# Recent Advances and Challenges in the Seismo-Electromagnetic Study: A Brief Review
## 2 Seismo-Electromagnetic Phenomena in the Lithosphere
The electromagnetic phenomena associated with the 2000 Izu earthquake swarm may be one of the most plausible results, as many reports have been published using different kinds of methodologies, such as PCA [44; 45], singular spectral analysis (SSA) [46], fractal analysis [31; 47; 48], spectral density analysis [49], and direction-finding analysis [26]. In general, in addition to the above methods used for seismo-electromagnetic anomaly recognition, there are also other methods such as polarization analysis [49; 50], wavelet analysis [51], diurnal variation analysis [35], and so on. Whether these magnetic anomalies contain precursory information and how they can improve the forecasting of sizable earthquakes were not fully discussed. A sensitive geomagnetic network was installed in Kanto, Japan, to verify and clarify the ULF electromagnetic phenomena preceding large earthquakes. Plenty of data associated with moderate-large earthquakes have been accumulated. Researchers have analyzed the geomagnetic data observed during the past decade in the Izu and Boso Peninsula. [PERSON] et al. [52] conducted some statistical studies of ULF seismo-magnetic phenomena in these areas and verified the correlation between ULF magnetic anomalies and sizeable local earthquakes. Previous studies showed that the earthquake-related magnetic signals may decrease with the epicentral distance and increase with the magnitude of the earthquake event [12; 13; 30; 53], suggesting that augmenting earthquake forecasting with magnetic anomalies might involve their distance and magnitude dependences. The performance of trial forecasts based on ULF geomagnetic anomalies from the Kakioka Geomagnetic Observatory was evaluated by [PERSON] et al. [13]. They used the deep-night 0.01 Hz band data to avoid man-made noise, and the results showed that approximately 20% of the target earthquakes were preceded by the hypothesized type of magnetic anomaly related to the earthquake's occurrence. The authors rejected the concern that the successful forecasts were aftershocks and post-seismic disturbances of earthquakes, by demonstrating that trial forecasting based on recent seismicity is no better than random. Recently, [PERSON] et al. evaluated the optimal parameter for earthquake forecasting in the Kanto region, Japan [10], by utilizing long-term magnetic data observed in the region during the period 2000-2010 and applying [PERSON]'s error diagram [54; 55]. In the United States, a research project named QuakeFinder (QF) is carried out by the aerospace company Stellar Solutions. The goal was to develop a methodology to detect and analyze pre-earthquake electromagnetic (EM) signals in order to produce earthquake forecasts. As of April 2018, the array comprised 168 stations, most located in California, along the San Andreas Fault, and some located in Peru, Chile, Greece, and Sumatra [56]. The researchers discovered increases in ultra-low frequency magnetic pulse activity starting two weeks before the nearby seismic events in some cases and disappearing after the event [57]. The discovery is consistent with the statistical results of ULF seismo-magnetic phenomena in Japan [13]. In addition to the single earthquake's electromagnetic signals, some studies used 15+ years of data to statistically test whether electromagnetic signals do exist before significant earthquakes (close to the magnetometer stations) [58; 59]. The algorithmic framework for investigating the temporal relationship of magnetic field pulses and earthquakes has been proposed, and some studies of searching for pre-earthquake signals such as long-term air ion monitoring have been investigated [58]. Recently, the case-control study on a decade of ground-based magnetometers in California conducted by QF and Google revealed signals in the frequency domain of 0.016-25 Hz before earthquakes [59], and the artificial intelligence method used can improve the efficiency and accuracy of anomaly detection. When compared to the statistical results of papers by [PERSON] et al.