![]() These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. We achieved near-perfect predictions for both event classification and localization. The same model is trained again for locating the event with the output layer of the model replaced with linear units. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. The more » objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. ![]() Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. The generality of the used CF-CNN was thus demonstrated, while the use of the graph-theoretical descriptors assisted in interpreting the predicted results. Our graph analysis suggests that the mean degree and number of polygons for networks with larger errors tend to lie further from the mean than those with lower errors. Using topology measures, such as the Wiener index and the average shortest path length along with two similarity measures, we showed that all networks from the test set were within the range of the ones from the training set, suggesting that the training set covered the chemical space of interest quite well. The graph-theoretical descriptors were developed in order to analyze the properties of the full database and interpret the predictive power of the CF-CNN. In addition, clusters of sizes not included in the training set exhibited errors of the same magnitude, indicating that the CF-CNN ptotocol is general enough to accurately predict energies of networks for both smaller and larger sizes than those used during training. =10, 30) yielded a mean absolute error of 0.002$$\pm$$0.002 kcal/mol per water molecule, giving the trained CF-CNN the highest accuracy of any neural network-based surrogate model to date. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies. Analysis on the class activation maps of the last CNN layer further discloses more » the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. ![]() Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. For the first time, one-to-one material identification was achieved. To augment the data for training the model, noise was extracted from experimental data and shuffled then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery.
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