## 3 Results and Discussion
However, the method has notable limitations in high-noise environments or areas with strong lateral electrical heterogeneity: noise interference can significantly reduce the signal-to-noise ratio (SNR), while abrupt changes in electrical properties can cause the induced polarization response to deviate from simplified model assumptions, increasing the non-uniqueness in inversion solutions. Additionally, in areas with high grounding noise (e.g., clay-rich layers) or very low-resistivity overburden, SSIP performs poorly. The former greatly reduces the SNR and hampers the identification of dispersion features, while the latter restricts detection depth and induces shielding effects. In such scenarios, supplementary geophysical techniques or data optimization methods are necessary to improve interpretational reliability. In practical applications, SSIP faces various technical challenges. Electromagnetic coupling effects--including capacitive and inductive coupling--can significantly distort IP signals in low-resistivity regions or under large electrode spacing, leading to phase and amplitude frequency response distortions. For example, coupling effects in high-frequency ranges may completely obscure genuine IP responses. Data processing is also complex: although time-domain exponential function fitting can partially preserve IP information, it is sensitive to outliers such as anomalous time segments and requires optimization using frequency-domain analysis or additional filtering techniques. Frequency selection is particularly critical, as different ore types (e.g., massive versus disseminated ore) exhibit distinct characteristic frequencies. Improper frequency selection may result in weakened anomaly responses or even missed targets. Environmental factors such as increased distributed capacitance in humid areas and industrial stray currents further degrade data quality, necessitating corrective technical measures. To fully exploit the strengths of SSIP while overcoming its limitations, several key aspects must be addressed during field implementation. First, frequency selection and instrument configuration must be optimized based on the characteristics of the target deposit (low frequencies for massive ores, high frequencies for disseminated ores), and electromagnetic coupling suppression should be considered when determining working frequencies. During fieldwork, grounding resistance must be minimized by employing parallel electrode configurations or using water infiltration techniques to improve the grounding of current electrodes. Laying cables in moist environments should be avoided; where necessary, cables should be elevated above ground to reduce capacitive coupling. For data processing, it is advisable to combine time-domain fitting (e.g., exponential decoupling) with frequency-domain analysis (e.g., multi-frequency phase correction), and to incorporate machine-learning algorithms for automatic identification of anomalous data points, thereby enhancing interpretational accuracy. Moreover, survey results should be validated through drilling or known geological cross-sections and analyzed using multiple parameters (e.g., phase, dispersion rate, and relative phase) to avoid misinterpretation based on single-parameter analysis. In areas with strong industrial interference, measurements should be conducted during off-peak hours, and data averaging across multiple acquisitions should be employed to suppress noise. Regular instrument calibration is also essential to ensure data reliability.