Coda waves are multiply scattered waves that arrive much later than the major waves.Small seismic velocity variations are observed in reservoirs because of small variations in reservoir properties, which affect the fi rst arrivals. Hence, fi rst arrivals cannot be used to detect small seismic velocity variations. However, small variations can be reliably detected by the coda waves because of the amplification owing to multiple scattering. We investigate the ability of coda wave interferometry to detect seismic velocity variations and monitor time-lapse reservoir characteristics using numerical simulations and experimental data. We use the Marmousi II model and finite-difference methods to build model seismic data and introduce small seismic velocity variations in the target layer. We examine the model seismic data before and after the changes and observe the coda waves. We fi nd that velocity changes can be detected by coda wave interferometry and demonstrate that coda wave interferometry can be used in monitoring timelapse reservoir characteristics.
With the development of seismic exploration technology, geological structure interpretation has become more and more refined, whereas random noise interference, subsalt weak seismic reflection signals, and other issues are also gradually emerged at the same time, which resulted in traditional geological structure interpretation accuracy reduction only relying on single seismic data. A novel technical process integrated time-frequency decomposition of seismic data, seismic dip constraints, and geological structure interpretation is proposed in this paper which is named multiscale seismic dip constraint geological structure interpretation. The technical process contains five steps which first use the basis tracking spectrum decomposition technology to convert the seismic data into the time-frequency domain and then decompose the raw seismic data into coarse scale, fine scale, and deliberate scale through window and threshold methods. Subsequently, execute local layer dip calculation with Hilbert transform and geological structure interpretation on seismic data of different scale, respectively. At last, perform geological structure attribute fusion to obtain fine geological structure interpretation. Synthetic data test and field data test show that through multiscale time-frequency decomposition, high-frequency noise interference can be removed and the subsalt seismic weak signal can be enhanced, and then, high-precision fine complex geological structure interpretation can be obtained with seismic dip constraint. Therefore, the technical process proposed in this paper is effective and can be widely applied in the interpretation of field seismic data.
Since China’s reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO2) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO2 emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO2 emissions, this study establishes a hybrid intelligent algorithm model suitable for CO2 emissions prediction based on China’s CO2 emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO2 emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.
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