| Citation: | Ma Guoqing, Wu Qi, Xiong Shengqing, Li Lili, 2021. Ratio Method for Calculating the Source Location of Gravity and Magnetic Anomalies Based on Deep Learning. Earth Science, 46(9): 3365-3375. doi: 10.3799/dqkx.2020.350 |
|
Beiki, M., 2013. TSVD Analysis of Euler Deconvolution to Improve Estimating Magnetic Source Parameters: An Example from the Åsele Area, Sweden. Journal of Applied Geophysics, 90: 82-91. https://doi.org/10.1016/j.jappgeo.2013.01.002
|
|
Cao, S. J., Zhu, Z. Q., Lu, G. Y., 2012. Gravity Tensor Euler Deconvolution Solutions Based on Adaptive Fuzzy Cluster Analysis. Journal of Central South University (Science and Technology), 43(3): 1033-1039 (in Chinese with English abstract). http://epub.cnki.net/grid2008/docdown/docdownload.aspx?filename=ZNGD201203039&dbcode=CJFD&year=2012&dflag=pdfdown
|
|
Florio, G., Fedi, M., 2014. Multiridge Euler Deconvolution. Geophysical Prospecting, 62(2): 333-351. https://doi.org/10.1111/1365-2478.12078
|
|
Gerovska, D., Araúzo-Bravo, M. J., 2003. Automatic Interpretation of Magnetic Data Based on Euler Deconvolution with Unprescribed Structural Index. Computers & Geosciences, 29(8): 949-960. https://doi.org/10.1016/S0098-3004(03)00101-8
|
|
Han, W. X., Zhou, Y. T., Chi, Y., 2018. Deep Learning Convolutional Neural Networks for Random Noise Attenuation in Seismic Data. Geophysical Prospecting for Petroleum, 57(6): 862-869, 877 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-SYWT201806008.htm
|
|
Hou, Z. L., Wang, E. D., Zhou, W. N., et al., 2019. Euler Deconvolution of Gravity Gradiometry Data and the Application in Vinton Dome. Oil Geophysical Prospecting, 54(2): 472-479, 242 (in Chinese with English abstract). http://www.researchgate.net/publication/332846748_Euler_deconvolution_of_gravity_gradiometry_data_and_the_application_in_Vinton_Dome
|
|
Lu, B. L., Fan, M. N., Zhang, Y. Q., 2009. The Calculation and Optimization of Structure Index in Euler Deconvolution. Progress in Geophysics, 24(3): 1027-1031 (in Chinese with English abstract). http://www.cnki.com.cn/Article/CJFDTotal-DQWJ200903030.htm
|
|
Ma, G. Q., 2013. The Study on the Automatic Interpretation Methods of Potential Field (Gravity & Magnetic) and Its Gradients (Dissertation). Jilin University, Changchun (in Chinese with English abstract).
|
|
Mushayandebvu, M. F., van Driel, P., Reid, A. B., et al., 2001. Magnetic Source Parameters of Two-Dimensional Structures Using Extended Euler Deconvolution. Geophysics, 66(3): 814-823. https://doi.org/10.1190/1.1444971
|
|
Nabighian, M. N., 1972. The Analytic Signal of Two-Dimensional Magnetic Bodies with Polygonal cross-Section: Its Properties and Use for Automated Anomaly Interpretation. Geophysics, 37(3): 507-517. https://doi.org/10.1190/1.1440276
|
|
Peters, L. J., 1949. The Direct Approach to Magnetic Interpretation and Its Practical Application. Geophysics, 14(3): 290-320. https://doi.org/10.1190/1.1437537
|
|
Reid, A. B., Allsop, J. M., Granser, H., et al., 1990. Magnetic Interpretation in Three Dimensions Using Euler Deconvolution. Geophysics, 55(1): 80-91. https://doi.org/10.1190/1.1442774
|
|
Reid, A. B., Thurston, J. B., 2014. The Structural Index in Gravity and Magnetic Interpretation: Errors, Uses, and Abuses. Geophysics, 79(4): J61-J66. https://doi.org/10.1190/geo2013-0235.1
|
|
Rumelhart, D. E., Hinton, G. E., Williams, R. J., 1986. Learning Representations by Back-Propagating Errors. Nature, 323(6088): 533-536. https://doi.org/10.1038/323533a0
|
|
Salem, A., Ravat, D., Mushayandebvu, M. F., et al., 2004. Linearized Least-Squares Method for Interpretation of Potential-Field Data from Sources of Simple Geometry. Geophysics, 69(3): 783-788. https://doi.org/10.1190/1.1759464
|
|
Stavrev, P., Reid, A., 2010. Euler Deconvolution of Gravity Anomalies from Thick Contact/Fault Structures with Extended Negative Structural Index. Geophysics, 75(6): I51-I58. https://doi.org/10.1190/1.3506559
|
|
Sun, Y. S., 1980. Computing the Vertical Second Grder Derivative of Gravitational and Magnetic Anomalies. Oil Geophysical Prospecting, 15(6): 45-62 (in Chinese with English abstract).
|
|
Thompson, D. T., 1982. EULDPH: A New Technique for Making Computer-Assisted Depth Estimates from Magnetic Data. Geophysics, 47(1): 31-37. https://doi.org/10.1190/1.1441278
|
|
Wang, H. R., 2020. The Study on the Gravity and Magnetic Inversion Based on Parallel Computing and Deep Learning Algorithm (Dissertation). Jilin University, Changchun (in Chinese with English abstract).
|
|
Yang, F. S., Ma, J. W., 2019. Deep-Learning Inversion: a Next-Generation Seismic Velocity Model Building Method. Geophysics, 84(4): 583-599. https://doi.org/10.1190/geo2018-0249.1
|
|
Yao, C. L., Guan, Z. N., Wu, Q. B., et al., 2004. An Analysis of Euler Deconvolution and Its Improvement. Geophysical and Geochemical Exploration, 28(2): 150-155 (in Chinese with English abstract). http://search.cnki.net/down/default.aspx?filename=WTYH200402017&dbcode=CJFD&year=2004&dflag=pdfdown
|
|
Zhang, C. Y., Mushayandebvu, M. F., Reid, A. B., et al., 2000. Euler Deconvolution of Gravity Tensor Gradient Data. Geophysics, 65(2): 512-520. https://doi.org/10.1190/1.1444745
|
|
Zhang, G. Y., Wang, Z. Z., Lin, C. Y., et al., 2020. Seismic Reservoir Prediction Method Based on Wavelet Transform and Convolutional Neural Network and Its Application. Journal of China University of Petroleum (Edition of Natural Science), 44(4): 83-93 (in Chinese with English abstract).
|
|
Zheng, H., Zhang, B, 2020. Intelligent Seismic Data Interpolation via Convolutional Neural Network. Progress in Geophysics, 35(2): 721-727 (in Chinese with English abstract).
|
|
Zhou, W. Y., Ma, G. Q., Hou, Z. L., et al., 2017. The Study on the Joint Euler Deconvolution Method of Full Tensor Gravity Data. Chinese Journal of Geophysics, 60(12): 4855-4865 (in Chinese with English abstract). http://www.researchgate.net/publication/326395072_The_study_on_the_joint_Euler_deconvolution_method_of_full_tensor_gravity_data
|
|
曹书锦, 朱自强, 鲁光银, 2012. 基于自适应模糊聚类分析的重力张量欧拉反褶积解. 中南大学学报(自然科学版), 43(3): 1033-1039. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201203039.htm
|
|
韩卫雪, 周亚同, 池越, 2018. 基于深度学习卷积神经网络的地震数据随机噪声去除. 石油物探, 57(6): 862-869, 877. doi: 10.3969/j.issn.1000-1441.2018.06.008
|
|
侯振隆, 王恩德, 周文纳, 等, 2019. 重力梯度欧拉反褶积及其在文顿盐丘的应用. 石油地球物理勘探, 54(2): 472-479, 242. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902027.htm
|
|
鲁宝亮, 范美宁, 张原庆, 2009. 欧拉反褶积中构造指数的计算与优化选取. 地球物理学进展, 24(3): 1027-1031. doi: 10.3969/j.issn.1004-2903.2009.03.029
|
|
马国庆, 2013. 位场(重&磁)及其梯度异常自动解释方法研究(博士学位论文). 长春: 吉林大学.
|
|
孙运生, 1980. 重磁异常垂向二阶导数的计算. 石油地球物理勘探, 15(6): 45-62.
|
|
王浩然, 2020. 基于并行计算与深度学习算法的大规模重磁数据反演研究(硕士学位论文). 长春: 吉林大学.
|
|
姚长利, 管志宁, 吴其斌, 等, 2004. 欧拉反演方法分析及实用技术改进. 物探与化探, 28(2): 150-155. doi: 10.3969/j.issn.1000-8918.2004.02.017
|
|
张国印, 王志章, 林承焰, 等, 2020. 基于小波变换和卷积神经网络的地震储层预测方法及应用. 中国石油大学学报(自然科学版), 44(4): 83-93. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDX202004011.htm
|
|
郑浩, 张兵, 2020. 基于卷积神经网络的智能化地震数据插值技术. 地球物理学进展, 35(2): 721-727. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ202002041.htm
|
|
周文月, 马国庆, 侯振隆, 等, 2017. 重力全张量数据联合欧拉反褶积法研究及应用. 地球物理学报, 60(12): 4855-4865. doi: 10.6038/cjg20171225
|