Temporal Variation and Statistical Assessment of the b Value off the Pacific Coast of Tokachi, Hokkaido, Japan

Xie, Weiyun; Hattori, Katsumi; Han, Pu

doi:10.3390/e21030249

Cited by 28 publications

(16 citation statements)

References 27 publications

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“…The internal physical properties of earthquake processes are complex. However, some earthquakes with larger energy have been reported to generate various patterns before their occurrences [ 21 , 52 , 53 ], including foreshocks [ 54 , 55 ], preseismic quiescence [ 56 , 57 ], precursory spatiotemporal clusters [ 33 , 58 ], and earthquake critical phenomena [ 53 , 59 ]. It is currently impractical to expect that preseismic anomalies would show significant event-to-event correspondence with earthquakes.…”

Section: Resultsmentioning

confidence: 99%

Detecting Earthquake-Related Anomalies of a Borehole Strain Network Based on Multi-Channel Singular Spectrum Analysis

Hattori

Zhu

et al. 2020

Entropy

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To investigate the nonlinear spatio-temporal behavior of earthquakes, a complex network has been built using borehole strain data from the southwestern endpoint of the Longmenshan fault zone, Sichuan-Yunnan region of China, and the topological structural properties of the network have been investigated based on data from 2011–2014. Herein, six observation sites were defined as nodes and their edges as the connections between them. We introduced Multi-channel Singular Spectrum Analysis (MSSA) to analyze periodic oscillations, earthquake-related strain, and noise in multi-site observations, and then defined the edges of the network by calculating the correlations between sites. The results of the daily degree centrality of the borehole strain network indicated that the strain network anomalies were correlatable with local seismicity associate with the earthquake energy in the strain network. Further investigation showed that strain network anomalies were more likely to appear before major earthquakes rather than after them, particularly within 30 days before an event. Anomaly acceleration rates were also found to be related to earthquake energy. This study has revealed the self-organizing pre-earthquake phenomena and verified the construction of borehole networks is a powerful tool for providing information on earthquake precursors and the dynamics of complex fault systems.

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Section: Resultsmentioning

confidence: 99%

Detecting Earthquake-Related Anomalies of a Borehole Strain Network Based on Multi-Channel Singular Spectrum Analysis

Hattori

Zhu

et al. 2020

Entropy

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“…The MAXC method also gave better Mc estimates that were used to find the spatial frequency magnitude distribution (FMD). MAXC yielded a more vigorous estimate than the least squares regression method [30,31]. To find the Mc, we used a time window of 100 event samples, a 50-event shifting step, and a processing grid cell of 0.1 0 × 0.1 0 [31,32].…”

Section: Analysis Methodsmentioning

confidence: 99%

Spatial Variation of b-Values and Their Relationship with the Fault Blocks in the Western Part of the Tibetan Plateau and Its Surrounding Areas

Hussain

Zhang

Usman

et al. 2020

Entropy

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The Tibetan Plateau is considered to be one of the best natural laboratories for seismological research. This study sought to determine the spatial variations of b-values in the western part of the Tibetan Plateau, along with its surrounding areas, and the relation with the region’s fault blocks. The study region lies within 27–36.5° N, 78–89° E, and its fracture structure consists of strike-slip faults, as well as normal and thrust faults. A catalog record from 2009–2019 provided 4431 well-centered earthquakes that varied in magnitude from 0.1 to 8.2 M. The record was obtained from China’s seismological network, which is capable of recording low magnitudes to analyze b-values in the study area. The key findings of this study are as follows: (1) the range of earthquake depth in the region was 0–256 km, with the depth histogram showing a high frequency occurrence of shallow earthquakes in the area; (2) a time histogram showed that the major earthquakes occurred between 2014–2015, including the notable 2015 Gorkha earthquake (M = 8.2); (3) the b-value computed in the study area was 0.5 to 1.6, but in most of the study area, the b-value ranged from 0.6 to 0.9, which was a low to intermediate value, due to the presence of strike-slip faults in the central part of the study area and underthrusting in the region (south of the study area); and (4) a high b-value was found in the northwestern and eastern regions of the area, which proved that the area is prone to small earthquakes in the near future. The study also showed that the central and southern areas of the study region had low to intermediate b-values, meaning that it is prone to destructive and massive earthquakes with high magnitudes, such as the Gorkha earthquake (southern part of the study area). Low b-values revealed the degree of variation in rock properties, including large stress and strain, a fractured medium, a high deformation rate, and large faults. Small b-values were observed when the stress level was high in the investigated region, which might be used to predict a massive high-magnitude earthquake in the near future.

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“…Because of the limitations in the detection capability of the seismograph network, some weak earthquakes are not recorded. We used the maximum curvature technique (Xie et al, 2019) to estimate the smallest magnitude Mc that could be completely detected in the seismic-relevant area (with half large size of the earthquake-sensitive area of the Ecuador earthquake). The estimated complete magnitude is Mc = 4.3 by the seismic events (from USGS) in 2016.…”

Section: Cumulative Benioff Strain S Studymentioning

confidence: 99%

Analysis of Swarm Satellite Magnetic Field Data Before the 2016 Ecuador (Mw = 7.8) Earthquake Based on Non-negative Matrix Factorization

Zhu

Fan

et al. 2021

Front. Earth Sci.

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In this paper, based on non-negative matrix factorization (NMF), we analyzed the ionosphere magnetic field data of the Swarm Alpha satellite before the 2016 (Mw = 7. 8) Ecuador earthquake (April 16, 0.35°N, 79.93°W), including the whole data collected under quiet and disturbed geomagnetic conditions. The data from each track were decomposed into basis features and their corresponding weights. We found that the energy and entropy of one of the weight components were more concentrated inside the earthquake-sensitive area, which meant that this weight component was more likely to reflect the activity inside the earthquake-sensitive area. We focused on this weight component and used five times the root mean square (RMS) to extract the anomalies. We found that for this weight component, the cumulative number of tracks, which had anomalies inside the earthquake-sensitive area, showed accelerated growth before the Ecuador earthquake and recovered to linear growth after the earthquake. To verify that the accelerated cumulative anomaly was possibly associated with the earthquake, we excluded the influence of the geomagnetic activity and plasma bubble. Through the random earthquake study and low-seismicity period study, we found that the accelerated cumulative anomaly was not obtained by chance. Moreover, we observed that the cumulative Benioff strain S, which reflected the lithosphere activity, had acceleration behavior similar to the accelerated cumulative anomaly of the ionosphere magnetic field, which suggested that the anomaly that we obtained was possibly associated with the Ecuador earthquake and could be described by one of the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) models.

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Temporal Variation and Statistical Assessment of the b Value off the Pacific Coast of Tokachi, Hokkaido, Japan

Cited by 28 publications

References 27 publications

Detecting Earthquake-Related Anomalies of a Borehole Strain Network Based on Multi-Channel Singular Spectrum Analysis

Detecting Earthquake-Related Anomalies of a Borehole Strain Network Based on Multi-Channel Singular Spectrum Analysis

Spatial Variation of b-Values and Their Relationship with the Fault Blocks in the Western Part of the Tibetan Plateau and Its Surrounding Areas

Analysis of Swarm Satellite Magnetic Field Data Before the 2016 Ecuador (Mw = 7.8) Earthquake Based on Non-negative Matrix Factorization

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