‐based and ocean bottom seismographs in a complex subduction zone. By applying this method to investigate 1,083 intermediate‐depth and deep earthquakes in the Tonga slab, we successfully resolve the source spectra and stress drops of 743MW2.6–6.0 earthquakes. Although the absolute stress drops are subject to the choices of source model parameters, the relative stress drops are more reliably resolved. The median stress drop of Tonga earthquakes does not change with respect to magnitude but decreases with depth by 2–3 times in two separate depth ranges of 70–250 and 400–600 km, corresponding to intermediate‐depth and deep earthquakes, respectively. The median stress drops show spatial variations, with two high‐stress‐drop (five times higher than the ambient value) regions, coinciding with strong local deformation where the Tonga slab bends or tears. In the Tonga double seismic zone at 120–300 km depths, the median stress drop appears smaller in the lower plane than in the upper plane, suggesting a slower rupture velocity or a higher fluid content in the lower‐plane region. Our results suggest that intermediate‐depth and deep earthquakes in the Tonga slab generally follow the earthquake self‐similar model and favor the fluid‐related embrittlement hypothesis for both groups of earthquakes.
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Deep learning for deep earthquakes: insights from OBS observations of the Tonga subduction zone
SUMMARY Applications of machine learning in seismology have greatly improved our capability of detecting earthquakes in large seismic data archives. Most of these efforts have been focused on continental shallow earthquakes, but here we introduce an integrated deep-learning-based workflow to detect deep earthquakes recorded by a temporary array of ocean-bottom seismographs (OBSs) and land-based stations in the Tonga subduction zone. We develop a new phase picker, PhaseNet-TF, to detect and pick P- and S-wave arrivals in the time–frequency domain. The frequency-domain information is critical for analysing OBS data, particularly the horizontal components, because they are contaminated by signals of ocean-bottom currents and other noise sources in certain frequency bands. PhaseNet-TF shows a much better performance in picking S waves at OBSs and land stations compared to its predecessor PhaseNet. The predicted phases are associated using an improved Gaussian Mixture Model Associator GaMMA-1D and then relocated with a double-difference package teletomoDD. We further enhance the model performance with a semi-supervised learning approach by iteratively refining labelled data and retraining PhaseNet-TF. This approach effectively suppresses false picks and significantly improves the detection of small earthquakes. The new catalogue of Tonga deep earthquakes contains more than 10 times more events compared to the reference catalogue that was analysed manually. This deep-learning-enhanced catalogue reveals Tonga seismicity in unprecedented detail, and better defines the lateral extent of the double-seismic zone at intermediate depths and the location of four large deep-focus earthquakes relative to background seismicity. It also offers new potential for deciphering deep earthquake mechanisms, refining tomographic models, and understanding of subduction processes.
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- PAR ID:
- 10517781
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 238
- Issue:
- 2
- ISSN:
- 0956-540X
- Format(s):
- Medium: X Size: p. 1073-1088
- Size(s):
- p. 1073-1088
- Sponsoring Org:
- National Science Foundation
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