Phase arrival picking for bridging multi-source downhole microseismic data using deep transfer learning

Abstract

The phase arrival picking of the downhole microseismic dataset is a critical step in fracturing monitoring data processing. Recently, data-driven methods have been widely used in seismology studies, especially in seismic phase picking. The picking results heavily depend on whether large quantities of accurately labeled phase samples could be obtained to extract the characteristics of seismic waveforms. Also, there is a shortcoming of poor generalization ability in dealing with the cross-source transfer scenarios. In this paper, we propose a novel deep transfer learning method for microseismic phase arrival picking by fine-tuning one existing pretrained model based on a few phase samples. The pretrained model, which has been domain-adapted for phase picking, adopts 2D U-Net to both extract time and space features, thereby improving the overall picking accuracy. Moreover, the fully convolutional U-Net architecture has the ability to handle samples with variable sizes so could be used for bridging downhole microseismic data from different sources. The results of two transfer cases show that compared with the direct application of the pretrained model and a newly trained model, the proposed method could provide more satisfactory performance with only limited seismic phase samples. Also, our method significantly reduces the cost of labeling and saves time because of avoiding repeated training.

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

“…Additionally, the connection of feature maps between the encoder and decoder allows for fine-grained information transfer during upsampling. Such U-Net algorithms have shown excellent results in the field of seismology, including the reconstruction of seismic-signal data resolution [45,46] and P-wave FAP detection studies [47][48][49].…”

“…Additionally, the connection of feature maps between the encoder and decoder allows for fine-grained information transfer during upsampling. Such U-Net algorithms have shown excellent results in the field of seismology, including the reconstruction of seismic-signal data resolution[45,46] and P-wave FAP detection studies[47][48][49].Figure7illustrates the U-Net architecture proposed in this study. The model consists mainly of an encoder and a decoder, both of which are designed with identical layers.…”

A Feasibility Study on Microseismic Monitoring of Rock Burst in Traffic Tunnels

“…The microseismic localization methods mainly include geometric localization methods, relative localization methods, spatial domain localization methods, linear localization methods, and nonlinear localization methods. According to the different localization principles, the localization methods can also be divided into two types; those based on three-axis sensors [87][88][89][90] and those based on the arrival-time difference theory [91][92][93].…”

Intelligent Microseismic Monitoring of Mineral Equipment Manufacturing Industry Based on Deep Learning in the Artificial Intelligence Environment

“…The purpose of introducing TL in this study is to acquire knowledge by learning existing seismic data, so as to establish a microseismic event identification task model. The transfer process consists of feature transfer and pre-training (Zhang & Leng et al, 2022;Ma et al, 2023;Duan et al, 2021). The layers that can be transferred are the convolution and the pooling layer.…”