Microseismic monitoring has emerged as a critical technique for exploiting tight reservoirs, particularly those involving hydraulic fracturing, such as shale gas and coalbed methane. The conventional migration-based stacking location method for surface microseismic events relies heavily on the accuracy of the velocity model. However, obtaining an accurate three-dimensional (3D) velocity model is often challenging, prompting the common use of one-dimensional (1D) layered velocity models derived from well-logging data or constant velocity models calibrated through perforation shots. To enhance the precision of microseismic event localization and improve practical applicability, we introduce a refined migration-based stacking location method incorporating two depth-dependent effective parameters: stacking velocity and heterogeneity factor. Two effective parameters were found to describe wave raypath through heterogeneity media, which can be estimated by semblance-based scanning technology. Furthermore, to address potential errors in the velocity model and residual statics arising from topographical variations, we incorporate microseismic event moveout-corrected gathers for residual static corrections. This additional step further refines the accuracy of microseismic event locations. Another advantage of our proposed method is its ability to directly compute the theoretical travel time during the migration-based location process, eliminating the need for precomputing and storing a traveltime table. The efficacy and practicality of our method are demonstrated through applications to both synthetic model data and field data examples.

Shear-wave velocity is a key parameter for subsurface material characterization, and Rayleigh wave inversion is widely used to retrieve S-wave velocity profiles in near-surface imaging and ambient noise tomography. While the fundamental mode is typically observable, higher modes are often absent or limited due to field conditions, complicating the inversion process. Existing frameworks require prior identification of mode numbers, which traditionally relies on either manual supervision or repeated inversions. Manual identification is susceptible to subjective errors and inefficiency, whereas repeated inversions depend heavily on the accuracy of the initial model, reducing robustness. To address these limitations, this study proposes a novel automatic method for higher-mode identification in Rayleigh wave dispersion curves using a broad learning network. The proposed method directly models the relationship between higher-mode dispersion curves and their corresponding mode numbers, leveraging the known mode number of the fundamental mode. Validation through numerical simulations and field data applications demonstrates its effectiveness in accurately identifying higher-mode numbers. Furthermore, inversion tests incorporating the identified mode numbers confirm that the proposed method enhances inversion reliability, mitigating the adverse effects of mode misidentification on S-wave velocity estimation. Although the method identifies only one mode number at a time and requires retraining when applied to new field data, the high training efficiency of the BL network enables rapid adaptation to different dataset. By providing a scalable and computationally efficient solution for multimodal dispersion curve inversion, this study advances subsurface exploration in geophysical applications.

Microseismic event locations provide critical insights into fracture locations and stress conditions within rock formations, which are essential for seismic hazard monitoring. In practice, pick-based location methods are widely utilized due to their high computational efficiency. However, the accuracy of picking is compromised when the signal-to-noise ratio (SNR) is low. Source location utilizing equal differential time (EDT) surfaces between station pairs represents an effective strategy for mitigating picking errors. Typically, EDT surfaces are constructed using either a fixed width or a fixed probability density function (pdf), which presents challenges in simultaneously achieving high resolution and accuracy. To address this issue, we propose an adaptive probabilistic imaging location method that constructs EDT surfaces by incorporating an adaptive pdf linked to the SNR of arrival picking data. The location probability imaging function is defined as the product of the independent EDT surfaces used for locating sources. For high-SNR data, where picking errors are typically small, the adaptive pdf converges more rapidly than the Gaussian distribution, yielding higher resolution by assigning lower probabilities to locations distant from true positions. For low-SNR data, where picking errors are typically large, the adaptive pdf exhibits heavy-tailed characteristics with a slower rate of decrease, enhancing the accuracy by assigning higher probabilities to likely true locations. The effectiveness and stability of the method are validated through theoretical analyses and synthetic data tests. Application to mine microseismic data indicates that the proposed method improves the accuracy and resolution of microseismic event locations relative to other methods.

Electromagnetic perturbations associated with earthquakes and volcano eruptions have been intensively documented in the past decades, from both ground and satellite observations. However, the magnetic signals associated with crustal activity are usually very weak and mixed with global geomagnetic signals originating from external sources. Thus, one of the key issues in seismo-electromagnetic study is to identify local geomagnetic signals from global magnetic pulsations. The interstation transfer function can recover the global magnetic pulsations at the observatory by using the data of reference station, providing an effective way for signal discrimination. To further improve the accuracy of global magnetic signal estimation, in this study, we develop a new method for interstation transfer function calculation based on multivariate wavelet coherence using adaptive selection of time window lengths for different periods. Test on real data demonstrates that the global signals of the external source are accurately estimated, including the horizontal X and Y components and the vertical Z component of the observatory. The results of using different reference stations confirm the robustness of the proposed method. It is proved that the method can be used to eliminate the external ionospheric source signals effectively and identify local magnetic field signals lying in the background at the observatory. The proposed method can be useful in seismo-electromagnetic signal identification and extraction.

Rayleigh wave inversion is a reliable method for inverting the shear-wave ($S$-wave) velocities to reflect the stiffness status of the soil and rock masses of the subsurface. The optimization potential of neural networks in the inversion task is gaining recognition among researchers. Regarding neural-network-based Rayleigh wave inversion, a closer functional relationship between the training samples and the unknown function to be modeled indicates improved inversion performance. The traditional sampling method involves randomly generating samples within a predefined search space, which can result in some samples deviating from the actual functional relationship, thus reducing the accuracy and stability of the inversion. However, few studies consider the sample selection issue in the inversion process based on neural networks. This study proposes a sample selection method for selecting more appropriate training samples to overcome the neglect of sample selection, enhancing the functional modeling of neural networks for Rayleigh wave inversion. The implementation of the proposed sample selection method involves two procedures. First, the random samples are generated within a predefined search space to create a pool of samples. Afterward, the mean moving correlation coefficients of the samples inside the pool are calculated to select more suitable samples for network training based on the moving correlation calculation. Numerical simulations and field data applications demonstrate the necessity and effectiveness of the proposed sample selection method for neural-network-based Rayleigh wave inversion. It is concluded that the proposed method effectively enhances the performance of$S$-wave velocity estimation through Rayleigh wave inversion using neural networks.

Search space design is a fundamental procedure for the inverse problem in Rayleigh wave exploration. In practice, such a crucial task mainly depends on researchers’ experience-based judgment. However, intricate near-surface materials may lead to erroneous search space design, and consequently, accurate inversion results cannot always be ensured. By the forward calculation, it is found that there is a strong relationship between the fundamental dispersion curves of a given earth model and its miniature model; namely, if the parameters (layer thicknesses and S-wave velocities) of the given earth model are shrunk at a certain scale, the phase velocities on the fundamental dispersion curve will decrease at the same scale. Taking advantage of this relation, we propose a broad learning framework for search space design in a data-driven manner. First, a training set of minified models is generated using the forward calculation of dispersion curves. Then, a mapping relationship between dispersion curves and minified earth models is built via a broad learning network. Finally, a search space for the actual earth model is designed using the minified model based on the relation. As the ranges of parameters in the minified model are much smaller, the network can find the model parameters for the corresponding dispersion curve quickly and easily. Numerical simulations and field data applications demonstrate the reliability and effectiveness of the proposed method for search space design. It is concluded that the proposed method can promote accurate estimation of S-wave velocities by Rayleigh wave inversion without experience-based judgment.