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Non-uniqueness in the inversion of seismic data can be considered the main challenge for the application of such data. Prior information, such as downhole data, can be used to control this problem. However, in most cases, prior information is not available; accordingly, geophysicists/analysts have to suppose a primary model for the observed data and then find the final adequate layered earth model through trial and error. In this study, a new technique was developed based on the artificial neural network (ANN) for the inversion of seismic refraction data in the absence of prior information. In this regard, a sequential multilayer perceptron (SMLP) was proposed, which integrates the sequential information of the model parameters to predict a reasonable layered earth model. In fact, at first, a multilayer perceptron (MLP) (First-MLP) was trained by synthetic data; then, a layered earth model, i.e., the primary model, was predicted for the observed data. Next, using the primary model, a range for each of the model parameters, i.e., thickness and P-wave velocity, for each layer was defined. Subsequently, new synthetic samples were generated based on the determined ranges. Finally, using another MLP (Second-MLP), which was trained by the new synthetic samples, the final model for the observed data was estimated. The proposed method was also tested by employing different synthetic data with and without noise. Moreover, the SMLP inversion technique was used to analyze the experimental seismic refraction dataset at a dam construction site. The results for both synthetic and experimental data confirmed the reliability of the proposed SMLP inversion technique.
Non-uniqueness in the inversion of seismic data can be considered the main challenge for the application of such data. Prior information, such as downhole data, can be used to control this problem. However, in most cases, prior information is not available; accordingly, geophysicists/analysts have to suppose a primary model for the observed data and then find the final adequate layered earth model through trial and error. In this study, a new technique was developed based on the artificial neural network (ANN) for the inversion of seismic refraction data in the absence of prior information. In this regard, a sequential multilayer perceptron (SMLP) was proposed, which integrates the sequential information of the model parameters to predict a reasonable layered earth model. In fact, at first, a multilayer perceptron (MLP) (First-MLP) was trained by synthetic data; then, a layered earth model, i.e., the primary model, was predicted for the observed data. Next, using the primary model, a range for each of the model parameters, i.e., thickness and P-wave velocity, for each layer was defined. Subsequently, new synthetic samples were generated based on the determined ranges. Finally, using another MLP (Second-MLP), which was trained by the new synthetic samples, the final model for the observed data was estimated. The proposed method was also tested by employing different synthetic data with and without noise. Moreover, the SMLP inversion technique was used to analyze the experimental seismic refraction dataset at a dam construction site. The results for both synthetic and experimental data confirmed the reliability of the proposed SMLP inversion technique.
Borehole seismic methods have been widely used for characterizing the shallow subsurface. Accurate analysis of their data is aided by a solid understanding of the borehole sources' characteristics. This study presents a field evaluation of two impulsive borehole seismic sources (Trident's Scorpion sparker and RT Clark's Ballard weight drop) in crosswell and reverse vertical seismic profile (RVSP) geometries at a Gulf of Mexico coastal site with two shallow vertical wells. The acquired data is then utilized to characterize the near-surface coastal sediments. The Scorpion source generated P-wave dominant frequencies that were recorded as 650 Hz and 250 Hz in the crosswell and RVSP geometries, respectively. For the Ballard source in the two geometries, the P-wave dominant frequencies were 1100 Hz and 250 Hz. We were also able to pick direct S-wave arrivals with the Ballard source, and their dominant frequencies were 100 Hz and 40 Hz for in situ and surface recordings, respectively. The average signal-to-noise ratio (SNR) recorded with the Scorpion data for the crosswell geometry and RVSP, respectively, is 13 and 6, and for the Ballard source, 62 and 30. We also investigated the source radiation patterns and signature wavelets. Seismic tomography was performed for the area between the two wells. Low P-wave and S-wave velocities correspond to three fresh water-saturated sand zones identified from drilling cuttings and previous well log data. Also, the plot of velocity of P-wave (Vp) versus S-wave (Vs) fits reasonably to the Mudrock Line. Both sources can excite repeatable energetic seismic signals up to 150 m away and could be useful in many geotechnical settings and even single-well imaging.
On 8 January 2022, a Moment Magnitude (Mw) 6.7 earthquake occurred in Menyuan, China. The epicenter was located in the western segment of the Lenglongling fault of the Qilian-Haiyuan fault zone. In this area, the Mw 5.9 Menyuan earthquake on 26 August 1986 and the Mw 5.9 Menyuan earthquake on 21 January 2016 successively occurred. The seismogenic structures of the 1986 and 2016 earthquakes are on the Northern Lenglongling fault, which is a few kilometers away from the Lenglongling fault. After the 2022 Menyuan earthquake, we collected GF-7 and Sentinel-1 satellite images to measure the surface deformation of the earthquake sequence. Based on the elastic dislocation theory, the fault model and fault slip distribution of the 2016 and 2022 Mengyuan earthquakes were inverted using coseismic surface displacements. The results show that the 2016 event is a reverse event, with the maximum coseismic surface displacement on LOS reaching 8 cm. The strike, dip, and rake of the earthquake rupture were 139°, 41°, and 78°, with the maximum slip reaching 0.6 m at a depth of 8 km. The surface rupture of the 2022 Mw 6.7 earthquake ran in the WNW–ESE direction with a maximum displacement on LOS of 72 cm. The main seismogenic fault of the 2022 event was the western segment of the Lenglongling fault. The strike, dip, and rake of the rupture were 112°, 85°, and 3°, with the maximum slip reaching 4 m at a depth of 4 km. The Coulomb failure stress change shows that the earthquake sequence generated a considerable positive Coulomb failure stress of more than 2 bar. These observations suggest that the earthquake sequence around Menyuan is mainly governed by the activities of the Lenglongling fault around the northeastern Tibetan Plateau. In addition, their sequential occurrences could be related to earthquake-triggering mechanisms due to stress interaction on different deforming faults. Thus, the Lenglongling fault has received a great amount of attention regarding its potential earthquake hazards.
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