Following the recent establishment of a high-density seismic network equipped with low-cost micro-electro-mechanical system (MEMS) P-wave-alert-device (P-Alert) by the earthquake early warning (EEW) research group at the National Taiwan University, a large quantity of strong-motion records from moderate-magnitude earthquakes (M_L > 6) around Taiwan has been accumulated. Using a data preprocessing scheme to recover the dynamic average embedded within the P-Alert data, we adopted an automatic baseline correction approach for the P-Alert accelerograms to determine the coseismic deformation (Cd). Comparisons between the Cd values determined using global positioning system (GPS) data, strong-motion records from the P-Alert network, and data from the Taiwan Strong Motion Instrumentation Program (TSMIP) demonstrates that the near-real-time determination of Cd values (>2 cm), which provide crucial information for seismic hazard mitigation, is possible using records from low-cost MEMS accelerometers.

The seismo-electrical coupling is critical to understand the mechanism of geoelectrical precursors to earthquakes. A novel seismo-electrical model, called Chen–Ouillon–Sornette (COS) model, has been developed by combining the Burridge–Knopoff spring-block system with the mechanisms of stress-activated charge carriers (i.e., electrons and holes) and pressure-stimulated currents. Such a model, thus, can simulate fracture-induced electrical signals at a laboratory scale or earthquake-related geoelectrical signals at a geological scale. In this study, by using information measures of time series analysis, we attempt to understand the influence of diverse electrical conditions on the characteristics of the simulated electrical signals with the COS model. We employ the Fisher–Shannon method to investigate the temporal dynamics of the COS model. The result showed that the electrical parameters of the COS model, particularly for the capacitance and inductance, affect the levels of the order/disorder in the electrical time series. Compared to the field observations, we infer that the underground electrical condition has become larger capacitance or smaller inductance in seismogenic processes. Accordingly, this study may provide a better understanding of the mechanical–electrical coupling of the earth’s crust.

In Taiwan, most of the rivers in the Pingtung Plain are ephemeral stream. In the dry season, the water source is unstable and cannot be used all the year round. Due to the uneven distribution of time and space, the supply and demand of water resources are often imbalanced. In order to provide a stable groundwater source strategy, it is necessary to understand the geologic characteristics of groundwater.Electrical resistivity surveying methods have been widely used to determine the thickness and resistivity of layered media for the purpose of assessing groundwater potential and siting boreholes in fractured unconfined aquifers. In this study, we used CP configuration on the Electrical Resistivity Tomography (ERT) monitoring system at two study sites. One is the surface-borehole survey line at the Dashu, Kaohsiung City, Taiwan, and the other is the surface survey line at the Daliao, Kaohsiung City, Taiwan. Both of sites located on Quaternary alluvium of Pingtung Plain which composed of coarse sand and gravel.The resistivity difference might be caused by the dynamic of the groundwater. We analyzed the change in the electrical properties of the gravel layer during the rainfall season at the Dashu site and analyzed the groundwater level change by ERT method during the pumping event at the Daliao site which is the pumping station to understand the groundwater replenishment situation. The ERT result can be calculated Relative Water Saturation (RWS) of the shallow formation fluid, and it reveal the permeability of the gravel layer and the hydrogeological characteristics of the sites. Furthermore, we assumed the different particle size and porosity to estimate the resistivity and the hydraulic conductivity coefficient theoretical trend line, compare the observation well data is used to estimate the actual porosity and the actual hydraulic conductivity range. Finally, for the groundwater conditions in the large area of the Pingtung Plain, we use the theoretical trend line to analyze the data of WRG’s 34 wells in western Pingtung Plain. The results show (i) the well logging resistivity is positively correlated with the hydraulic conductivity. (ii) the sandstone and mudstones with small variables have smaller well-measured resistivity and a lower hydraulic conductivity. (iii) affected by compaction, the porosity tends to decrease with increasing depth. (iv) on the west side of the Pingtung Plain, the particle sizes are relatively consistent, and the hydraulic conductivity is 10 -3~10 -4 m/s.This study is using ERT to provide hydrological data analysis on small areas and large areas in the Pingtung Plain, and also contributes to groundwater operations and management

Modeling earthquake rupture dynamics often requires stochastic approaches to address the impracticality of obtaining analytical solutions for asymmetric many-body systems. Following Langevin's approach, we propose a stochastic dynamic model for the earthquake rupture process, where complexity in degrees of freedom is reduced by introducing a random force to account for uncertainties in fault plane heterogeneity and structural collisions. In this coarse-grained framework, the random term captures unresolved heterogeneity and interactions at a macroscopic system scale; it does not assert that rupture at the scale of specific fault patches is inherently random. Treating the tectonic process as a Coulomb friction process allows this Langevin equation to be viewed as a stochastic variant of Newton’s second law, attributing physical significance to the sample paths.However, applying a zero-dimensional (0-D) stochastic framework to complex faulting raises a critical conceptual challenge: can a model lacking explicit spatial dimensions reproduce the highly heterogeneous energy distribution observed in nature? Intuition suggests that the exponential slip distribution derived from a 0-D process may not exhibit tail behavior sufficient to satisfy the standard asperity criterion, where a small fraction of the fault area releases most of the seismic energy. To validate the physical basis of the model, we first examine the spectral properties of the synthetic velocity fluctuations. Results demonstrate that the model output is not arbitrary white noise; rather, the velocity spectra exhibit a Lorentzian form characterized by a single corner frequency. This spectral structure indicates that system memory is governed by a characteristic timescale determined by the load ratio, reflecting a competition between frictional dissipation (which erases memory) and external driving (which sustains motion).Furthermore, we evaluate the steady-state slip distribution derived from the corresponding Fokker–Planck equation against empirical scaling relations for asperities. Adopting the criterion which defines an asperity as regions where slip exceeds 1.5 times the average, and using squared slip as an upper-bound proxy for energy release under elastic loading, we calculate the theoretical energy concentration. The model predicts that the top ∼22% of the statistical "area" contributes ∼81% of the total energy. This theoretical prediction lies within the 20–30% range observed empirically for asperity area fractions. These findings suggest that the concentration of energy in asperities can emerge from stochastic frictional dynamics, arising from the exponential tail of the slip distribution without explicit modeling of spatial heterogeneity.

Geoelectric time series (TS) have long been studied for their potential for probabilistic earthquake forecasting, and a recent model (GEMSTIP) directly used the skewness and kurtosis of geoelectric TS to provide times of increased probability (TIPs) for earthquakes for several months in the future. We followed up on this work by applying the hidden Markov model (HMM) to the correlation, variance, skewness, and kurtosis TSs to identify two hidden states (HSs) with different distributions of these statistical indexes. More importantly, we tested whether these HSs could separate time periods into times of higher/lower earthquake probabilities. Using 0.5 Hz geoelectric TS data from 20 stations across Taiwan over 7 years, we first computed the statistical index TSs and then applied the Baum–Welch algorithm with multiple random initializations to obtain a well-converged HMM and its HS TS for each station. We then divided the map of Taiwan into a 16-by-16 grid map and quantified the forecasting skill, i.e., how well the HS TS could separate times of higher/lower earthquake probabilities in each cell in terms of a discrimination power measure that we defined. Next, we compare the discrimination power of empirical HS TSs against those of 400 simulated HS TSs and then organized the statistical significance values from this cellular-level hypothesis testing of the forecasting skill obtained into grid maps of discrimination reliability. Having found such significance values to be high for many grid cells for all stations, we proceeded with a statistical hypothesis test of the forecasting skill at the global level to find high statistical significance across large parts of the hyperparameter spaces of most stations. We therefore concluded that geoelectric TSs indeed contain earthquake-related information and the HMM approach is capable of extracting this information for earthquake forecasting.

Soil moisture plays a critical role in the growth process of rice, directly influencing crop growth and yield. This study focuses on how meteorological factors (net radiation, air temperature, and potential evapotranspiration) and plant factors (crop coefficient) impact the daily depletion of soil moisture across different growth periods of rice. The research is based on observational data from the second rice cropping season, 2023, in Guanyin District, Taoyuan City, Taiwan. A multiple linear regression model was developed to incorporate plant and meteorological factors and their influences on soil moisture at various depths. Additionally, a one-dimensional heat conduction model was utilized to analyze the interactions within the soil-plant-atmosphere continuum (SPAC) system. The results indicate that rice roots significantly impact the daily depletion of soil moisture at a depth of 20 cm. In comparison, the influence of meteorological factors stabilizes at depths of 30 to 40 cm. By integrating soil moisture data with meteorological and plant factors, this study compared the estimated thermal diffusivity and damping depth using a multiple linear regression model with values derived from in-situ soil temperature observations. The results show consistency, further validating the model's accuracy in assessing the influence of meteorological factors at various depths. This conceptual model improves the understanding of soil moisture, plant, and atmosphere interactions in rice growth. It also provides a robust scientific basis for estimating the daily depletion of soil moisture using plant and meteorological factors, which informs the optimization of water resource management and irrigation strategies customized to different growth periods. This research aims to enhance irrigation water use efficiency by providing dynamic changes in soil moisture, contributing to better water resource management and sustainability in rice agriculture.Keywords : Soil Moisture; Multiple Linear Regression Models; One-Dimensional Heat Conduction Model; Depth Effects; Rice Growth