Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Abstract

<jats:p> Seismic structural interpretation involves highlighting and extracting faults and horizons that are apparent as geometric features in a seismic image. Although seismic image processing methods have been proposed to automate fault and horizon interpretation, each of which today still requires significant human effort. We improve automatic structural interpretation in seismic images by using convolutional neural networks (CNNs) that recently have shown excellent performances in detecting and extracting useful image features and objects. The main limitation of applying CNNs in seismic interpretation is the preparation of many training data sets and especially the corresponding geologic labels. Manually labeling geologic features in a seismic image is highly time-consuming and subjective, which often results in incompletely or inaccurately labeled training images. To solve this problem, we have developed a workflow to automatically build diverse structure models with realistic folding and faulting features. In this workflow, with some assumptions about typical folding and faulting patterns, we simulate structural features in a 3D model by using a set of parameters. By randomly choosing the parameters from some predefined ranges, we are able to automatically generate numerous structure models with realistic and diverse structural features. Based on these structure models with known structural information, we further automatically create numerous synthetic seismic images and the corresponding ground truth of structural labels to train CNNs for structural interpretation in field seismic images. Accurate results of structural interpretation in multiple field seismic images indicate that our workflow simulates realistic and generalized structure models from which the CNNs effectively learn to recognize real structures in field images. </jats:p>

Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

“…In defining this function, the parameters a k , b k , c k , and σ k are all randomly chosen from some predefined ranges as discussed by Wu et al (2020). By using the damping scalar function , we gradually decrease the curvature (or bending extent) vertically upward from bottom to top in the model.…”

“…This linear function is used to generate the purely dipping structures in the reflectivity model. The slopes of the dipping structures in the x − and y − directions, respectively, are defined by the parameters p and q , which are randomly chosen from a limited range of [− 0.25, 0.25] to avoid generating layers with extremely high slopes after combining with s 1 (Wu et al, 2020). We set to ensure that the center trace r ( x c , y c , z ) of the reflectivity model is not shifted.…”

“…After constructing a reflectivity model (Figure 5c) with simulated folding structures and collapsed paleokarst features, our next step is to simulate a synthetic seismic image (Figure 5d) by convolving a Ricker wavelet with the reflectivity model. Instead of convolving in the vertical direction as in conventional methods, we compute the convolution here in the direction perpendicular to the reflector structures as suggested by Wu, Liang, et al (2019) and Wu et al (2020). The peak frequency of the Ricker wavelet is also randomly chosen from a predefined range (10–35 Hz) to compute a synthetic seismic image with various frequency components.…”

“…where the first function s 1 is a combination of N Gaussian functions scaled by a vertically damping function as suggested by and Wu et al (2020):…”

“…CNN‐based methods are powerful for multidimensional image processing tasks including image classification (e.g., He et al, 2016; Krizhevsky et al, 2012; Simonyan & Zisserman, 2014; Szegedy et al, 2015), object detection (e.g., Lin et al, 2017; Liu et al, 2016; Redmon et al, 2016; Ren et al, 2015; Sermanet et al, 2013), and image segmentation (e.g., Badrinarayanan et al, 2017; Chen et al, 2017; Long et al, 2015; Ronneberger et al, 2015). Recently, CNNs have been increasingly applied in geoscience problems (Bergen et al, 2019; Bianco et al, 2019) including the interpretation of geologic faults (e.g., Di et al, 2019; Lu et al, 2018; Wu, Liang, et al, 2019; Wu, Shi, et al, 2019; Zhao & Mukhopadhyay, 2018), horizons (e.g., Geng et al, 2019; Wu, Zhang, et al, 2019; Wu et al, 2020), salt bodies (e.g., Di & AlRegib, 2020; Di et al, 2018; Guillen et al, 2015; Shi et al, 2019), and channels (Pham et al, 2019) in seismic images.…”