Machine learning-based attenuation of steeply dipping events of seismic reflection image beneath the Korean Peninsula

Seismic reflection images derived by ambient-noise seismic interferometry (SI) can show subsurface structures without active sources. To image and interpret the upper mantle structures and tectonic boundaries beneath the southern part of Korean Peninsula, we applied SI method to seismic ambient noise data recorded at 119 seismic stations on the Korean Peninsula in 2014 (from the seismic network of the Korean Meteorological Administration). The factor that makes interpretation difficult is the steeply dipping events in reflection images. Most of these events of apparent steeply dips show as true reflection events from steep geologic boundaries. Therefore, we need to attenuate these events to interpret true reflection events. These events overlap many times. Also, the value of the slope has several values close to half of the Rayleigh waves or P waves. To attenuate these events with these complex features, we used machine learning techniques. We attenuated our steeply dipping events by applying the Extraction of diffractions method. As the steeply dipping events are attenuated, horizontal events were strengthened, and noises were attenuated. We can more clearly identify the reflection events of the Moho discontinuity and the lithosphere/asthenosphere (LAB) boundary near the two-way reflection times of 7-11 s and 17-22 s respectively.