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Abstract Large variations in the maximum earthquake magnitude ( $$M_{{\text{max}}}$$ M max ) have been observed among the world’s subduction zones. There is still no universal relationship between $$M_{{\text{max}}}$$ M max and a given subduction-zone parameter, such as plate age, plate dip angle, or plate velocity, which suggests that multiple parameters control $$M_{{\text{max}}}$$ M max . Here, we conduct exhaustive variable selections that are based on three evaluation criteria; leave-one-out cross-validation errors (LOOCVE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) to determine the combination of subduction-zone parameters that best explains $$M_{{\text{max}}}$$ M max . Multiple linear regression analyses are applied using 18 subduction-zone parameters as potential candidates for the explanatory variables of $$M_{{\text{max}}}$$ M max . The minimum BIC is obtained when five variables (trench sediment thickness, existence of an accretionary prism, upper-plate crustal thickness, bending radius of the subducting oceanic plate, and trench depth) are selected as explanatory variables; each variable contributes positively to $$M_{{\text{max}}}$$ M max . Minimum LOOCVE and AIC values are obtained when eight variables (the five parameters for BIC, plus the along-strike plate convergence rate, age of the subducting plate, and maximum depth of the subducting plate) are selected. Our selection of the trench sediment thickness and plate bending radius contributing to $$M_{{\text{max}}}$$ M max is consistent with previous studies. The results show that increasing upper-plate crustal thickness results in a large $$M_{{\text{max}}}$$ M max . In addition to smoothing the subducting-plate interface via subducted sediments, along-dip extension of the crustal area along the convergent plate boundary would be important for generating a large earthquake. Graphic Abstract

The potential mechanisms that drive the behavior of subducted oceanic plates at the 660-km discontinuity are subject to debate. Here we conduct logistic regression analysis and model selection to determine the key subduction-zone parameters in natural subduction zones that discriminate the plate behavior along the discontinuity. We select the key variables based on three information criteria: leave-one-out cross-validation score (LOO), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Among the 17 subduction-zone parameters analyzed, only the trench velocity, convergence rate, and trench width are selected in the simplest model that minimizes BIC. The thermal parameter and several other variables are also selected to minimize AIC and LOO. Our results suggest that a stagnant slab occurs along the 660-km discontinuity when there is a narrow oceanic plate and a retreating trench in natural subduction zones, which has also been modeled in previous numerical simulations. Neither the stress nor the deformation rate of the upper-plate margin is selected in the three optimal models, which suggests that back-arc spreading in natural subduction zones does not globally characterize plate behavior at the 660-km discontinuity, although back-arc spreading and a stagnant slab coincide in some numerical simulations. The combination of subduction-zone data analysis and numerical simulations will therefore provide deep insights into the dynamics of Earth’s deep interior.

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability.