Influence of source uncertainty on stochastic ground motion simulation: a case study of the 2022 Mw 6.6 Luding, China, earthquake

Dang, Pengfei; Cui, Jie; Liu, Qifang; Li, Yadong

doi:10.1007/s00477-023-02427-y

Cited by 17 publications

(4 citation statements)

References 43 publications

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“…However, this abrupt decrease in AT must be due to some sudden energy emanation in the atmosphere over the epicentral region. Some satellite data can clear the EQ precursors in the atmosphere [54][55][56][57][58]. However, a prominent increase in the AT and substantial decrease in RH was also observed during the Monte Cristo Range EQ as possible precursory signals for the impending main shock (Figures 11 and 12).…”

Section: Discussionmentioning

confidence: 95%

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

et al. 2023

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The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.

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Section: Discussionmentioning

confidence: 95%

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

et al. 2023

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Section: Anfis Architecturementioning

confidence: 99%

“…The ANFIS network has five layers (Figure 8) while the central core is a fuzzy inference system. The first layer receives inputs and converts them to fuzzy values by membership functions [57,58]. It is clear that more accurate evaluation means a slight difference between the ANFIS answer and the real value [61,62].…”

Section: Artificial Intelligence Algorithms 221 Anfis Architecturementioning

confidence: 99%

Comparative Study in Design of Fiber-Reinforced Concrete at Elevated Temperatures by Numerical Evaluation through Developed Hybrid Metaheuristic Algorithms

et al. 2023

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Fibrous concrete has good properties such as high ductility, high strength, suitable energy absorption and cracking resistance, which can be useful in many applications. This type of concrete is one of the best materials used in the construction of impact-resistant masonries, such as burial masonry structures, and explosive masonry warehouses. In this study, an artificial intelligence assessment based on the experimental test data from a laboratory has been performed on the fibrous concrete to evaluate the behavior of the samples at elevated temperatures and determine the most governing parameter on the mechanical properties of the fibrous concrete at elevated temperatures. For the first time, a hybrid intelligence algorithm has been developed based on the neural network structure using both genetic and swarm optimization algorithms. The ANFIS-PSO-GA (APG) algorithm was trained with experimental data and evaluated the flexural load and deflection of the samples. In order to detect the most prominent feature in the fire resistance of the fibrous concrete, five different subdatasets were designed. The results of the APG algorithm have been challenged with the ANFIS-PSO algorithm, which is a well-known hybrid numerical evaluation algorithm. As per the results, the newly designed APG algorithm has been successfully performed on both deflection and flexural prediction phases. Based on the numerical achievements, fiber features such as the fiber content and fiber mechanical properties are governing factors on the fibrous concrete resistance at elevated temperatures.

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“…The parameter S2, produced during pyrolysis, is the most useful for estimating the hydrocarbon generative potential (Dang et al, 2023;Peters and Cassa, 1994;Tissot and Welte, 1984;Wu et al, 2022). According to Bordenave et al (1993) and Shah and Abdullah (2017), a good petroleum generation capacity requires a minimum of 5 mg HC/g S2.…”

Section: Total Organic Carbon and Pyrolysismentioning

confidence: 99%

Integrated Interpretation of Eocene-Palaeocene Rocks in Potwar Basin, Pakistan: Implications for Petroleum Generation

Shah

2023

Earth sci. res. j.

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This study demonstrates how an integrated geochemical and petrophysical analysis can be used to evaluate the petroleum generation potential of source and reservoir rocks. The Eocene and Palaeocene sequences of the Potwar Basin, located in the upper Indus Basin of Pakistan, were analyzed. Well logs and Schlumberger log interpretation charts were used for the petrophysical analysis of the Chorgali Formation's reservoir potential. Geochemical methods were applied to 34 well-cutting Sakesar and Patala formations samples. Results from Vitrinite Reflectance (VR) (%Ro) and Tmax data suggest that both formations have reached the peak of their oil generation window. The Sakesar Formation has a mean TOC of 1.88 wt. % and HI values of 375 mg HC/g TOC. The Patala Formation has a mean TOC of 3.33 wt.% in well A and HI values ranging from 2.4 to 369 mg HC/g TOC in well B, with a mean TOC of 3.52 wt%. Both formations have mixed Type II/III kerogen. The findings indicate that both the Sakesar and Patala formations possess good oil/gas-generation potential and may act as source rocks in the Potwar Basin. Petrophysical analysis of the Chorgali Formation shows an average porosity of 10.32%, water saturation of 36.14%, and hydrocarbon saturation of 63.85%. This indicates that the Chorgali Formation has an average to good reservoir potential. The research findings will aid exploration and production companies in the Fimkassar Oilfield.

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Influence of source uncertainty on stochastic ground motion simulation: a case study of the 2022 Mw 6.6 Luding, China, earthquake

Cited by 17 publications

References 43 publications

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

Comparative Study in Design of Fiber-Reinforced Concrete at Elevated Temperatures by Numerical Evaluation through Developed Hybrid Metaheuristic Algorithms

Integrated Interpretation of Eocene-Palaeocene Rocks in Potwar Basin, Pakistan: Implications for Petroleum Generation

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