Seismic interpretation is often limited by low resolution and strong noise data. To deal with this issue, we propose to leverage deep convolutional neural network (CNN) to achieve seismic image super-resolution and denoising simultaneously. To train the CNN, we simulate a lot of synthetic seismic images with different resolutions and noise levels to serve as training data sets. To improve the perception quality, we use a loss function that combines the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> loss and multiscale structural similarity loss. Extensive experimental results on both synthetic and field seismic images demonstrate that the proposed workflow can significantly improve the perception of quality of original data. Compared to conventional methods, the network obtains better performance in enhancing detailed structural and stratigraphic features, such as thin layers and small-scale faults. From the seismic images super-sampled by our CNN method, a fault detection method can compute more accurate fault maps than from the original seismic images. 

Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising

A new singularity-free analytical formula has been developed for the gravity field of arbitrary 3D polyhedral mass bodies with horizontally and vertically varying density contrast using third-order polynomial functions. First, the observation sites are moved to the origin of the coordinate system. Then, the volume and surface integral theorems are invoked successively to transform the volume integrals into surface integrals over polygonal faces and into line integrals over the edges of the polyhedral mass bodies. Furthermore, singularity-free closed-form solutions are derived for these line integrals over the edges. Thus, the observation sites can be located inside, on, or outside the 3D distributions. A synthetic prismatic mass body is adopted to verify the accuracy and singularity-free property of our newly developed analytical expressions. Excellent agreements are obtained between our solutions and other published closed-form solutions with relative errors in the order of 10−12% to 10−13%. In a...

Gravity anomalies of arbitrary 3D polyhedral bodies with horizontal and vertical mass contrasts up to cubic order

/pdf/depth-distribution-of-the-sedimentary-basin-and-moho-46kcz2rvdw.pdf

Depth distribution of the sedimentary basin and Moho undulation in the Yellow Sea, NE Asia interpreted by using satellite-derived gravity field

Segmentation of the Andean margin by isostatic models and gradients

The use of analog sensor arrays is often assumed to provide signal-to-ambient-noise improvements proportional to the square root of the number of sensors being summed. We determined via numerical modeling and field experiments that the improvements sought were significantly hindered once the ambient noise exhibited coherence over the array being summed. As a first step, a numerical model was developed to explore the optimal sensor spacing based on the average correlation coefficient between sensors. Field experiments were then carried out to measure ambient noise using closely spaced geophones at several sites in Perth, Western Australia. We show that the measured noise at six test sites was strongly coherent over distances of up to 10 m, with the level of coherency being inversely proportional to frequency. The resulting optimum geophone spacing under our field conditions was determined to be 7.5 m. These results offered further encouragement to reassess our use of analog arrays and to consider r...

The coherency of ambient seismic noise recorded during land surveys and the resulting implications for the effectiveness of geophone arrays

Fusen Xiao, Jinli Yang, Bo Liang, Meng Zhang, Rong Li, Fang Li, Hongping Xiao, Xue Lei, Qinglin Liu and Thomas Heesom present the planning, acquisition, processing and results from a full-azimuth high-density point-source, point-receiver broadband seismic survey over part of the Gongshanmiao structure in the Sichuan Basin, southwest China.

High-density 3D point receiver seismic acquisition and processing – a case study from the Sichuan Basin, China

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High-density 3D point receiver seismic acquisition and processing – a case study from the Sichuan Basin, China