Strong ground motion data of the 2015 Gorkha Nepal earthquake sequence in the Kathmandu Valley

Shigefuji, Michiko; Takai, Nobuo; Bijukchhen, Subeg; Ichiyanagi, Masayoshi; Rajaure, Sudhir; Dhital, Megh Raj; Paudel, Lalu Prasad; Sasatani, Tsutomu

doi:10.1038/s41597-022-01634-6

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…The accelerograms for the 2015 Nepal earthquake provided by USGS recorded at KATNP station have the same sampling rate as the PESMOS database, are subjected to baseline correction, and filtered to perform sensor response correction following 34 . The accelerograms provided by the recently developed seismographic network such as CIGN, IITR, and HU‐TU with a sampling rate of 0.01 s can provide a broad range of frequency content of ground motion; therefore, are processed with cutoff frequency ranges up to Nyquist frequency of 50 Hz 35,36 . For COSMOS database the lower cutoff frequency of 0.1 Hz is considered and 0.05 Hz is considered for all other databases.…”

Section: Methodsmentioning

confidence: 99%

“…34 The accelerograms provided by the recently developed seismographic network such as CIGN, IITR, and HU-TU with a sampling rate of 0.01 s can provide a broad range of frequency content of ground motion; therefore, are processed with cutoff frequency ranges up to Nyquist frequency of 50 Hz. 35,36 For COSMOS database the lower cutoff frequency of 0.1 Hz is considered and 0.05 Hz is considered for all other databases. Based on the range of usable frequencies of ground motion data of all the databases, the response spectra are estimated at the natural periods ranging between 0.01 and 5 s.…”

Section: Ground Motion Database and Processingmentioning

confidence: 99%

See 1 more Smart Citation

Ground motion models for regions with limited data: Data‐driven approach

Sreenath,

Basu,

Raghukanth

2024

Earthq Engng Struct Dyn

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The ground motion model (GMM) for response spectra is crucial to hazard studies and structural design. GMM developed for regions with abundant data can capture appropriate spectral attenuation characteristics. However, several seismically active regions, such as the Himalayas, have limited recorded data, and thus, traditionally, simulated, or other regional data are supplemented with the available data for developing a GMM. Despite data augmentation, GMMs developed for such regions cannot appropriately depict attenuation characteristics. Therefore, the paper presents a comprehensive study encompassing the development of data‐driven approaches to obtain GMM for response spectra using the Himalayan crustal data with inputs: magnitude, distance, site class, depth, and a flag corresponding to the horizontal and vertical components. In this regard, GMM using transfer learning is initially developed from a pre‐trained model using global crustal data. Additionally, GMM using the XGBoost approach with monotonic constraints enforced on inputs is developed. These approaches could not capture trends appropriately. Finally, a monotonic constrained transfer learning model is developed to appropriately depict magnitude saturation and scaling, magnitude‐dependent geometric spreading, anelastic attenuation, depth scaling, site amplification, and other desired characteristics.

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Section: Methodsmentioning

confidence: 99%

Section: Ground Motion Database and Processingmentioning

confidence: 99%

Ground motion models for regions with limited data: Data‐driven approach

Sreenath,

Basu,

Raghukanth

2024

Earthq Engng Struct Dyn

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“…To enhance our knowledge of the seismic risk and hazard in the Kathmandu Valley and to offer important information for earthquake disaster mitigation. Michiko Shigefuji et al collected the strong ground motion data [4]. The dataset includes the 7.8-magnitude earthquake that struck Gorkha, Nepal, in 2015.…”

Section: Datasetmentioning

confidence: 99%

Predictive Modeling of Seismic Events: A Comparative Analysis Of K-Nearest Neighbors and Random Forest Algorithms

Sun

2024

HSET

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This study processes and predicts seismic data using data visualization approaches, K-nearest neighbors (KNN) and the random forest (RF) algorithms. The analysis's dataset includes a number of variables connected to earthquakes. The primary goal is to devise a forecast algorithm capable of accurately categorizing seismic events, data visualization tools are utilized to gain insights into the dataset, producing informative charts that depict the distribution and correlations among different variables. This visual evaluation aids in pinpointing anomalies or trends, facilitating a deeper understanding of the data's characteristics and guiding decisions during the modeling phase. Subsequently, the KNN method classifies earthquake occurrences based on their attributes, predicting the class label by considering the characteristics of its nearest neighbors. Additionally, Accurate classification of seismic events is enhanced by using RF, an ensemble learning technique that combines many decision trees to produce predictions. To optimize outcomes, the study adjusts the random forest model's hyperparameters through cross-validation. The study compares the performance of KNN and RF using a confusion matrix. The confusion matrix shows a thorough insight of categorization performance, which provides a comprehensive view of categorization efficacy. This assessment underscores the models' precision and effectiveness in classifying seismic events.

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“…To compare our earthquake simulations against ground motion records, we accessed the data collected at one seismic station (labelled as KATNP in Figure 1) as well as six high-frequency Global Navigation Satellite Systems (GNSS) stations. The data recorded at KATNP was processed and shared by Shigefuji et al (2022) whereas the GNSS station data was processed and shared by Galetzka et al (2015).…”

Section: Observations and Spatial Domainmentioning

confidence: 99%

From ground motion simulations to landslide occurrence prediction

Dahal¹,

Cruz²,

Tanyaş³

et al. 2023

Preprint

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Ground motion simulations solve wave equations in space and time, thus producing detailed estimates of the shaking time series. This is essentially uncharted territory for geomorphologists, for we have yet to understand which ground motion (synthetic or not) parameter, or combination of parameters, is more suitable to explain the coseismic landslide distribution. To address this gap, we developed a method to select the best ground motion simulation using a combination of Synthetic Aperture Radar Interferometry (InSAR) and strong motion data. Upon selecting the best simulation, we further developed a method to extract a suite of intensity parameters, which we used to both bivariately and multivariately analyse coseismic landslide occurrences taking the Gorkha earthquake as a reference. Our results show that beyond the virtually unanimous use of peak ground acceleration, velocity, or displacement in the literature, different shaking parameters could play a more relevant role in landslide occurence. These parameters are not necessarily linked to the peak values but mostly linked to the actual displacement, velocity, frequency content and shaking duration, elements too often neglected in geomorphological analyses. This in turn implies that we have yet to fully acknowledge the complexity of the interactions between full waveforms and hillslope responses.

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Strong ground motion data of the 2015 Gorkha Nepal earthquake sequence in the Kathmandu Valley

Cited by 10 publications

References 25 publications

Ground motion models for regions with limited data: Data‐driven approach

Ground motion models for regions with limited data: Data‐driven approach

Predictive Modeling of Seismic Events: A Comparative Analysis Of K-Nearest Neighbors and Random Forest Algorithms

From ground motion simulations to landslide occurrence prediction

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