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The Farnsworth Unit in northern Texas is a field site for studying geologic carbon storage during enhanced oil recovery (EOR) using CO2. Microseismic monitoring is essential for risk assessment by detecting fluid leakage and fractures. We analyzed borehole microseismic data acquired during CO2 injection and migration, including data denoising, event detection, event location, magnitude estimation, moment tensor inversion, and stress field inversion. We detected and located two shallow clusters, which occurred during increasing injection pressure. The two shallow clusters were also featured by large b values and tensile cracking moment tensors that are obtained based on a newly developed moment tensor inversion method using single-borehole data. The inverted stress fields at the two clusters showed large deviations from the regional stress field. The results provide evidence for microseismic responses to CO2/fluid injection and migration.

We constructed a more complete earthquake catalog in the 2021 Yangbi M 6.4 focal area by re-scanning the continuous waveforms integrated with deep learning and template matching techniques, to explore the seismogenic structures of the Yangbi mainshock and its nucleation process. The new catalog has three times the number of earthquakes than the CENC catalog, and the magnitude completeness has dropped from 1.1 to 0.5. The distribution of earthquakes indicates a broom-shaped structure consisting of several oblique secondary faults and a strike-slip main fault which strikes to 315° with 80° dipping to NE. The earthquakes extend along the fault strike about 27 km in width and 2–13 km at depth and have noticeable variations on seismicity in the mainshock’s north and south. Compared with the north, the south has denser and higher magnitude aftershocks and also has a seismic gap probably weakened by the fluid at the depth range of about 5–6 km. The foreshocks were mainly active in the 8-kilometer-long fault zone south of the mainshock, which show a steady drop in b -values over time and a migration pattern toward the epicenter of two steep jumps, stagnation, and then acceleration which finally triggered the mainshock. While in the north, seldom foreshock occurred, and the aftershocks were delayed triggered 3 hours after the mainshock, and sparsely scattered shallow at depth and small in magnitude. To summarize, the northern part of the Yangbi seismogenic fault is thought to be relatively locked, whereas the southern part has a weakening zone and promotes pre-slip. The nucleation mechanism of the mainshock and its onset at the junction of the locked and pre-slip zones may be a combination of pre-slip and cascade triggering.

This study analyzes seismic activity related to the 2025 ML 6.4 Dapu earthquake sequence in Chiayi County and Tainan City, Taiwan. By integrating a machine learning-based earthquake catalog with the Weighted Template Matching Algorithm (WTMA), over 40,000 microseismic events were detected, many of which were previously undetected due to waveform overlap with other seismic events. These microseismic detections enhance the understanding and interpretation of detailed aftershock distributions. Additionally, Centroid Moment Tensor (CMT) solutions were analyzed for further insights into the underlying seismogenic structures. In particular, the mainshock region exhibits complex structural features characterized by both east-dipping detachment faults and reactivated west-dipping basement faults occurring at varying depths. These findings highlight the intricate structural dynamics within Taiwan's actively deforming orogenic belt. The results also suggest that interactions within the fault system triggered progressive seismic activity, gradually propagating to adjacent areas. Such insights are critical for refining seismic hazard assessments and contribute to enhanced understanding of regional tectonic processes. Key points WTMA provides a more complete earthquake catalog, refining the spatial distribution of seismicity. The Dapu earthquake sequence reveals both east-dipping faults and reactivated west-dipping basement faults contributing to seismic activity. The delayed activation of seismicity in adjacent regions suggests complex post-seismic fault interactions.

This study employs a broadband seismometers network around the Zemuhe-Anninghe Fault zone in Yunnan to detect and locate earthquakes from 2015 to 2019, yielding 11091 event locations. Seismic activity is concentrated at the intersection of the Zemuhe Fault and the Xiaojiang Fault, with the locations revealing the spatial relationship between these two fault zones. The distribution of earthquakes shows significant spatial clustering and indicates numerous secondary structures beyond the main faults. Based on these high-precision microearthquake locations, 1462 focal mechanism solutions were calculated, predominantly showing strike-slip movements. At the intersection of the Zemuhe and Xiaojiang faults, a concentration of normal fault type earthquakes aligns with the characteristics of the regional extensional basin. Average focal mechanism solutions and structural stress field inversion results indicate some rotation of stress fields across different regions; from north to south, the principal stress axis rotat

Microseismic identification and localization of signals from single-component distributed optical fiber acoustic sensors (DAS) in vertical wells are limited by low signal-to-noise ratio and lack of directional information, making effective signal identification and accurate localization difficult. Improving the detection rate and accuracy of such data events is helpful for analyzing the effect of fracturing. To address this, this paper proposes a method for automatically picking and locating microseismic events based on dual fitting modeling and waveform inversion. First, empirical mode decomposition (EMD) is used to adaptively decompose and reconstruct the original DAS signal to filter out approximately 80% of high-frequency noise (noise above 200 Hz). Second, the classic short-time average/long-time average energy ratio algorithm is used to pick all “event points.” Finally, DBSCAN density clustering and RANSAC robust fitting are combined to perform secondary screening and fitting modeling of the “event points” to obtain the continuous event arrival time distribution along the well section direction, and the spatial location of the seismic source is inverted based on the fitting results. Tested with experimental data from Well XX, the automatic detection rate reached 96%, and the accuracy of machine detection compared with manual judgment reached 95%.

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of computationally demanding forward elastic-wave propagation simulations. Here we train and use an ensemble of Gaussian process surrogate meta-models, or proxy emulators, to accelerate the generation of accurate template seismograms from random microseismic event locations. In the presence of multiple microseismic events occurring at different spatial locations with arbitrary amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and narrow curving degeneracies. This is a challenging computational task even for state-of-the-art Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple microseismic events in a strong noise background using Bayesian inference, in particular, the Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal likelihood that permits hypothesis testing for discriminating true vs. false event detection. Bayesian evidence-based reasoning is helpful in identifying real microseismic events as opposed to the environmental noise. The geophysical challenge here is the high-computational burden for simulating noiseless template seismic responses for explosive type events and combining them together having different amplitudes and origin times. We use Gaussian process based surrogate models as proxy for multi-receiver seismic responses to be used for the Bayesian detection of microseismic events in a heterogeneous marine velocity model. We used the MultiNest sampler for Bayesian inference since in the presence of multiple events, the likelihood surface becomes multimodal. From the sampled points, a density-based clustering algorithm is employed to filter out each microseismic event for improved mode separation and obtain the posterior distribution of each event in a joint 5D space of amplitude, origin time, and three spatial co-ordinates. Choice of the resolution parameter in MultiNest sampler ( N live ) is also crucial to obtain accurate inference within reasonable computational time and resources and have been compared for two different scenarios ( N live  = 300, 500). A data analytics pipeline is proposed in this paper, starting from GPU based simulation of microseismic events to training a surrogate model for cheaper likelihood calculation, followed by 5D posterior inference for simultaneous detection of multiple events.

Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network (CNN-MDN) model was established and well trained to a high degree of accuracy using a dataset with 16,000 preprocessed waveforms. By comparison with other methods, 4000 waveforms were tested to evaluate the precision, recall, and F1-score. The results revealed that the CNN-MDN demonstrated the highest performance in microseismic detection. Moreover, the low sensitivity of the CNN-MDN to noise of different intensities was proved by testing on semi-synthetic data. The model also possesses good generalization ability and superior performance capability for microseismic detection under different geological structure backgrounds, and it can correctly detect the microseismic events with Mw ≥ 0.5. These preliminary results show that the CNN-MDN can be directly applied to unprocessed microseismic data and has great potential in real-time microseismic monitoring applications.

The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise interference. This paper proposes a microseismic P-wave arrival time automatic picking model that integrates physical constraints with a deep learning architecture. This study trained and optimized the model using a high-quality, manually labeled dataset. A systematic comparison with the AR picker algorithm and the short-term–long-term average ratio method revealed that this model achieved a precision of 96.60%, a recall of 90.59%, and an F1 score of 93.50% on the test set, with a P-wave arrival time-picking error of less than 20 ms. The average arrival time error was only 5.49 ms, significantly outperforming traditional methods. In cross-mining area generalization tests, the model performed excellently in two mining areas with consistent sampling frequencies (1000 Hz) and high signal-to-noise ratios, demonstrating good engineering transferability. However, its performance decreased in a mining area with a higher sampling rate and stronger noise, indicating its sensitivity to data acquisition parameters. This study developed a high-precision, robust, and potentially cross-domain adaptive model for automatically picking microseismic P-wave arrival times. This model provides support for the automation, precision, and intelligence of coal mine microseismic monitoring systems and has significant practical value in promoting real-time early warning and risk prevention for mine dynamic hazards.

Detection of arrival times of microseismic events is one of the most fundamental steps in the application of microseismic monitoring. However, accurately determining arrival time remains challenging due to low signal-to-noise ratio and the complexity of geological structures in microseismic monitoring. To address this, we propose a new arrival time detection strategy, which is implemented by using a transformed difference between multiwindow energy ratios method (TDER). First, we use a modified difference between multiwindow energy ratios (DER’) to characterize microseismic traces. Then, we introduce a detection method for detecting arrival times using a transformed DER’, i.e., TDER. The transforming process can extract the feature of arrival points in manual picking. To establish and validate the TDER method, we use pseudo-synthetic and field data. The method’s sensitivity to varying noise levels was tested using pseudo-synthetic data combined with real noise. Two field datasets were collected from a microseismic monitoring system deployed on an unstable rock face and an active tomography survey on a mountain, respectively. This method demonstrated good performance in low signal-to-noise ratio (SNR) scenarios and outperformed traditional STA/LTA and the original DER picking methods with superior accuracy and fewer failed detection. We also examined how changes in parameters affect the TDER picking results. The developed method is an adaptive and nearly parameter-free method, which can be easily implemented.