Microseismic imaging plays an important role in hydraulic fracture detection, and the first-arrival picking of microseismic events is the bedrock of microseismic imaging. Manual picking is the most reliable and also the most time-consuming method for the detection of the first arrival of microseismic events. Accurate and efficient first-arrival picking in a real noisy environment is a challenge for most of the automatic first-arrival picking methods. We have developed a novel workflow to automatically pick the first arrival of microseismics by using a state-of-the art pixel-wise convolutional image segmentation method. We first form the training data by randomly selecting part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment the selected traces into two parts according to the time index of manual picking and assign each part a label accordingly. Then, we build an encoder-decoder convolutional neural network architecture and use the training data and training label as the input. Next, we obtain the trained network hierarchy by learning the segmented training data and labels. Finally, we predict the first arrivals of microseismic events by applying the trained network hierarchy to the rest of the microseismic traces. The synthetic and field data examples demonstrate that our method successfully identifies the first arrivals. The predicted first-arrival result obtained by using our method is superior to the result obtained by using the traditional method of short-term average and long-term average.

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Seismic noise attenuation is an important step in seismic data processing. Most noise attenuation algorithms are based on the analysis of time-frequency characteristics of the seismic data and noise. We have aimed to attenuate white noise of seismic data using the convolutional neural network (CNN). Traditional CNN-based noise attenuation algorithms need prior information (the “clean” seismic data or the noise contained in the seismic) in the training process. However, it is difficult to obtain such prior information in practice. We assume that the white noise contained in the seismic data can be simulated by a sufficient number of user-generated white noise realizations. We then attenuate the seismic white noise using the modified denoising CNN (MDnCNN). The MDnCNN does not need prior clean seismic data nor pure noise in the training procedure. To accurately and efficiently learn the features of seismic data and band-limited noise at different frequency bandwidths, we first decomposed the seismic data into several intrinsic mode functions (IMFs) using variational mode decomposition and then apply our denoising process to the IMFs. We use synthetic and field data examples to illustrate the robustness and superiority of our method over the traditional methods. The experiments demonstrate that our method can not only attenuate most of the white noise but it also rejects the migration artifacts.