CNN training hyperparameters.

Seismic impedance inversion is a geophysical technique that transforms seismic data into quantitative subsurface properties, primarily acoustic impedance. This process enables the identification of rock boundaries, hydrocarbon reservoirs, and lithological variations, thus supporting informed drilling decisions and reducing exploration risks. However, conventional inversion methods face limitations such as noise sensitivity, low resolution, and reduced effectiveness in geologically complex areas, often resulting in oversimplified subsurface models. This study addresses these challenges by employing deep learning approaches, specifically LeNet, AlexNet, and conventional CNN architectures, to improve seismic resolution and synthetic seismogram generation. The methodology involves preprocessing seismic and well-log data, calculating acoustic impedance and reflection coefficients, and applying Continuous Wavelet Transform (CWT) for feature extraction. The models are trained using synthetic seismograms and validated against real seismic data. Among the models evaluated, AlexNet demonstrates superior performance in seismic data reconstruction, achieving the lowest MSE (0.0031), RMSE (0.0557), and MAE (0.052), along with the highest R2 score (0.993). The proposed technique demonstrates superior predictive accuracy, refined subsurface characterization, and reduced geological risk, thereby establishing a robust benchmark for advanced geophysical data analysis.

地震阻抗反演（Seismic impedance inversion）是一项将地震数据转换为定量化地下属性的地球物理技术，核心目标为提取声阻抗（acoustic impedance）。该过程可实现岩层界面、油气储层（hydrocarbon reservoirs）与岩性变化的识别，为科学钻井决策提供支撑并降低勘探风险。然而传统反演方法存在诸多局限：对噪声敏感、分辨率偏低，且在地质复杂区域的反演效果欠佳，常导致地下模型过于简化。本研究针对上述挑战，采用深度学习方法，具体涵盖LeNet、AlexNet及经典卷积神经网络（Convolutional Neural Network, CNN）架构，以提升地震分辨率并生成合成地震记录（synthetic seismogram）。研究流程包括对地震数据与测井数据（well-log data）进行预处理，计算声阻抗与反射系数，并通过连续小波变换（Continuous Wavelet Transform, CWT）完成特征提取。随后以合成地震记录训练模型，并基于实际地震数据开展验证。在所有评估的模型中，AlexNet在地震数据重建任务中表现最优，其均方误差（Mean Squared Error, MSE）为0.0031、均方根误差（Root Mean Squared Error, RMSE）为0.0557、平均绝对误差（Mean Absolute Error, MAE）为0.052，同时取得最高的决定系数（R-squared, R²）得分0.993。本研究提出的技术具备更优异的预测精度、更精细的地下刻画能力，且可降低地质风险，从而为先进地球物理数据分析建立了可靠的基准。