# The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
## 4 Results
### Machine Learning for Point Classification
We choose Manjang cave as the most deformation-fragile case because of the shallow structure of the lava cave (see the following discussions), extensive length, and high interaction with tourist activities. On the contrary, PGS was originally created by the vertical failures of lava cavities, and the proximity to roads supposedly indicates instability and deformations. With 177 InSAR observations (77 on Manjang cave and 100 on PGS s) in road crossing areas, we discovered that the deformation patterns could be classified as (1) minus LOS migrations (\(<\)\(-\)1 mm/year) which imply vertical subsidence, so-called V-migration patterns, and (2) positive LOS migrations (\(>\)0.75 mm/year) which are almost impossible as vertical deformations due to the rocky surface of lava flow hence might refer to horizontal deflections, so-called H-migration patterns. Thus, 50 V-migrations and 17 H-migrations were assigned as the training data. It was noted that some minus LOS migrations might be induced by horizontal creep to the LOS direction in the SAR sensor. However, this portion in V-migration should be minor as the major contribution to LOS direction is up- and downward deformation [62]. Interestingly, around PGS, the majority of deformations (\(>\)70%) are V-migration, while H-migration occupied 50% of deformation points in Manjang cave. It fits the hypothesis that vertical failures in PGSs mainly induce V-migration patterns, but a large cavity such as Manjang produced the H/V-migration pattern together. Therefore, we constructed the training vectors of potential lava tubes combining two migration patterns together. Their behaviors included highly variable signals perhaps involved with seasonal effects as shown in Figure 7a,b, where changes possibly induced by thermal expansion are noticed in the summertime. H-migration patterns were introduced as the secondary indicator of the potential instability of the lava cavity. The stable PS points with no significant variation were excluded for the input data of machine learning applications. However, we observed that two kinds of deformation patterns might be misrecognized as genuine LTDPs. The first one is the deformation induced by regional subsidence, and the second is the structural deformation, such as newly built buildings. The regional subsidence is clearly observed in Seogwipo sediment, perhaps caused by regional condensations by heavy mass loading (see Figures 1 and 6). Therefore, the strong minus deformations (\(<\)\(-\)2 mm/year) in Seogwipo sediment were chosen as the training vectors of regional subsidence. The stable behavior is shown in Figure 7c, in which the variation is quite different from the PGS-road-crossing or tube-road-crossing regions. The instability of individual structures was chosen on the opposite side of Jeju City areas where large buildings are populated. Since there are many deformations with random changing patterns, the average behavior is uniform and is shown in Figure 7d. Because there are not enough training points, the pattern of H-movement varies (Figure 7e).