## 2 Experimental Principles
### Experimental Procedure and Principle  
According to the different test conditions, it was divided into the no lens-shaped aquifer test group, the low-permeability lens test group, and the high-permeability lens test group. Firstly, the two sides of the sand tank partition and the bottom drain were placed in the screen to prevent the loss of sand and soil. Uniformly fill the main medium and compact every 10 cm, with a total of 55 cm filled. A lens body (20 \(\times\) 20 \(\times\) 5 cm) is placed at a height of 45 cm, and a leakage trough is placed at a height of 50 cm. A peristaltic pump is used to continuously and quantitatively pump 4600 mL of oil into the leakage trough of the sand box at a speed of 4 rpm. A camera is placed outside the sand box, taking a photo every 15 min to record the migration and redistribution process of LNAPL at different times. The images are then redrawn into digital images using CorelDRAW 2018 software. Twenty-four electrodes (1 \(\times\) 24) are arranged on top of the sand box with an electrode spacing of 4 cm, measuring in seven layers, using a concentrated cable. The power supply time is 500 ms, the power-off time is 50 ms, the gradient threshold is 100 mv/s, the overload delay is 500 ms, and data is collected every 15 min. The dipole-dipole arrangement measurement method (Figure 2) was used to calculate the resistivity \(\rho\) by supplying power downward through the power supply electrodes A and B, and measuring the potential difference between the measurement electrodes M and N. The calculation formula is as follows:  
\[\rho=\text{K}\frac{\Delta\text{U}}{\text{I}} \tag{1}\]  
\[\text{K}=\frac{2\pi}{\frac{1}{\text{AM}}-\frac{1}{\text{AN}}-\frac{1}{\text{ BR}}+\frac{1}{\text{BN}}} \tag{2}\]  
where \(\rho\) is the resistivity (\(\Omega\cdot\)m), K is the geometry coefficient, \(\Delta\)U is the observed potential difference (V), I is the supply current, and AM, AN, BM, BN are the distances (m) between the corresponding electrodes. The measured resistivity was called apparent resistivity. To determine the true resistivity values of the subsurface, apparent resistivity datasets undergo data inversion [30]. In order to clearly reflect the resistivity characteristics of the LNAPL during the migration process, the background resistivity of the sandbox medium was measured before the leakage occurred, followed by time-shifted differential inversion. Time-lapse inversion of the obtained ERT data was carried out using software Res2 DINV [31]. The high-density resistivity model inversion using the least-squares method with 5 iterations and an error value selected to be less than 10% was as follows:  
\[\left(J^{T}+uf\right)d=J^{T}g \tag{3}\]  
\(J\) is the partial derivative matrix; \(J^{T}\) is the transpose matrix of \(J\); \(u\) is the damping factor; \(d\) is the modification vector of the model parameters; \(g\) is the residual vector, which is the difference between the measured and simulated logarithmic values of the calculated resistivity; and \(f\) is the smoothing matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Diesel Type**} & **Densities** & **Dynamic** & **Surface** & **Combustion** & **Boiling Point** \\  & **(g/mL)** & **Viscosity** & **(cst)** & **(dyne/cm)** & **Point** (\({}^{\circ}\)C) & **(\({}^{\circ}\)C)** \\ \hline
0\# & 0.84 & 2.54 & 27.8 & 220 & 180–370 \\ \hline \end{tabular}
\end{table}
Table 2: Basic Physical Properties of Diesel Fuel.