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排序方式: 共有223条查询结果,搜索用时 125 毫秒
51.
“九五”地磁数字化改造项目仪器配置分为两个部分,绝对观测仪-CTM-磁力仪和相对记录仪-GM3磁通门磁力仪,大连台自仪器装备以来,经过精心安装调试与观测摸索出一套仪器维护与高精度观测的经验,资料精度逐步达到规范要求,2001年11月9日通过验收,被专家组评价为“达到国内领先水平”。 相似文献
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Magnetic mapping for the detection and characterization of UXO: Use of multi-sensor fluxgate 3-axis magnetometers and methods of interpretation 总被引:1,自引:0,他引:1
Marc Munschy Daniel Boulanger Patrice Ulrich Mustapha Bouiflane 《Journal of Applied Geophysics》2007,61(3-4):168-183
Fluxgate vector magnetometers can be powerful instruments in magnetic mapping and in the detection of unexploded ordnance (UXO). Being lightweight and having low power requirements, they allow the development of multi-sensor use. The main problems in using fluxgate magnetometers arise from calibration errors and drift but these can be overcome using a quick and simple method of calibration in the field. This method also has the advantage of compensating permanent and induced magnetic fields generated by magnetized objects carried with the sensors. Measurement accuracy is similar to that obtained with scalar magnetometers. Multi-magnetometer profiling allows direct inversion of raw magnetic data along profiles in order to locate and characterize dipoles typically generated by UXO. The method used is a non-linear inverse procedure that estimates the three coordinates and magnetization vector of the dipole. Application of the technique to both a synthetic case and a field example illustrates the advantages of the method compared with the use of the analytic signal. 相似文献
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分量质子旋进磁力仪观测误差分析 总被引:1,自引:1,他引:0
朱兆才 《地震地磁观测与研究》2000,21(2):48-53
分析了影响质子旋进磁力仪和分量质子旋进磁力仪观测质量的因素 ,给出了分量质子旋进磁力仪补偿线圈电流允许误差、补偿线圈定向 (特别是线圈磁南北方向 )应满足的关系式和定向允许误差。提出了分量质子旋进磁力仪的使用要点 相似文献
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Mahesh Pal 《国际地质力学数值与分析法杂志》2006,30(10):983-996
This paper investigates the potential of support vector machines (SVM)‐based classification approach to assess the liquefaction potential from actual standard penetration test (SPT) and cone penetration test (CPT) field data. SVMs are based on statistical learning theory and found to work well in comparison to neural networks in several other applications. Both CPT and SPT field data sets is used with SVMs for predicting the occurrence and non‐occurrence of liquefaction based on different input parameter combination. With SPT and CPT test data sets, highest accuracy of 96 and 97%, respectively, was achieved with SVMs. This suggests that SVMs can effectively be used to model the complex relationship between different soil parameter and the liquefaction potential. Several other combinations of input variable were used to assess the influence of different input parameters on liquefaction potential. Proposed approach suggest that neither normalized cone resistance value with CPT data nor the calculation of standardized SPT value is required with SPT data. Further, SVMs required few user‐defined parameters and provide better performance in comparison to neural network approach. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Summary. A hardware configuration and program are described for interfacing a DIGICO spinner rock magnetometer to an APPLE microcomputer. This configuration provides full control of measurements and permits storage of the results on floppy or hard discs. A similar configuration can be used to operate the anisotropy unit. 相似文献
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The support vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. In this paper SVM models are developed for predicting the ultimate axial load-carrying capacity of piles based on cone penetration test (CPT) data. A data set of 108 samples is used to develop the SVM models. These data were obtained from the literature containing pile load tests and each sample contains information regarding pile geometry, full-scale static pile load tests and CPT results. Moreover, a sensitivity analysis is carried out to examine the relative significance of each input variable with respect to ultimate strength prediction. Finally, a statistical analysis is conducted to make comparisons between predictions obtained from the SVM models and three traditional CPT-based methods for determining pile capacity. The comparison confirms that the SVM models developed in this paper outperform the traditional methods. 相似文献