# The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
## 4 Results
### Machine Learning for Point Classification  
Despite long historical research work, the distribution pattern of lava tubes remains unclear. One clue to reveal the pattern is the involvement with low viscosity lava flow, known as pahoehoe lava [56]. However, as shown in Figure 1, the spatial extent of pahoehoe lava flow and distribution of lava tubes are not highly correlated. Therefore, we tried to constrain the potential distribution of lava tubes using other evidence. We created a density map of lava tubes (179 in total) with the weighted values of their lengths and the co-kriging interpolation as shown in Figure 5b. The other useful information to spot the subsurface lava cavity is the existence of a permeable geological structure (PGS) so-called \"Sumgol\", which assigns the collapse locations of the lava cavity or the vertical joints or cracks over lava flows [57]. It is presumably related to the distribution of undiscovered lava tubes. We digitized the PGSs and applied the distance transformation with the city block sampling method [58] as shown in Figure 5d. The convolution of two spatial distributions was proposed to represent subsurface cavities by lava flow. The convolution corresponds to the simple additions of the two materials as we do not apply any weighting. The gridded data sets assigned as the distribution of PGS and the footprints of lava tubes were employed to further process InSAR data sets as a constraint. The other data sets extracted from the geospatial information were used for the ground truth and validation. The details are described in Section 5. Lava tube instability has been also identified by the structural analyses employing Finite Element Method (FEM) in [59; 60] and demonstrated in the fieldwork reported in [44]. As surface deformation was evident, InSAR time series analysis capable of observing a wide area was proposed to observe displacement induced by lava tubes. In particular, PS velocities present LOS directional behaviors on localized landscape objects; thus, PS was used to identify the LTDP and associated risks. Meanwhile, the sinkholes detected by InSAR analyses previously performed by [46; 47; 61] were proposed as comparable study cases as the cavities induced by underground sinkholes and lava tubes would cause similar surface deformations. Although we were able to extract a large number of deformation candidates based on the InSAR analysis results, the technical difficulties in discriminating  
Figure 5: Spatial analyses to create processing mask. (**a**) Lava tube distributions and (**b**) density map of lava tubes calculated with the weighted tube length. (**c**) PGS distributions and (**d**) distance transformation map of PGS. potential LTDP remain as follows: (1) there are false LTDP caused by other factors such as regional subsidence, thermal dilations, and failures of InSAR algorithms; (2) the behavior of LTDP is not clear, as we do not have precise ground truth; (3) the domains to be tested as target LTDP are enormous because the PS observations included more than 200,000 points all over Jeju Island. To address these difficulties, we introduced machine learning methods. First, we built the training datasets based on the spatial analyses and the geological/demographic contexts. The established ground truth data sets were then feedforwarded to the training stage of the proposed machine learning methods. Subsequently, we classified all PS target points using trained machine learning platforms. The extracted points as LTDPs were re-analyzed in the comparison of background context and validation data sets. Table 2 and Figure 6 show the standards to define training data sets and their locations. As [44] described, the road crossing with lava tubes or the cavities on lava flows often caused the roof failures of corresponding lava tubes/cavities. We investigated the deformation patterns of PS observations on road-crossing points with Manjang cave and all PGSs.