In this paper, we propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way to address this ill-posed seismic inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong non-uniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model as well as the timevarying property of seismic data. To approach these challenges, we propose an end-to-end Seismic Inversion Networks (SeisInvNet for short) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup and global context of its corresponding seismic profile. Then from enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our proposed SeisInv dataset according to various evaluation metrics, and the inversion results are more consistent with the target from the aspects of velocity value, subsurface structure and geological interface. In addition to the superior performance, the mechanism is also carefully discussed, and some potential problems are identified for further study. Index Terms-Seismic inversion, Deep neural networks.
Velocity model inversion is one of the most important tasks in seismic exploration. Full waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion methods, but it heavily depends on initial models and is computationally expensive. In recent years, a large number of deep learning based velocity model inversion methods have been proposed. One critical component in those deep learning based methods is a large training set containing different velocity models. We propose a method to construct a realistic structural model for deep learning network. Our P-wave velocity model building method for creating dense-layer/fault/salt body models can automatically construct a large number of models without much human effort, which is very meaningful for deep learning networks. Moreover, to improve the inversion result on these realistic structural models, instead of only using the common-shot gather, we also propose to extract features from the common-receiver gather as well. Through a large number of realistic structural models, reasonable data acquisition methods, and appropriate network setups, a more generalized result can be obtained through our proposed inversion framework, which has been demonstrated to be effective on the independent testing data set. The results of dense-layer models, fault models, and salt body models are compared and analyzed, respectively, which demonstrates the reliability of the proposed method and also provides practical guidelines for choosing the optimal inversion strategies in realistic situations.
The microphase separation dynamics of the triblock copolymer surfactant P103 [(ethylene oxide)17(propylene oxide)60(ethylene oxide)17] was investigated by a dynamic variant of mean-field density functional theory. Different self-assembled aggregates, spherical micelles, micellar clusters and disk-like micelles, are explored in the solution. The spherical micelle above critical micelle concentration (CMC) is a dense core consisting mainly of PPO and a hydrated PEO swollen corona, and is in good agreement with the experimental results concerning their structures. At a concentration of 10-15%, micellar clusters with a larger PPO core form as a result of coalescence among spherical micelles. At concentrations above 16% by volume, a series of disk-like micelles come into being. The order parameters show that spherical micelles are easily formed, while the micellar clusters or disk-like micelles need a longer time to reach steady equilibrium. The results show that mesoscopic simulation can augment experimental results on amphiphilic polymers, and provide some mesoscopic information at the mesoscale level.
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