Generative Adversarial Network for Desert Seismic Data Denoising

Seismic exploration is a kind of exploration method for oil and gas resources. However, the disturbance of numerous random noise will decrease the quality and signal-to-noise ratio (SNR) of real seismic records, which brings difficulties to the following works of processing and interpretation. The seismic records of desert region pose a particular problem because of the strong energy noise and the spectrum overlapping between effective signals and random noise. Recent research works demonstrate that a convolutional neural network (CNN) can increase the SNR of seismic records. The optimization of denoising methods based on CNN is principally driven by the loss functions that largely focus on minimizing the mean-squared reconstruction error between denoising records and theoretical pure records. The denoising results estimated by the CNN model are often lacking the perfection of the signal structure. Therefore, when processing seismic records with low SNR, the denoising results often have a lack of effective signal in some traces, which leads to the poor continuity of events. In order to solve this problem, we adopt the strategy of generative adversarial network (GAN) to construct a GAN for denoising. It is divided into two parts: the generator (the denoising network based on CNN) is used to remove noise, while the discriminator is used to guide the generator to restore the structure information of effective signals. The generator and discriminator enhance the performance of each other through adversarial training, and the generator after adversarial training can greatly recover events and suppress random noise in synthetic and real desert seismic data.

I. Introduction

Seismic exploration is a kind of important way for oil and gas exploration. It is a geophysical exploration method, which uses the response of artificial seismic waves to infer the lithology and structure of stratum by analyzing wave velocity, travel time, angles of reflection and refraction, and so on. However, this response received by geoophones, i.e., seismic data, is often interfered by a large amount of random noise [1]–[4]. Therefore, how to suppress seismic random noise and enhance the signal-to-noise ratio (SNR) of seismic data becomes an important topic, which is of great significance to analyze stratum structure and explore oil, gas, and mineral resources [1], [2].