From 1997 to 2006, the Mars Global Surveyor (MGS) spacecraft provided magnetic field measurements while orbiting Mars, extensively sampling the magnetic field at an altitude of about 400 km (Acuña et al., 1998) after periapsis was raised upon completion of the aerobraking phase. The MGS mission discovered that Mars possesses many localized remanent magnetic fields, which most likely originate in the Martian lithosphere (Acuña et al., 1999). Remanent magnetic fields, otherwise known as crustal fields or lithospheric magnetic fields, are widely believed to be induced by an ancient core dynamo. Mars currently does not have a global dipole magnetic field as in the case of Earth and Mercury (Langlais et al., 2010). The most intense crustal fields of Mars are located in the Southern Hemisphere. These fields are 1 to 2 orders of magnitude stronger than the crustal fields on Earth (Kother et al., 2015;Voorhies et al., 2002), 3 to 4 orders of magnitude stronger than the crustal fields on Moon (Purucker & Nicholas, 2010) and Mercury (Johnson et al., 2015).

Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.