Application of machine learning for the extrapolation of seismic data

Low frequencies in seismic data are often challenging to acquire. Without low frequencies, though, a method like full-waveform inversion might fail due to cycle-skipping. This thesis aims to investigate the potential of neural networks for the task of low-frequency extrapolation to overcome aforementioned problem. Several steps are needed to achieve this goal: First, suitable data for training and testing the network must be found. Second, the data must be pre-processed to condition them for machine learning and efficient application. Third, a specific workflow for the task of low-frequency extrapolation must be designed. Finally, the trained network can be applied to data it has not seen before and compared to reference data. In this work, synthetic data are used for training and evaluation because in such a controlled experiment the target for the network is known. For this purpose, 30 random but geologically plausible subsurface models were generated based on a simplified geology around the Asse II salt mine, and used for finite-difference simulations of seismograms. The corresponding shot gathers were pre-processed by, among others, normalizing them and splitting them up into patches, and fed into a convolutional neural network (U-Net) to assess the network’s performance and its ability to reconstruct the data. Two different approaches were investigated for the task of low-frequency extrapolation. The first approach is based on using only low frequencies as the network’s target, while the second approach has the full bandwidth as target. The latter yielded superior results and was therefore chosen for subsequent applications. Further tests of the network design led to the introduction of ResNet blocks instead of simple convolutions in the U-Net layers, and the use of the mean-absolute-error instead of the mean-squared-error loss function. The final network designed in this way was then applied to the synthetic data originally reserved for testing. It turned out that the chosen method is able to successfully extrapolate low frequencies by more than half an octave (from about 8 to 5 Hz) given the experimental setup at hand. Although the results start to deteriorate in the low-frequency band for larger offsets, full-waveform inversion will overall benefit from the application of the presented machine learning approach

InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion

Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3D high-resolution velocity maps via deep networks is still a great challenge. In this paper, we present InversionNet3D, an efficient and scalable encoder-decoder network for 3D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and thus enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3D Kimberlina dataset demonstrate that InversionNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions

Using explainability to design physics-aware CNNs for solving subsurface inverse problems

We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN) for solving an inverse problem for shallow subsurface imaging. Although CNNs have gained popularity in recent years across many fields, the development of CNNs remains an art, as there are no clear guidelines regarding the selection of hyperparameters that will yield the best network. While optimization algorithms may be used to select hyperparameters automatically, these methods focus on developing networks with high predictive accuracy while disregarding model explainability (descriptive accuracy). However, the field of Explainable Artificial Intelligence (XAI) addresses the absence of model explainability by providing tools that allow developers to evaluate the internal logic of neural networks. In this study, we use the explainability methods Score-CAM and Deep SHAP to select hyperparameters, such as kernel sizes and network depth, to develop a physics-aware CNN for shallow subsurface imaging. We begin with a relatively deep Encoder-Decoder network, which uses surface wave dispersion images as inputs and generates 2D shear wave velocity subsurface images as outputs. Through model explanations, we ultimately find that a shallow CNN using two convolutional layers with an atypical kernel size of 3x1 yields comparable predictive accuracy but with increased descriptive accuracy. We also show that explainability methods can be used to evaluate the network's complexity and decision-making. We believe this method can be used to develop neural networks with high predictive accuracy while also providing inherent explainability.Comment: 26 pages, 14 figures, 4 table