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The Earth, in large portions, is covered in oceans, sediments, and glaciers. High‐resolution body wave imaging in such environments often suffers from severe reverberations, that is, repeating echoes of the incoming scattered wavefield trapped in the reverberant layer, making interpretation of lithospheric layering difficult. In this study, we propose a systematic data‐driven approach, using autocorrelation and homomorphic analysis, to solve the twin problem of detection and elimination of reverberations without a priori knowledge of the elastic structure of the reverberant layers. We demonstrate, using synthetic experiments and data examples, that our approach can effectively identify the signature of reverberations even in cases where the recording seismic array is deployed in complex settings, for example, using data from (a) a land station sitting on Songliao basin, (b) an ocean bottom station in the fore‐arc setting of the Alaska amphibious community seismic experiment, and (c) a station deployed on ice‐sediment strata in the glaciers of Antarctica. The elimination of the reverberation is implemented by a frequency domain filter whose parameters are automatically tuned using seismic data alone. On glaciers where the reverberating sediment layer is sandwiched between the lithosphere and an overlying ice layer, homomorphic analysis is preferable in detecting the signature of reverberation. We expect that our technique will see wide application for high‐resolution body wave imaging across a wide variety of conditions. Plain Language Summary: The Earth, in large portions, is covered in oceans, sediments, and glaciers. Because of the large differences in rock properties between these layers and the solid Earth underneath, waves get trapped in them which hampers the successful investigation of deeper layers. This is often referred to as "reverberation," waves that "echo" or "sing" again and again. In this study, we propose a systematic approach to detect and eliminate this "singing" effect using only observed data, without any knowledge of the local geological structure. Our approach extends the autocorrelation analysis, a widely used method to detect repeating patterns in time series, and also takes advantage of the homomorphic analysis, a popular technique in speech and audio processing. We demonstrate the effectiveness of our approach using both human‐generated and real seismic data, collected from complex geosettings around the world, including in the oceans to the south of the Alaska Peninsula, sedimentary basin in northeast China, and Antarctica. On glaciers where the reverberation comes from the complicated coupled effect of ice and sediment, homomorphic analysis is preferable in detecting the signature of reverberation. We expect that our technique will greatly improve the imaging of deep Earth structure across a wide variety of conditions. Key Points: A data‐driven approach using autocorrelation and homomorphic analysis is proposed to detect the signature of reverberations in seismic dataThe proposed approach can be applied to various geological settings where reverberations are present; that is, sediments, oceans, and glaciersSignal enhancement using either approach is best in single‐layer systems, while homomorphic analysis is preferred for ice‐sediment systems [ABSTRACT FROM AUTHOR]