Abstract Accurate and (near) real-time earthquake monitoring provides the spatial and temporal behaviors of earthquakes for understanding the nature of earthquakes, and also helps in regional seismic hazard assessments and mitigations. Because of the increase in both the quality and quantity of seismic data, an automated earthquake monitoring system is needed. Most of the traditional methods for detecting earthquake signals and picking phases are based on analyses of features in recordings of an individual earthquake and/or their differences from background noises. When seismicity is high, the seismograms are complicated, and, therefore, traditional analysis methods often fail. With the development of machine learning algorithms, earthquake signal detection and seismic phase picking can be more accurate using the features obtained from a large amount of earthquake recordings. We have developed an attention recurrent residual U-Net algorithm, and used data augmentation techniques to improve the accuracy of earthquake detection and seismic phase picking on complex seismograms that record multiple earthquakes. The use of probability functions of P and S arrivals and potential P and S arrival pairs of earthquakes can increase the computational efficiency and accuracy of backprojection for earthquake monitoring in large areas. We applied our workflow to monitor the earthquake activitymore »
ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P - and S -Phase Arrivals
Abstract Seismograms are convolution results between seismic sources and the media that seismic waves propagate through, and, therefore, the primary observations for studying seismic source parameters and the Earth interior. The routine earthquake location and travel-time tomography rely on accurate seismic phase picks (e.g., P and S arrivals). As data increase, reliable automated seismic phase-picking methods are needed to analyze data and provide timely earthquake information. However, most traditional autopickers suffer from low signal-to-noise ratio and usually require additional efforts to tune hyperparameters for each case. In this study, we proposed a deep-learning approach that adapted soft attention gates (AGs) and recurrent-residual convolution units (RRCUs) into the backbone U-Net for seismic phase picking. The attention mechanism was implemented to suppress responses from waveforms irrelevant to seismic phases, and the cooperating RRCUs further enhanced temporal connections of seismograms at multiple scales. We used numerous earthquake recordings in Taiwan with diverse focal mechanisms, wide depth, and magnitude distributions, to train and test our model. Setting the picking errors within 0.1 s and predicted probability over 0.5, the AG with recurrent-residual convolution unit (ARRU) phase picker achieved the F1 score of 98.62% for P arrivals and 95.16% for S arrivals, and picking rates were more »
- Award ID(s):
- 1725729
- Publication Date:
- NSF-PAR ID:
- 10297784
- Journal Name:
- Seismological Research Letters
- Volume:
- 92
- Issue:
- 4
- Page Range or eLocation-ID:
- 2410 to 2428
- ISSN:
- 0895-0695
- Sponsoring Org:
- National Science Foundation
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SUMMARY Precisely constraining the source parameters of large earthquakes is one of the primary objectives of seismology. However, the quality of the results relies on the quality of synthetic earth response. Although earth structure is laterally heterogeneous, particularly at shallow depth, most earthquake source studies at the global scale rely on the Green's functions calculated with radially symmetric (1-D) earth structure. To avoid the impact of inaccurate Green's functions, these conventional source studies use a limited set of seismic phases, such as long-period seismic waves, broad-band P and S waves in teleseismic distances (30° < ∆ < 90°), and strong ground motion records at close-fault stations. The enriched information embedded in the broad-band seismograms recorded by global and regional networks is largely ignored, limiting the spatiotemporal resolution. Here we calculate 3-D strain Green's functions at 30 GSN stations for source regions of 9 selected global earthquakes and one earthquake-prone area (California), with frequency up to 67 mHz (15 s), using SPECFEM3D_GLOBE and the reciprocity theorem. The 3-D SEM mesh model is composed of mantle model S40RTS, crustal model CRUST2.0 and surface topography ETOPO2. We surround each target event with grids in horizontal spacing of 5 km and vertical spacing of 2.0–3.0 km, allowing usmore »
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ABSTRACT Rapid association of seismic phases and event location are crucial for real‐time seismic monitoring. We propose a new method, named rapid earthquake association and location (REAL), for associating seismic phases and locating seismic events rapidly, simultaneously, and automatically. REAL combines the advantages of both pick‐based and waveform‐based detection and location methods. It associates arrivals of different seismic phases and locates seismic events primarily through counting the number of P and S picks and secondarily from travel‐time residuals. A group of picks are associated with a particular earthquake if there are enough picks within the theoretical travel‐time windows. The location is determined to be at the grid point with the most picks, and if multiple locations have the same maximum number of picks, the grid point among them with smallest travel‐time residuals. We refine seismic locations using a least‐squares location method (VELEST) and a high‐precision relative location method (hypoDD). REAL can be used for rapid seismic characterization due to its computational efficiency. As an example application, we apply REAL to earthquakes in the 2016 central Apennines, Italy, earthquake sequence occurring during a five‐day period in October 2016, midway in time between the two largest earthquakes. We associate and locate moremore »
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1785/0220200382
