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20 pages, 8063 KiB

Open AccessArticle

Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region

by Haijia Wen, Xinzhi Zhou, Chi Zhang, Mingyong Liao and Jiafeng Xiao

Remote Sens. 2023, 15(9), 2226; https://doi.org/10.3390/rs15092226 - 22 Apr 2023

Cited by 14 | Viewed by 2859

Abstract

This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64–0.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas. Full article

(This article belongs to the Special Issue Remote Sensing of Earthquake Engineering and Earthquake-Triggered Landslides and Displacement Monitoring)

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17 pages, 4012 KiB

Open AccessArticle

Magnetic Anomaly Characteristics and Magnetic Basement Structure in Earthquake-Affected Changning Area of Southern Sichuan Basin, China: A New Perspective from Land-Based Stations

by Chao Dong, Bin Chen and Can Wang

Remote Sens. 2023, 15(1), 23; https://doi.org/10.3390/rs15010023 - 21 Dec 2022

Cited by 4 | Viewed by 3148

Abstract

The Changning area is located in the southern Sichuan basin and the western Yangtze Plate and is the most abundant shale gas exploration area in China. In recent years, Changning has experienced frequent earthquakes with moderate magnitudes, attracting extensive interest. To investigate the magnetic characteristics in Changning, 952 land-based stations were employed to establish a magnetic anomaly model with a resolution of 2 km, and the subsurface magnetic basement structure was obtained by an iterative algorithm in the Fourier domain. The magnetic anomaly model shows significant distinctions between the northern salt mine area and the southern shale gas area. The magnetic basement includes the crystalline basement and the Sinian sedimentary rock metamorphic basement, which has strong magnetism. The large intracratonic rift that developed in the Sinian–Early Cambrian plays an important role in the evolution of Changning, which also impacts magnetic anomalies and the magnetic basement structure. Finally, by comparing the seismic wave velocity ratio structure, the deeper magnetic basement that corresponds to the higher seismic wave velocity ratio can be explained. This article implies that magnetic anomalies and magnetic basement depth have a certain correlation with earthquakes in Changning, and it provides a geodynamic reference for Changning and the southern Sichuan basin. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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18 pages, 17990 KiB

Open AccessArticle

Evaluating the Performance and Stability of the Highway Subgrades in Seismic Events, a Case Study of the Changning Earthquake, Sichuan

by Zhen Cui, Maochu Zhang, Kai Wu and Hongsheng Ma

Int. J. Environ. Res. Public Health 2022, 19(21), 14379; https://doi.org/10.3390/ijerph192114379 - 3 Nov 2022

Cited by 4 | Viewed by 2146

Abstract

On 17 June 2019, an M6.0 earthquake occurred in Changning County, Sichuan Province, China. Considerable highway subgrades were damaged in this earthquake. By investigating seismic damage of these subgrades, a systematical analysis of influence factors and failure mode of the damages on highway subgrade have been given. There is a close relationship between the damaged degree of subgrade and the distance of epicenter, fault distance, and structure type of subgrade. The seismic hazard investigation shows that the seismic damage of the cut-and-fill subgrade was more severe than that of the fill subgrade. Taking the Changning earthquake ground motion record as input, 3D dynamic finite element analyses were performed. The seismic damage mechanisms of cut-and-fill subgrade and fill subgrade were revealed. The numerical simulation confirmed that the cut-and-fill subgrade was more easily damaged by earthquakes compared with the fill subgrade. The fill-and-cut interface of the cut-and-fill subgrade had a notable plastic strain band after the earthquake, and the permanent displacement of the slope top was significant. In this manner, the numerical results are consistent with seismic investigation data. Considering the seismic investigation data for highway subgrades are rare, this paper may provide effective references for aseismic design and deformation control of highway subgrades. Full article

(This article belongs to the Collection Environmental Risk Assessment)

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15 pages, 3827 KiB

Open AccessArticle

Source Geometry and Causes of the 2019 Ms6.0 Changning Earthquake in Sichuan, China Based on InSAR

by Hua Gao, Mingsheng Liao, Xiaoge Liu, Wenbin Xu and Nan Fang

Remote Sens. 2022, 14(9), 2082; https://doi.org/10.3390/rs14092082 - 26 Apr 2022

Cited by 8 | Viewed by 5329

Abstract

On 17 June 2019, an Ms6.0 earthquake occurred in Changning, Sichuan, China (Changning event), which was the largest earthquake on record within 50 km of the area. It attracted great attention as the area has the largest shale gas production in China as well as significant mineral salt production. Using the Interferometric Synthetic Aperture Radar (InSAR), we extract the coseismic deformation of the Changning event and two earlier Ms > 5.0 earthquakes which occurred in the same region (16 December 2018 Ms5.7 and 3 January 2019 Ms5.3) from the Sentinel-1 and ALOS2 data. We use nonlinear and linear methods to invert the fault models of the three earthquakes based on the deformation fields. The final model shows that the Changning event was caused by a fault with left-lateral strike and thrust slip. The strike is 124.3° with a dip angle of 43.4°. The seismic moment obtained by inversion is 5.28 × 10¹⁷ Nm, corresponding to Mw 5.78. Based on the fault models, we analyze the cause of the Changning earthquake considering the local tectonic setting, Coulomb stress change, mining, and fluid injection. We consider that the event may be related to salt mining. The two earlier Ms > 5.0 earthquakes may also play an important role in advancing the Changning earthquake. Full article

(This article belongs to the Special Issue Into the Seismic Cycle: Remote Sensing of Ground Deformation and Earthquake Activity)

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16 pages, 2849 KiB

Open AccessArticle

A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

by Manjiang Shi, Qi Cao, Baisong Ran and Lanyan Wei

Sustainability 2021, 13(10), 5608; https://doi.org/10.3390/su13105608 - 18 May 2021

Cited by 15 | Viewed by 4502

Abstract

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction. Full article

(This article belongs to the Special Issue Planning for Resilience in Rural Communities)

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