[1] A SHEINKER, B GINZBURG, N SALOMONSKI et al. Magnetic anomaly detection using high-order crossing method. IEEE Transactions on Geoscience and Remote Sensing, 50, 1095-1103(2012).

[2] G YIN, Y ZHANG, H FAN et al. Detection， localization and classification of multiple dipole-like magnetic sources using magnetic gradient tensor data. Journal of Applied Geophysics, 128, 131-139(2016).

[3] M DAVID WIGH, T M HANSEN, A DØSSING. Inference of unexploded ordnance （UXO） by probabilistic inversion of magnetic data. Geophysical Journal International, 220, 37-58(2020).

[4] V PAOLETTI, A BUGGI, R PAŠTEKA. UXO detection by multiscale potential field methods. Pure and Applied Geophysics, 176, 4363-4381(2019).

[5] H M KIM, C G HEO, S H CHO et al. Determination scheme for accurate defect depth in underground pipeline inspection by using magnetic flux leakage sensors. IEEE Transactions on Magnetics, 54, 1-5(2018).

[6] G YIN, L ZHANG. Three-dimensional reconstruction of a small-scale magnetic target from magnetic gradient observations. Journal of Magnetism and Magnetic Materials, 482, 229-238(2019).

[7] D A CLARK. New methods for interpretation of magnetic vector and gradient tensor data I： eigenvector analysis and the normalised source strength. Exploration Geophysics, 43, 267-282(2012).

[8] [8] 8尹刚， 张英堂， 米松林， 等. 基于倾斜角和Helbig方法的多磁源目标反演技术［J］. 上海交通大学学报， 2017， 51（5）： 577-584.YING， ZHANGY T， MIS L， et al. Multiple magnetic targets inversion technique based on tilt angle and helbig method［J］. Journal of Shanghai Jiao Tong University， 2017， 51（5）： 577-584.（in Chinese）

[9] J P LI, Y T ZHANG, H B FAN et al. Estimating the location of magnetic sources using magnetic gradient tensor data. Exploration Geophysics, 50, 600-612(2019).

[10] Q Z LI, Z Y SHI, Z N LI et al. Magnetic object positioning based on second-order magnetic gradient tensor system. IEEE Sensors Journal, 21, 18237-18248(2021).

[11] Q Z LI, Z Y SHI, Z N LI et al. Preferred configuration and detection limits estimation of magnetic gradient tensor system. IEEE Transactions on Instrumentation and Measurement, 70, 1-14(2021).

[12] A K JAIN, R P W DUIN, J C MAO. Statistical pattern recognition： a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 4-37(2000).

[13] G B HUANG, Q Y ZHU, C K SIEW. Extreme learning machine： theory and applications. Neurocomputing, 70, 489-501(2006).

[14] W E SNYDSMAN, F AMINZADEH, C B WEIL. Pattern recognition in geophysical exploration, 53-60(1987).

[15] C CALDERÓN-MACÍAS, M K SEN, P L STOFFA. Artificial neural networks for parameter estimation in geophysics. Geophysical Prospecting, 48, 21-47(2000).

[16] FERN&#. NDEZ J P， BARROWES B， O’NEILL K，. FERN&#, 6217, 25-32(225).

[17] C ANISH. A pattern recognition based approach to consistency analysis of geophysical datasets. Computers & Geosciences, 36, 464-476(2010).

[18] B EHRET. Pattern recognition of geophysical data. Geoderma, 160, 111-125(2010).

[19] [19] 19谢永茂. 基于模板匹配的局部磁异常识别方法［D］. 北京： 中国地质大学（北京）， 2012.XIEY M. Recognition of Magnetic Local Anomaly Based on Template Matching［D］. Beijing： China University of Geosciences， 2012. （in Chinese）

[20] [20] 20李昌珑， 李宗超， 吕红山， 等. 基于三维图像模式识别的西藏东南部地震灾害损失风险评估［J］. 地球物理学报， 2019， 62（1）： 393-410. doi: 10.6038/cjg2019M0360LIC L， LIZ C， LÜH S， et al. Seismic disaster loss risk assessment for southeastern Tibet based on 3D image pattern recognition［J］. Chinese Journal of Geophysics， 2019， 62（1）： 393-410.（in Chinese）. doi: 10.6038/cjg2019M0360

[21] J ZHENG, H FAN, Q ZHANG et al. Magnetic anomaly target recognition based on SVD and SVMs. IEEE Transactions on Magnetics, 55, 1-8(2019).

[22] J Y ZHENG, H B FAN, G YIN et al. A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD. AIP Advances, 9(2019).

[23] Z J ZHOU, M ZHANG, J F CHEN et al. Detection and classification of multi-magnetic targets using mask-RCNN. IEEE Access, 8, 187202-187207(2020).

[24] G B HUANG. An insight into extreme learning machines： random neurons， random features and kernels. Cognitive Computation, 6, 376-390(2014).

[25] J K XUE, B SHEN. A novel swarm intelligence optimization approach： sparrow search algorithm. Systems Science & Control Engineering, 8, 22-34(2020).

[26] H ABDI, L J WILLIAMS. Principal component analysis. Wiley interdisciplinary reviews： computational statistics, 2, 433-459(2010).

[27] [27] 27李青竹， 李志宁， 张英堂， 等. 磁梯度张量系统传感器阵列的快速旋转校准［J］. 光学 精密工程， 2018， 26（7）： 1813-1826. doi: 10.3788/ope.20182607.1813LIQ Z， LIZ N， ZHANGY T， et al. Fast rotation calibration of sensor array in magnetic gradient tensor system［J］. Opt. Precision Eng.， 2018， 26（7）： 1813-1826.（in Chinese）. doi: 10.3788/ope.20182607.1813

[28] [28] 28李青竹， 李志宁， 张英堂， 等. 张量衍生不变关系下的磁源单点定位［J］. 光学 精密工程， 2019， 27（8）： 1880-1893. doi: 10.3788/ope.20192708.1880LIQ Z， LIZ N， ZHANGY T， et al. Magnetic source single-point positioning by tensor derivative invariant relations［J］. Opt. Precision Eng.， 2019， 27（8）： 1880-1893.（in Chinese）. doi: 10.3788/ope.20192708.1880

[29] [29] 29马国庆， 李丽丽， 杜晓娟. 磁张量数据的边界识别和解释方法［J］. 石油地球物理勘探， 2012， 47（5）： 815-821， 844， 682.MAG Q， LIL L， DUX J. Boundary detection and interpretation by magnetic gradient tensor data［J］. Oil Geophysical Prospecting， 2012， 47（5）： 815-821， 844， 682.（in Chinese）

[30] DANIELA， GEROVSKA, DANIELA， GEROVSKA. Determination of the parameters of compact Ferro-metallic objects with transforms of magnitude magnetic anomalies. Journal of Applied Geophysics, 55, 173-186(2004).

[31] P STAVREV, D GEROVSKA. Magnetic field transforms with low sensitivity to the direction of source magnetization and high centricity. Geophysical Prospecting, 48, 317-340(2000).

[32] J ZHANG, W MA, J LIN et al. Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence. Measurement, 59, 73-87(2015).

[33] [33] 33孙世政， 于竞童， 韩宇， 等. 基于SSA-ELM的双层十字梁结构光纤布拉格光栅传感器三维力解耦［J］. 光学 精密工程， 2022， 30（3）： 274-285. doi: 10.37188/OPE.2021.0549SUNS Z， YUJ T， HANY， et al. FBG sensor of double-layer cross beam structure based on SSA-ELM three-dimensional force decoupling［J］. Opt. Precision Eng.， 2022， 30（3）： 274-285.（in Chinese）. doi: 10.37188/OPE.2021.0549

[34] YANLONG， ZHU, YANLONG， ZHU. Optimal parameter identification of PEMFC stacks using Adaptive Sparrow Search Algorithm. International Journal of Hydrogen Energy, 46, 9541-9552(2021).

[35] CHENGLONG， ZHANG, CHENGLONG， ZHANG. A stochastic configuration network based on chaotic sparrow search algorithm. Knowledge-Based Systems, 220, 106924(2021).

[36] [36] 36马飞燕， 李向新. 基于改进麻雀搜索算法-核极限学习机耦合算法的滑坡位移预测模型［J］. 科学技术与工程， 2022， 22（5）： 1786-1793. doi: 10.3969/j.issn.1671-1815.2022.05.008MAF Y， LIX X. Landslide displacement prediction model using improved SSA-KELM coupling algorithm［J］. Science Technology and Engineering， 2022， 22（5）： 1786-1793.（in Chinese）. doi: 10.3969/j.issn.1671-1815.2022.05.008