Utilization of Intelligent Optimization Algorithms in Geological Prospecting Seismic Data Analysis

In geological exploration, the accuracy of seismic data analysis is pivotal. Traditional methods struggle with high-dimensional data, leading to inaccuracies. This study introduces the Differential Evolution algorithm to enhance analysis precision and efficiency through preprocessing, feature selection, and optimization. The DE algorithm outperforms conventional methods in feature identification and anomaly detection, achieving up to 95.6% accuracy. It also demonstrates robustness across various signal-to-noise ratios and seismic events, particularly under high ratios, maintaining 95.6% robustness. Moreover, it excels in efficiency, reducing data processing and training times significantly compared to traditional approaches. The DE algorithm offers valuable insights and tools for geological exploration, promising significant contributions to future resource development.