# 2-D Cross-Plot Model Analysis Using Integrated Geophysical Methods for Landslides Assessment
## 2 Methods
### 2-D Cross-Plot Analysis Model Development  
Cross-plot analysis widely implements in the oil and gas industry for formation evaluations and lithological delineations [64; 65; 66; 67]. This method applies multiple input parameters, such as electrical resistivity, nuclear, and acoustic logging in determining lithological reservoir characteristics. [PERSON], 2011, used cross-plot analysis to integrate the S-wave velocity and resistivity result as qualitative vulnerability assessment in producing a map of permeability distribution and \(N\)-value [68]. [PERSON] and [PERSON], with [PERSON] et al., have shown a similar application of cross-plot analysis where degrees of compaction (loose or dense) are divided using correlations between S-velocity values and \(N\)-values, and soil types by comparing to borehole data [69; 70]. Cross-plot analysis between seismic refraction and 2-D resistivity is an analytical approach where different soil type subsurface conditions are classified based on their seismic velocity and resistivity values [71]. Instead of individually analyzing the result from each method, four-quadrant criteria, based on the ranges of seismic velocity and electrical resistivity, are introduced. Based on the measured  
Figure 3: Schematic diagram of seismic refraction. Figure 2: Electrode arrangement for resistivity array of pole-dipole [59]. seismic velocity and resistivity at a given location, and where that point falls within four quadrants (Q1-Q4), the soil types and vulnerability of the subsurface is estimated. A new model of cross-plot analysis was introduced in integrating two different geophysical parameters as a goal to enhance the subsurface resolution using P-wave velocity and resistivity data. Based on the previous Hayashi model, four quadrants used to divide the parameters. Figure 4a shows the schematic relationship between P-wave velocity and resistivity data. In this research, the cross-plot analysis was implemented to integrate both resistivity and seismic refraction results, which were in line with each other. The goal of this analysis is to provide detailed subsurface images so as to enhance the visualization of the features below since determining the failure zone was a very important part in slope behaviors. The end results provided the 2-D cross plot model for slope area, where the subsurface characteristics, such as boulders, saturated zones, weakness zones, bedrock, and other features, were well visualized. In general, three stages of processing were performed: geometry correction, graphical, and datum point analysis and clustering. In geometry correction, the datum point for each result was re-arranged based on the position of x and y that fall in the same position. The datum point, which does not fall at the same position of x and y, were removed from the data. After all the data were ensured to be precisely (or nearly) the same with the position of x and y locations for both data, the graphical and datum point analyses were performed to plot the resistivity-velocity graph. At this stage, the data, with a value much bigger than average range data, were removed. Clustering was performed to sort the data based on the ranged values of the quadrant from the scattered plot of the resistivity-velocity graph. Four quadrants (Q1-Q4) were assigned and have been sorted accordingly based on the quadrant limit of the resistivity and velocity. Each quadrant represents the different values with different characteristics, as shown previously in Figure 4a. In the clustering method, the sorted option in Microsoft Excel was utilized to categorize all of the data based on the quadrant ranged. Figure 4b shows the resistivity-velocity graph based on the quadrant range. All of the data for each quadrant were output as a (.xlsx) file and plotted as a 2-D model, using a post-map function in Surfer8 tools. The model was plotted accordingly to positions x and y of the data. Figure 5 shows the workflow of the cross-plot analysis.