Passive source seismic interferometry is a cost-effective geophysical method that converts noise signals into valuable information. The fidelity of the resultant common-shot gather is pivotal for effective imaging. The quality of reconstructed records via seismic interferometry directly correlates with the duration of background noise observation. However, practical applications often encounter difficulties in obtaining stable and usable long-duration observations of noise-based passive seismic records. Short-duration observations may introduce spurious physical events, thereby compromising the reliability of seismic wavefield imaging and geological interpretation. In this study, we introduce MDUNETR, an advanced passive data reconstruction network amalgamating Transformer and Multi-scale Dense Blocks (MDB) to enhance accuracy. By integrating Transformer and MDB, the network effectively captures both global and local information. Utilizing the MDUNETR network, we can reconstruct accurate passive source interferometric seismic records from short-duration noise interference signals. This overcomes the time limitations imposed by seismic interferometry on the original noise records. Theoretical data applications demonstrate the stability and fidelity of the seismic records reconstructed by this network, ensuring reliable results.

In passive source seismic surveys, signal continuity and signal-to-noise ratios have always tended to be low. On the one hand, since passive-source seismic surveys are often used for large-scale illumination of subsurface formations, the distances between receivers and sampling point intervals tend to be large. On the other hand, interference from coherent noise and spurious in-phase axes is unavoidable in passive source reconstruction recordings because of the signal originating from noise in the subsurface. All these problems lead to the continuity and signal-to-noise ratio of the virtual shot reconstructed from passive source seismic surveys are not guaranteed, which affects further processing and seriously limits the application of passive source seismic surveys. The traditional interpolation reconstruction methods cannot take noise suppression into account, or require additional operations to achieve both interpolation reconstruction and denoising. Based on this, this paper utilizes the powerful data processing ability of convolutional neural networks to design a global multi-scale fusion residual shrinkage network (GMF-RS) to solve the above passive source seismic exploration problem. It is tested that the trained network not only eliminates coherent noise and false events, but also improves the continuity in horizontal and vertical directions, enhances and extracts the effective signals, and provides better virtual shot records for subsequent seismic data processing. In addition, we designed a dual-input network and introduced active source seismic records as a complement to the passive source virtual seismic records, so that the processed waveforms can show better details.

Recognition of microseismic events is the primary task of microseismic monitoring. Aiming at the low signal-to-noise ratio (SNR) of weak microseismic events and the high cost of labeling them, an unsupervised learning method for recognizing microseismic events is proposed. The method first recognizes microseismic events from monitoring data segments by simultaneous deep clustering and then performs a second clustering to further pick the first arrival times of the detected microseismic events by multistage deep clustering. The networks in this two-step clustering framework are built on a newly designed residual shrinkage dense block (RSDB). To better suppress the noise in microseismic data, RSDB adds a densely connected hybrid dilated convolution and an improved threshold module to the deep residual shrinkage network. The autoencoder built by the RSDB and U-Net architecture is combined with simultaneous deep clustering and multistage deep clustering to recognize microseismic events and their first arrival times, respectively. Finally, tests on the synthetic data and field microseismic data demonstrate the feasibility and superiority of the proposed method.

The performance of the microseismic monitoring technique depends greatly on the accuracy of microseismic event localization. Recently, machine learning (ML) methods have been extensively implemented for the localization of microseismic events. These neural networks are typically trained using numerous microseismic events labeled with known source locations. Obtaining enough microseismic events with good source locations can be difficult and costly. To overcome this shortcoming, we present a microseismic events localization method using semi-supervised generative adversarial networks (GANs). We utilize limited labeled seismograms and large amounts of unlabeled seismograms to train the semi-supervised GANs, thus improving the prediction ability of the networks. Finally, we evaluate the performance of the proposed method using synthetic microseismic data and field data. Comparison with the supervised learning methods on the same microseismic data shows that the proposed method can significantly improve the accuracy of locating microseismic sources in the lack of sufficient source labels.

The automatic and accurate segmentation of the prostate cancer from the multi-modal magnetic resonance images is of prime importance for the disease assessment and follow-up treatment plan. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the generated attention maps of different modalities enable the model to transfer significant and discriminative information that contains more details. Moreover, a novel spatial correlated feature fusion module is further employed for learning more complementary correlation and non-linear information of different modality images. We evaluate our model in five-fold cross-validation on 358 MRI images with biopsy confirmed. Without bells and whistles, our proposed network achieves state-of-the-art performance on extensive experiments.

The automatic segmentation of prostate cancer (PCa) from the multi-modal magnetic resonance imaging (MRI) is of prime importance for the initial staging and prognosis of patients. Nevertheless, the challenge of how to utilize multi-modal image features more efficiently still needs resolving, especially in the segmentation scenario. In this paper, we propose a crossmodal self-attention distillation network that can fully exploit the encoded information of the intermediate layers from different modalities, and the learned attention maps of different modalities are then transferred among modalities to provide significant spatial information with more details incorporated. Furthermore, we propose a novel spatial correlated feature fusion module that is able to learn more complementary correlation and nonlinear information from different modality images. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on the PCa MRI dataset, and the experiment results demonstrate that our proposed approach could achieve state-of-the-art performance.