论文详情
建立精确的速度模型对地震成像和解释十分重要, 深度学习方法为建立精确的速度模型提供了新的途径, 然而, 目前可用于速度建模网络训练的样本非常有限。为此, 提出了一种利用随机曲线模拟地下速度模型, 自动生成大量样本用于深度学习训练的方法。该方法通过生成一组随机数, 插值形成随机序列, 利用三角函数将随机序列生成随机曲线来模拟地下界面, 生成层状速度模型, 在此基础上, 通过添加断裂、速度异常体和地层倾角来模拟更复杂的速度模型; 在速度预测网络构建方面, 选取Deeplabv3+网络作为速度预测网络。通过增加卷积层的方法, 优化了Deeplabv3+网络上采样后直接输出导致边界模糊的问题。将上述方法应用于模型数据和实际数据, 测试了该方法在含噪声、不同子波频率和部分数据缺失情况下的稳定性。结果表明, 该方法能够有效地应对噪声、不同子波频率和部分数据缺失的影响, 具有可靠的泛化性和鲁棒性。
Establishing accurate velocity models is crucial for seismic imaging and interpretation.Deep learning offers a new way to build precise velocity models, but the samples available for training the velocity-modeling network are severely limited.To address this issue, we propose a method that utilizes random curves to simulate the subsurface velocity model and automatically generates a large number of samples for deep learning training.A set of random numbers is generated and interpolated to form random sequences, which are then transformed into random curves using trigonometric functions to simulate subsurface interfaces and produce a layered velocity model.The velocity model is complicated by incorporating such features as faults, velocity anomalies, and bedding angles.We adopt Deeplabv3+ as the velocity prediction network.The Deeplabv3+ network is optimized by adding convolutional layers to address the issue of blurry boundaries caused by direct output after upsampling.We applied the proposed method to both synthetic and real data, and evaluated its stability under conditions of noises, wavelet variation, and data missing.The results demonstrate that our approach effectively mitigate the impact of wavelet variation, noises, and partial data absence, and show reliable generalization and robustness.