# Aveiro Canyon Head (Portugal) Submarine Slope Instability Assessment
## 4 Data Acquisition and Processing
### Predisposing Factors
Both classes represent a likely fragility zone with a sudden change in the inclination of the canyon slope, and therefore more prone to movements. Interestingly, on the other hand, the 25-30\({}^{\circ}\) and 30-35\({}^{\circ}\) classes scored negatively. This may be due to the following reasons: the area where the sea bottom surface might be more stable; and/or low resolution of the DBM that masked some slides and therefore, they are not included in the model. Regarding the aspect (slope face exposure) factor, the highest scores were obtained in the classes corresponding to the Southeast and East direction (Table 2). The classes concerning the South and Northeast orientations also appear with considerable scores. Seafloor curvature of the slopes presents the concave class as the most prone to the occurrence of slides (Table 2). This class has a relationship with the type of instabilities that were detected in the present study. Rotational slides, or slumps, have the particular characteristic of displaying a concave surface, which corroborates the score obtained for this class. The last predisposing factor analysed, regarding the superficial sediment characteristics, indicates that the type of sediment most prone to instability is fine silt, followed by medium silt (Table 2). In fact, according to the landslides inventory, only these types of sediment showed instabilities. However, it should be noted once again that this map is an extrapolation of a limited database of the available sample points. In summary, considering the scores obtained (Table 2), the most favourable conditions for triggering downslope mass movements in the Aveiro Canyon are as follows:  
1. depths larger than 900 m;
2. mean slope angle between 5\({}^{\circ}\) and 20\({}^{\circ}\), and above 40\({}^{\circ}\);
3. slope face exposed to the east quadrant (E-SE);
4. bottom surface with concave profile;
5. sedimentary deposits composed by medium to fine silt. Relevance of the predisposing factors in the construction of the logistic regression (LR) predictive model, which consists in adding, step by step, each factor according to its importance, indicate that the most relevant factor was \"Bathymetry\", followed by \"Mean slope\", \"Aspect\", \"Sediment cover\" and \"Curvature\" being the last one added. As for the IV approach, the relevance of each of the predisposing factors and their influence was tentatively analysed by: (1) calculating the absolute value of each score (Ii), obtained in each of its classes, and subsequently calculating the mean of the absolute values of the scores of the variables of each factor; (2) and calculating a success rate AUC. The results obtained (Table 3) indicate that, according with the mean absolute Ii values, the factor that has the higher influence in the occurrence of instability is \"Sediment cover\", followed by \"Bathymetry\". \"Mean slope\" is the factor that has the lowest preponderance. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Factors** & **Ii Absolute Mean Values** & **Success Rate Curve AUC** \\ \hline Bathymetry & 0.8315 & 0.7930 \\ Mean slope & 0.1876 & 0.6553 \\ Aspect & 0.1888 & 0.6030 \\ Curvature & 0.4742 & 0.5838 \\ Sediment cover & 1.7697 & 0.7120 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Absolute mean values and area under the curve (AUC) values for each one of the predisposing factors, based on the information value (IV) method. Analysing the results, the IV and LR models that include all factors, provided AUC values of 0.7908 and 0.8296 respectively (Table 4). From the analysis of the results obtained, it is clear that LR (AUC of 0.8296) produced more consistent results than the IV (AUC of 0.7908). This statement was somewhat expected, since the LR model considers the relationships between factors, rather than the individual evaluation. In that sense, models computed with IV method are classified as \"acceptable\", while the ones produced with the LR method are classified as \"very satisfactory\" [44].