LAUSR

First-break picking is an important step during the processing of both passive and active seismic data. Many automated algorithms have been developed to detect first-break points in large volumes of seismic data. However, it remains difficult to determine precise first-break points in seismograms with low signal-to-noise ratios. Therefore, we present a new approach based on the differences between multiwindow energy ratios (DERs) that minimizes the effects of noise. First, the DER is defined and a thresholding method for detecting first-break points using the DERs is proposed. Thresholding can be varied depending on the DER parameters, which ensures reliable results even if the parameters change. We use two types of seismic data to establish and verify the DER method: big data derived via global earthquake monitoring STanford EArthquake Dataset (STEAD) and ocean bottom cable (OBC) data acquired offshore of Pohang, Republic of Korea. We investigated the effects of parameter changes on the DER picking results. Good picking performance was verified under low signal-to-noise conditions and compared to conventional first-break picking methods. The DER accuracy was higher than that of conventional methods and outliers were rare.