## 1 Introduction  
Ionospheric sounding is a technique for investigating the behavior and properties of the ionosphere, a component of the Earth's upper atmosphere. Ionospheric disturbances are becoming recognized as potential predictors of seismic activity as significant disturbances have been widely observed by various techniques before the occurrence of earthquakes [1, 2, 3]. During pre-earthquake stages, the Earth's energy balance is disrupted due to the release and emission of thermal energy [4, 5], or gases such as radon, caused by fault activation [6]. Disturbances in the lithosphere move across the geospheres (lithosphere, cover sphere, atmosphere, and ionosphere), leading to traveling anomalies in each sphere [7]. The particular mechanics causing the electric discharges are not well understood, and several theories have been proposed to explain their occurrence, such as them occurring before an earthquake due to increased stress and strain in the active fault zone, causing rocks to generate a rise in electric discharges through a phenomenon called piezoelectricity. Piezoelectricity occurs at the atomic level when ions are displaced within a soil or rock's crystal lattice as a result of stress. This displacement induces an imbalance of electric charges, which generates a voltage across the material [8]. One concept proposes that stress-induced electrical charges within rocks, known as p-holes, have the capacity to accumulate. As p-holes from the earthquake's hypocenter gather on the ground surface, they can alter the Earth's electromagnetic field and ionize air molecules at the ground-atmosphere interface. This ionization process can produce a variety of abnormal occurrences in the atmosphere and ionosphere [9; 10]. When the tension reaches a critical level, the stored charge is suddenly released, resulting in an electric discharge that alters the near-surface atmospheric electric field [11]. Additionally, the build-up of p-holes on the surface is thought to lower the microwave dielectric constant of the ground and increase regional microwave radiation [12; 13]. An alternative theory posits that the mechanical fragmentation of rocks during seismic activity, coupled with the release of radon gas previously trapped within the soil and crust, can lead to a series of seismic anomalies. The decay of radon gas contributes to the ionization of the surrounding air, thereby inducing various abnormal perturbations in aerosol concentration, atmospheric humidity, latent heat, and ionospheric plasma. Seismic-related abnormalities in radon emission can be linked to pre-seismic electromagnetic phenomena, such as substantial changes in ion concentration and the atmospheric electric field [14]. These alterations in the atmospheric and ionospheric conditions facilitate the formation of charged aerosols, which in turn play a role in the generation of electric discharges during seismic events [15]. Recent studies have highlighted the growing efficiency of observational techniques in detecting ionospheric disturbances associated with earthquakes using advanced machine learning models, such as autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), the nonlinear autoregressive network with exogenous inputs (NARX) model, and remote sensing techniques [16; 17]. These techniques include the use of various tools such as the Global Navigation Satellite System (GNSS) [18] and electromagnetic satellites [19; 20]. Scientists have adopted hybrid machine learning approaches for TEC predictions, demonstrating the compatibility of deep learning in dealing with complex ionospheric data [21; 22; 23]. Notably, the effectiveness of LSTM approaches has been demonstrated to predict TEC values with a time of 24 h [24]. Deep learning methods can be applied to construct the TEC reference background, thus contributing to a better extraction of ionosphere disturbances. Over the past few decades, numerous studies have demonstrated a direct or indirect relation between the occurrences of geohazards and ionospheric disturbances.