Dataset Information

Application of a convolutional neural network for seismic phase picking of mining-induced seismicity.

ABSTRACT: Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rock-bursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.

SUBMITTER: Johnson SW

PROVIDER: S-EPMC8455167 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe detection of masses on mammogram represents one of the earliest signs of a malignant breast cancer. However, masses may be hard to detect due to dense breast tissue, leading to false negative results. In this study, we aimed to explore the clinical application of the convolutional neural network (CNN)-based deep learning (DL) system constructed in our previous work as an objective and accurate tool for breast cancer screening and diagnosis in Asian women.MethodsThis retrospective analysis included 324 patients with masses detected on mammograms at Shenzhen People's Hospital between April and December 2019. (I) Detection: images were independently analyzed by two junior radiologists who were blinded to relative results. Then, a senior radiologist analyzed the images after reviewing all the relevant information as the reference. (II) Classification: masses were classified by the same two junior radiologists and in consensus by two other seniors. Images were also input into the DL system. The sensitivity of detection by junior radiologists and the DL system, effects of different factors [breast density; patient age; morphology, margin, size, breast imaging reporting and data system (BI-RADS) category of the mass] on detection, the accuracy, sensitivity, and specificity of classification, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated.ResultsA total of 618 masses were detected. The detection sensitivity of the two junior radiologists [78.0% (482/618) and 84.0% (519/618), respectively] was lower than that of the DL system [86.2% (533/618)]. Breast density significantly affected the detection by two junior radiologists (both P=0.030), but not by the DL system (P=0.385). The AUC for classifying masses as negative (BI-RADS 1, 2, 3) or positive (BI-RADS 4A, 4B, 4C, 5) for the DL system was significantly higher compared to those of the two junior radiologists, but not significantly different compared to seniors [DL system, 0.697; junior, 0.612 and 0.620 (P=0.021, 0.019); senior in consensus, 0.748 (P=0.071)].ConclusionsThe CNN-based DL system could assist junior radiologists in improving mass detection and is not affected by breast density. This DL system may have clinical utility in women with dense breasts, including reducing the impact caused by inexperienced radiologists and the potential for missed diagnoses.

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Application of a convolutional neural network for seismic phase picking of mining-induced seismicity.

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