Forecast earthquake precursor in the Flores Sea

Jufriansah, Adi; Anggraini, Ade; Zulfakriza, Zulfakriza; Khusnani, Azmi; Pramudya, Yudhiakto

doi:10.11591/ijeecs.v32.i3.pp1825-1836

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Formula 5 expresses the hyperplane that is applied to non-linear data using the kernel function. [15], [22], [26], [45]- [49]. In conclusion, Polynomial SVM has the best performance, but needs to be tested further to avoid overfitting, while Linear SVM also shows excellent performance in separating data linearly.…”

Section: Resultsmentioning

confidence: 96%

Forecasting the Magnitude Category Based on The Flores Sea Earthquake

Jufriansah,

Khusnani,

Saputra

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Earthquakes are a phenomenon that is still a mystery in terms of predicting events, one of which is the magnitude. As technology develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude forecasting, the application of GaussianNB, Random Forest, and SVM has the potential to reveal these patterns and relationships in the data. With the six main phases of this research, namely data acquisition, data pre-processing, feature selection, model training, forecast result evaluation, and performance analysis, this study is expected to contribute to the development of more accurate and effective earthquake forecasting methods. From these results we first obtain the result that the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the assumption of a Gaussian distribution, which may not always suit the complex and diverse characteristics of earthquake data. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and requires more time to compute. The third option is SVM, which has both benefits and drawbacks that must be taken into account. The capacity of SVM to separate data that has both linear and nonlinear separation is one of its key advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments.

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Section: Resultsmentioning

confidence: 96%

Forecasting the Magnitude Category Based on The Flores Sea Earthquake

Jufriansah,

Khusnani,

Saputra

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Seismic Background Noise Level and Station Detectability in the Flores Sea

Khusnani,

Jufriansah,

Anggraini

et al. 2024

Glob. J. Earth Sci. Eng.

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The Flores back-arc thrust fissure is a significant contributor to earthquake events in the Flores Sea region, as evidenced by seismic investigations. As part of the endeavor to mitigate earthquake risk, seismic data investigations are necessary due to the high potential for earthquakes in the Flores Sea. Background noise in earthquakes is the term used to describe the micro vibrations that are perpetually produced as a result of natural phenomena, such as ocean waves, wind, or human activities. It is crucial to investigate this cacophony in seismology in order to distinguish the primary earthquake signal. Its spectrum analysis can assist in the identification of land changes and the prediction of tectonic activity. This analysis was conducted by employing the Incorporated Research Institutions for Seismology (IRIS) client function as a fetch data tool and the Modular Utility for Statistical Knowledge Gathering Data browser as a data quality monitoring system to verify the health and reliability of seismic data. The three station sites closest to the Flores Sea are the focus of this research data examination. The study's findings indicate that the recorded data at the station is still dominated by cultural noise, as evidenced by the analysis of the probability density function, power spectral density, and noise spectrograms. Additionally, each station exhibits activity with degrees of probability noise that are both high and variable. These results highlight the need for advanced techniques to filter cultural noise and improve the accuracy of seismic signal interpretation in this region. This analysis contributes to understanding tectonic activity in the Flores Sea and underscores the importance of continuous monitoring for earthquake preparedness and risk reduction.

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Forecast earthquake precursor in the Flores Sea

Cited by 2 publications

References 48 publications

Forecasting the Magnitude Category Based on The Flores Sea Earthquake

Forecasting the Magnitude Category Based on The Flores Sea Earthquake

Seismic Background Noise Level and Station Detectability in the Flores Sea

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