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Ionospheric anomalies related to the Mw 6.5 Samar, Philippines earthquake

Eroglu Emre

Acta Geophysica

2023

Vol. 71, no. 2

601--611

Models belonging to the ionosphere that is directly affected by factors such as solar activity, geomagnetic storm, earthquake, seasonal changes, and geographical location need to be considered altogether. In this sense, the cause of the ionospheric anomalies should be meticulously distinguished from each other. Ionospheric anomalies that occur before or (and) after an earthquake have a serious place in earthquake prediction studies. Total electron content (TEC) is one of the significant parameters to be able to discuss the anomalies of the ionosphere. This essay investigates ionospheric anomalies before and after the Mw 6.5 Samar, Philippines (12.025° N, 125.416° E and November 18, 2003, at 17:14 UT) earthquake. The paper analyzes anomalies with the aid of the TEC (TECU) map. In the paper, the time-domain TEC variables are transferred to the frequency-domain for observing some clues-peaks by short-term Fourier transformation spectral analysis. The discussion handles the effect of the solar activity with the F10.7 (sfu) index and the effect of geomagnetic storms with Bz (nT), v (km/s), P (nPa), E (mV/m), Kp (nT), and Dst (nT) parameters (index). The lower and upper boundaries of the TEC map obtained from the International Reference Ionosphere (IRI-2016) are calculated with the help of median and standard deviation. The boundary-setting process is named statistical analysis. TEC data exceeding the boundaries are marked as anomaly data. According to the paper, 11-day anomalies (9-day of which belong to pre-earthquake) are detected. Probably, the anomalies observed on November 6, 7, and 12 belong to the Samar earthquake.

3D crosswell electromagnetic inversion based on radial basis function neural network

Fang Sinan, Zhang Zhansong, Chen Wei, Pan Heping, Peng Jun

Acta Geophysica

2020

Vol. 68, no. 3

711--721

Crosswell electromagnetic (EM) method has fundamentally improved the horizontal detection ability of well logging and will become an increasingly promising approach for the secondary exploration of hydrocarbon reservoir. We applied orthogonal least squares (OLS) radial basis function neural network (RBFNN) based on improved Gram–Schmidt (G–S) procedure to three-dimensional (3D) crosswell EM inversion problems. In the inversion process of the simplifed crosswell model with single-grid conductivity anomalies and normal oil reservoir, compared the inversion results of other fve neural networks, OLS-RBFNN was proved to have the best global optimization ability and the fastest sample learning speed and the average inversion error of low conductivity anomalies model (4%) and oil reservoir model (9%) can meet the inversion requirements of crosswell EM method. Only the OLS-RBFNN could achieve ideal inversion results in the most concerned central area of crosswell model, and the inversion accuracy of this algorithm will be more outstanding when the model becomes more complex. Merely using the three-component time-domain crosswell EM data of two wells, the inversion of 3D medium conductivity in the crosswell dominant exploration area can be efectively realized through the nonlinear approximation of the OLS-RBFNN.