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.

Unlike Earth, Mars does not possess an intrinsic global, dipolar magnetic field, which leads to the direct interaction of the Martian ionosphere/atmosphere with the solar wind and scavenging of the planetary ions (e.g., Luhmann & Brace, 1991, and references therein;Zhang et al., 2021). The "frozen-in" interplanetary magnetic field (IMF), carried by the solar wind plasma flow, is draped around Mars as solar wind approaches the planet. This draped IMF would then slip into a wake, resulting in an elongated induced magnetotail that is characterized by a pair of magnetic lobes having antiparallel field lines, together with a current sheet between the lobes, while this induced magnetotail configuration would be controlled by the IMF orientation (e.g.

In many countries, slope failure is a complex natural issue that can result in serious natural hazards, such as landslide dams. It is associated with the challenge of slope stability evaluation, which involves the classification problem of slopes and the regression problem of predicting the factor of safety (FOS) value. This study explored the implementation of machine learning to analyze slope stability using a comprehensive database of 880 homogenous slopes (266 unstable and 614 stable) based on a simulation model developed as a surrogate model. A classification model was developed to categorize slopes into three classes, including S (stable, FOS > 1.2), M (marginally stable, 1.0 ≤ FOS ≤ 1.2), and U (unstable, FOS < 1.0), and a regression model was used to predict the target FOS value. The results confirmed the efficiency of the developed classification model via testing, achieving an accuracy of 0.9222, with 96.2% accuracy for the U class, 55% for the M class, and 95.2% for the S class. When U and M are in the same class (i.e., the U + M class), the test accuracy is 0.9315, with 93.3% accuracy for the S class and 92.9% accuracy for the U + M class. The low accuracy level for class M led to minor inaccuracies, which can be attributed to a data imbalance. Additionally, the regression model was found to have a high correlation coefficient R-square value of 0.9989 and a low test mean squared error value of 5.03 × 10−4, which indicates a strong relationship between the FOS values and the selected slope parameters. The significant difference in the elapsed time between the traditional method and the developed surrogate model for slope stability analysis highlights the potential benefits of machine learning.

Using the Kepler Eclipsing Binary Catalog, we found seven EW-type eclipsing binaries within the tidal radius of the intermediate-aged open cluster NGC 6819 (about 40′). These seven EW eclipsing binaries are all confirmed to be contact binaries by light curve analysis with the 2015 version Wilson–Devinney program. Using the parameter characteristics of contact binaries, we found that only KIC 4937217 could be a member of NGC 6819. Moreover, KIC 5199489 should be a shallow, unity-mass-ratio contact binary implying an early contact stage or a mass-ratio reverse stage. Nevertheless, KIC 5198934 and KIC 5374883 should be deep, low mass ratio contact binaries (DLMRCBs), which are usually considered as premergers.