Real‐time, reliable magnitudes for large earthquakes from 1 Hz GPS precise point positioning: The 2011 Tohoku‐Oki (Japan) earthquake

Wright, Tim; Houlié, Nicolas; Hildyard, M.W.; Iwabuchi, Tetsuya

doi:10.1029/2012gl051894

Cited by 127 publications

(80 citation statements)

References 34 publications

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“…A large moment release, first in the down-dip region, followed by a large slip near the trench, well corresponds to the inversion result of Ide et al (2011), but the timing of the transition is later and the location of large down-dip slip is apparently too close to the coast in their result. Analyses of high-rate GPS data are relatively new (e.g., Ji et al, 2004;Miyazaki et al, 2004;Ammon et al, 2011;Yue and Lay, 2011;Ohta et al, 2012;Wright et al, 2012). However, as shown by this study, high-rate GPS data contain rich information about the rupture process of earthquakes.…”

Section: Discussionmentioning

confidence: 99%

Constraints on the early-stage rupture process of the 2011 Tohoku-oki earthquake from 1-Hz GPS data

2012

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From the comparison of observed 1-Hz GPS data with simple forward computation that evaluates near and intermediate field terms, we put constraints on the early-stage rupture process of the 2011 Tohoku-oki earthquake. Mainly based on the time difference of the onset of large eastward displacements at stations along the northern coast of the source region, we estimate that the first significant moment release started around 35 km west of the hypocenter determined by JMA. The moment release occurred approximately from 20 s to 35 s after the initiation of the earthquake. Significant moment release around the hypocenter and in the near-trench region started from 25 s, and 35 s, at the earliest, respectively. Before about 20 s, the moment release of the 2011 Tohoku-oki earthquake must have been so small that any substantial displacements were not observed on land. The clear opposite motion that follows the large eastward displacement observed at many stations is due to the intermediate S-wave term.

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Section: Discussionmentioning

confidence: 99%

Constraints on the early-stage rupture process of the 2011 Tohoku-oki earthquake from 1-Hz GPS data

2012

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“…Measurement stations with expensive and accurate equipment are deployed and the data is analyzed by seismology scientists; this data is often not available to the general public in its raw form. However, in recent years several approaches have been explored to use alternative, inexpensive technology such as commercial-off-the-shelf GPS sensors to detect movement along earthquake faults (Grapenthin et al, n.d., Grapenthin et al, 2014, Wright et al, 2012, Clayton et al, 2015, Hudnut et al, 2002. These GPS based sensor networks measure displacement, and send real-time data measurement streams.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Earthquake Monitoring With Spatio-Temporal Fields

Whittier¹,

Nittel²,

Subasinghe³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With live streaming sensors and sensor networks, increasingly large numbers of individual sensors are deployed in physical space. Sensor data streams are a fundamentally novel mechanism to deliver observations to information systems. They enable us to represent spatio-temporal continuous phenomena such as radiation accidents, toxic plumes, or earthquakes almost as instantaneously as they happen in the real world. Sensor data streams discretely sample an earthquake, while the earthquake is continuous over space and time. Programmers attempting to integrate many streams to analyze earthquake activity and scope need to write code to integrate potentially very large sets of asynchronously sampled, concurrent streams in tedious application code. In previous work, we proposed the field stream data model (Liang et al., 2016) for data stream engines. Abstracting the stream of an individual sensor as a temporal field, the field represents the Earth's movement at the sensor position as continuous. This simplifies analysis across many sensors significantly. In this paper, we undertake a feasibility study of using the field stream model and the open source Data Stream Engine (DSE) Apache Spark(Apache Spark, 2017) to implement a real-time earthquake event detection with a subset of the 250 GPS sensor data streams of the Southern California Integrated GPS Network (SCIGN). The field-based real-time stream queries compute maximum displacement values over the latest query window of each stream, and related spatially neighboring streams to identify earthquake events and their extent. Further, we correlated the detected events with an USGS earthquake event feed. The query results are visualized in real-time.

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“…In particular, they allow the observation of the wavefield at very low frequencies, which may help to overcome the well-known magnitude saturation problem caused by the fact that the corner frequency of the source spectrum shifts toward lower frequencies with increasing source dimensions (Aki and Richards, 1980). It has recently been demonstrated that high-rate GPS data can be used for rapid source inversions in seismically active regions of the world, such as southern California, Japan, and Indonesia (e.g., Blewitt et al, 2009;Crowell et al, 2012;Melgar et al, 2012Melgar et al, , 2013Ohta et al, 2012;O'Toole et al, 2012;Wright et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Robust and Fast Probabilistic Source Parameter Estimation from Near‐Field Displacement Waveforms Using Pattern Recognition

Käufl¹,

Valentine²,

Wit³

et al. 2015

Bulletin of the Seismological Society of America

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The robust and automated determination of earthquake source parameters on a global and regional scale is important for many applications in seismology. We present a novel probabilistic method to invert a wide variety of (waveform) data for point-source parameters in real time using pattern recognition. Inferences are made in the form of marginal probability density functions for point-source parameters and incorporate realistic posterior uncertainty estimates. The neural-network-based method is calibrated using samples from the prior distribution, which are synthetic data vectors, and corresponding sources located in a predefined monitoring volume. Once a set of trained neural networks is available, inversions are fast with very moderate demands on computational resources: an inversion takes less than a second on a standard desktop computer. Uncertainties in the layered Earth model are taken into account in the Bayesian framework and increase the robustness of the results with respect to neglected 3D heterogeneities. Moreover, we find that the method is very robust with respect to perturbations such as observational noise and missing data and therefore is potentially well suited for automated and real-time tasks, such as earthquake monitoring and early warning. We demonstrate the method by means of synthetic tests and by inverting an observed high-rate Global Positioning System displacement dataset for the 2010 M w 7.2 El Mayor-Cucapah event. Our results are compatible with published point-source estimates for this event within the respective uncertainty bounds.Online Material: Additional information on the neural network methodology and implementation details, tables on neural network parameters, crustal model and reference double-couple solution, and figures showing prediction error, crustal models, normalized displacements, and histograms of weighted parameters.

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Real‐time, reliable magnitudes for large earthquakes from 1 Hz GPS precise point positioning: The 2011 Tohoku‐Oki (Japan) earthquake

Cited by 127 publications

References 34 publications

Constraints on the early-stage rupture process of the 2011 Tohoku-oki earthquake from 1-Hz GPS data

Constraints on the early-stage rupture process of the 2011 Tohoku-oki earthquake from 1-Hz GPS data

Real-Time Earthquake Monitoring With Spatio-Temporal Fields

Robust and Fast Probabilistic Source Parameter Estimation from Near‐Field Displacement Waveforms Using Pattern Recognition

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