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We propose a new method that considers the envelope, phase attributes, and texture analysis of the subbottom profile to automatically obtain continuous and accurate horizon picking. This method overcomes the shortcomings of the traditional methods in envelope and phase automatic horizon picking in terms of accuracy and continuity, and in efficiency when it comes to manual picking. Under the constraint of the envelope threshold from the envelope image and the texture segmentation from the phase image, the accurate envelope horizons are picked from the envelope image. Through mean filtering and template enhancement, the fine phase horizons are picked from the phase image. Finally, by combining the two kinds of picked horizons, more continuous, accurate, and finer horizons are obtained. The proposed method was tested on a real data set. Plain Language Summary: The proposed method considers the complementarity combination of envelope and phase horizon picking and integrates the envelope, phase, and texture attributes, allowing to get better horizons than a single horizon‐picking method. In the proposed method, the envelope horizons constrain the locations of layers and reduce the redundancy of the phase horizons, while the phase horizons avoid the influence of the envelope and guarantee the continuity of the picked horizons. Key Points: The proposed method obtains envelope horizons by combining envelope threshold segmentation on the envelope image and texture segmentation on the phase imageThe proposed method obtains phase horizons by applying template enhancement and horizon tracking to the phase imageThe final horizons can be obtained by combining envelope horizons and phase horizons [ABSTRACT FROM AUTHOR]

Manual horizon picking and semiautomatic horizon picking are the traditional methods for data processing of subbottom profiles (SBPs). However, the former is time consuming and laborsome, whereas the latter requires frequent manual intervention and easily suffers from low accuracy and discontinuous picking due to noises. A comprehensive and automatic horizon picking method is proposed by combining the given gray mutation, horizon tracking and filtering, and horizon growth algorithms in this paper. Based on the peaks and valleys corresponding to the strong and weak impedance contrasts in an echo sequence, the gray mutation is used to prepick horizons from a SBP image. According to sediment layer continuity and horizontal resolution, the horizon tracking and filtering are applied to construct discrete horizon segments and remove outliers. In consideration of the correlation of adjacent ping sequences and the horizon orientation, the horizon growth based on the horizon orientation constraints is utilized to connect the discrete horizon segments. Experiments validated the proposed method. The automatic horizon picking results were compared with the bore-hole data and the manual horizon picking results as well, and good consistencies were achieved. The determinations of parameters and the performance of the proposed method are discussed based on the theoretical study and experiments, and some conclusions are drawn. [ABSTRACT FROM AUTHOR]

The towing operation mode of side‐scan sonar system (SSS) easily results in dislocations and distortions of targets in the SSS strip image, brings difficulty to the mosaic of these strip images by the geocoding method or the tessellation‐line method and affects the recognition and understanding for seabed relief. Therefore this study proposes a new method, namely the segment‐image mosaic method based on corresponding features of the two adjacent SSS strip images. Through SSS image preprocessing, segment match based on corresponding features and image fusion in the common coverage area based on wavelet transformation, this new method overcomes the drawbacks of the traditional methods of image mosaic, fulfils well the mosaic of SSS strip images and finally a whole‐area SSS image is formed. Experiments have verified that the mosaic image formed by the new method can correctly reflect the position, shape and distribution of seabed targets, which is helpful to understand seabed relief. [ABSTRACT FROM AUTHOR]

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn. [ABSTRACT FROM AUTHOR]