Combined Detection Research of Shallow Gas Storage Structures Using Microtremor and Resistivity Methods

During seismic exploration, seismic data is collected to determine underground structural features and hydrocarbon-bearing stratum interfaces. The seismic data inversion process is highly complex and susceptible to interference from noise, which may lead to significant errors in inversion and affect comprehensive stratigraphic interpretation. The application of machine learning to seismic data interpretation and denoising remains technically challenging and yields suboptimal results. Micromotion exploration technology employs conventional “noise” as its signal source, utilizing widely occurring regular noise. On the basis of the theory of stationary random processes, it extracts frequency curves of surface waves from micromotion signals and performs inversion to obtain underground shear wave velocity profiles. Owing to its simplicity, cost-effectiveness, and environmental friendliness, micromotion exploration has notable advantages in structural exploration and hydrocarbon discovery. The micromotion detection results of an experimental area can quickly reflect the location of fault zones. When combined with electrical logging, this method is effective for shallow gas reservoir structure detection.