An official website of the United States government
Moho topography yields insights into the evolution of the lithosphere and the strength of the lower crust. The Moho reflected phase (PmP) samples this key boundary and may be used in concert with the first arriving P phase to infer crustal thickness. The densely sampled station coverage of distributed acoustic sensing arrays allows for the observation of PmP at fine-scale intervals over many kilometers with individual events. We use PmP recorded by a 100-km-long fiber that traverses a path between Ridgecrest, CA and Barstow, CA to explore Moho variability in Southern California. With hundreds of well-recorded events, we verify that PmP is observable and develop a technique to identify and pick P-PmP differential times with high confidence. We use these observations to constrain Moho depth throughout Southern California, and we find that short-wavelength variability in crustal thickness is abundant, with sharp changes across the Garlock Fault and Coso Volcanic Field.
more »
« less
Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California
Abstract Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P‐ and S‐wave arrivals, auto‐identification of later seismic phases such as the Moho‐reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine‐identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high‐quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep‐neural‐network‐based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)‐autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.
more »
« less
- PAR ID:
- 10369432
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 127
- Issue:
- 4
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)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 96.72% for P waves and 90.07% for S waves. The ARRU phase picker also shown a great generalization capability, when handling unseen data. When applied the model trained with Taiwan data to the southern California data, the ARRU phase picker shown no cognitive downgrade. Comparing with manual picks, the arrival times determined by the ARRU phase picker shown a higher consistency, which had been evaluated by a set of repeating earthquakes. The arrival picks with less human error could benefit studies, such as earthquake location and seismic tomography.more » « less
-
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 more than three times as many events (3341) as are in Italy's National Institute of Geophysics and Volcanology routine catalog (862). The spatial distribution of these relocated earthquakes shows a similar but more concentrated pattern relative to the cataloged events. Our study demonstrates that it is possible to characterize seismicity automatically and quickly using REAL and seismic picks.more » « less
-
null (Ed.)Abstract Local seismic events recorded by the large-N Incorporated Research Institutions for Seismology Community Wavefield Experiment in Oklahoma are used to estimate Moho reflections near the array. For events within 50 km of the center of the array, normal moveout corrections and receiver stacking are applied to identify the PmP and SmS Moho reflections on the vertical and transverse components. Corrections for the reported focal depths are applied to a uniform event depth. To stack signals from multiple events, further static corrections of the envelopes of the Moho reflected arrivals from the individual event stacks are applied. The multiple-event stacks are then used to estimate the pre-critical PmP and SmS arrivals, and an average Poisson’s ratio of 1.77±0.02 was found for the crust near the array. Using a modified Oklahoma Geological Survey (OGS) velocity model with this Poisson’s ratio, the time-to-depth converted PmP and SmS arrivals resulted in a Moho depth of 41±0.6 km. The modeling of wide-angle Moho reflections for selected events at epicenter-to-station distances of 90–135 km provides additional constraints, and assuming the modified OGS model, a Moho depth of 40±1 km was inferred. The difference between the pre-critical and wide-angle Moho estimates could result from some lateral variability between the array and the wide-angle events. However, both estimates are slightly shallower than the original OGS model Moho depth of 42 km, and this could also result from a somewhat faster lower crust. This study shows that local seismic events, including induced events, can be utilized to estimate properties and structure of the crust, which, in turn, can be used to better understand the tectonics of a given region. The recording of local seismicity on large-N arrays provides increased lateral phase coherence for the better identification of precritical and wide-angle reflected arrivals.more » « less
-
Abstract Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences ofPandSwave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets.more » « less
