Seismic structure interpretation based on machine learning: A case study in coal mining

Abstract

Interpretation of geologic structures entails ambiguity and uncertainties. It usually requires interpreter judgment and is time consuming. Deep exploitation of resources challenges the accuracy and efficiency of geologic structure interpretation. The application of machine-learning algorithms to seismic interpretation can effectively solve these problems. We analyzed the theory and applicability of five machine-learning algorithms. Seismic forward modeling is a key connection between the model and seismic response, and it can obtain seismic data of known geologic structures. Based on the modeling data, we first optimized the seismic attributes sensitive to the target geologic structure and then we verified the accuracy of the five machine-learning algorithms by the cross-checking method. In this case, the random forest algorithm had the highest accuracy. So we examined the structural interpretation method based on a random forest using the 3D seismic reflection data from coalfield exploration. The prediction effect of this interpretation workflow is verified by comparison with known geologic structures on the plane and profile. The results suggest that the random forest algorithm is feasible to indicate geologic structure interpretations in the case of collapsed column and fault structures and it can effectively improve the efficiency of seismic interpretation and its accuracy. The machine-learning-based workflow provides a new technique for seismic structure interpretation in coal mining.

Consistent convolution kernel design for missing shots interpolation using an improved U‐net

“…In recent years, deep learning (DL) has been widely used in seismic inversion (Mao et al ., 2019; Ovcharenko et al ., 2019; Li et al ., 2020), reservoir prediction (Karimpouli et al ., 2010; Saikia et al ., 2020; Zhu et al ., 2020), geologic feature identification (Wu et al ., 2019; Geng et al ., 2020; Liu et al ., 2020) and seismic data interpretation (Li et al ., 2019; Di et al ., 2020; Shi et al ., 2020). Some researchers proposed DL strategies to improve horizontal continuity in seismic data interpolation applications, and DL‐based methods can overcome some prior assumptions like linearity, low‐rank property and sparsity (Wang et al ., 2019).…”

Semi‐supervised deep autoencoder for seismic facies classification

“…(2018), Chopra and Marfurt (2018) and Li et al . (2019). These methods are based on the ‘shallow’ machine learning strategy.…”

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

“…Such techniques can contribute to speeding up the activity through an accurate interpretation that can serve as valuable assistance for the experts. For seismic signal classification tasks, many supervised machine learning models, such as Support Vector Machine (SVM) [ 10 ], Decision Trees [ 11 ], Multilayer Perceptron Neural Networks (MLP), or Convolutional Neural Networks [ 12 , 13 ], have been explored. These types of algorithms employ predefined labels (product of the human interpretation of seismic data) to train the models, optimizing their label recognition capacity in a given dataset.…”