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      The temporal and spatial evolution characteristics of induced seismicity in the Changning shale gas field based on dense array

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          Abstract

          In this paper, we performed microseismicity detection and location using the deep learning method and obtained a high-precision earthquake catalog in the Changning gas field, which is one of the largest shale gas production demonstration areas in China. We found that the spatial and temporal characteristics of seismicity in the region are indicative of its correlation with industrial operations. The distribution of earthquakes at depth reflected variations in reservoir depth and provided valuable constraints on it. The horizontal layered distribution of earthquakes at depth is due to the formation of fracture surfaces from interconnected fractures near the reservoir during hydraulic fracturing (HF) operations, which clearly demonstrates how HF operations impact seismicity. We suggested that the horizontally layered distribution was driven by two fundamental mechanisms: reactivation of pre-existing faults during HF and injection of high-pressure fluids into the reservoir, leading to fracture creation. Several M L ≥4 earthquakes, which did not occur on well-defined seismogenic faults, may have been triggered by pore elastic coupling resulting from regional stress accumulation and fluid injection. Significantly, the M L 4.9 seismic sequence occurring at the basement indicates that fracking has reactivated and promoted pre-existing faults, highlighting the need for further investigation into potential seismic hazards in the region.

          Supplementary Information

          The online version contains supplementary material available at 10.1038/s41598-024-77443-1.

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          A Double-Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California

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            Hydraulic Fracturing and Seismicity in the Western Canada Sedimentary Basin

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              Global review of human-induced earthquakes

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                Author and article information

                Contributors
                zhoulq@ief.ac.cn
                wenzp@cea-igp.ac.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 October 2024
                25 October 2024
                2024
                : 14
                : 25287
                Affiliations
                [1 ]GRID grid.450296.c, ISNI 0000 0000 9558 2971, Institute of Geophysics, , China Earthquake Administration, ; Beijing, 100081 China
                [2 ]Shanxi Earthquake Agency, Taiyuan, 030000 China
                [3 ]Institute of Earthquake Forecasting, China Earthquake Administration, ( https://ror.org/045sza929) Beijing, 100036 China
                Article
                77443
                10.1038/s41598-024-77443-1
                11511963
                39455724
                8267a7cf-8e31-4f17-8f35-808080adb253
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 18 July 2024
                : 22 October 2024
                Funding
                Funded by: the National Key Research and Development Program of China
                Award ID: 2020YFA0710603,
                Award ID: 2021YFC3000704
                Award ID: 2021YFC3000704
                Award ID: 2020YFA0710603,
                Award ID: 2020YFA0710603,
                Funded by: The Shanghai Artificial Intelligence Laboratory.
                Funded by: the National Natural Science Foundation of China
                Award ID: 42174066
                Award ID: 42174066
                Award ID: 42174066
                Funded by: the Central Public-interest Scientific Institution Basic Research Fund
                Award ID: CEAIEF20240405
                Award ID: CEAIEF20240405
                Award ID: CEAIEF20240405
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                the changning shale gas field,induced seismicity,seismicity detection and location,deep learning,the seismogenic mechanism,solid earth sciences,geophysics,seismology

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