With the increase in exploration target complexity, more parameters are required to describe subsurface properties, particularly for finely stratified reservoirs with vertical transverse isotropic (VTI) features. We have developed an anisotropic amplitude variation with offset (AVO) inversion method using joint PP and PS seismic data for VTI media. Dealing with local minimum solutions is critical when using anisotropic AVO inversion because more parameters are expected to be derived. To enhance the inversion results, we adopt a hierarchical inversion strategy to solve the local minimum solution problem in the Gauss-Newton method. We perform the isotropic and anisotropic AVO inversions in two stages; however, we only use the inversion results from the first stage to form search windows for constraining the inversion in the second stage. To improve the efficiency of our method, we built stop conditions using Euclidean distance similarities to control iteration of the anisotropic AVO inversion in noisy situations. In addition, we evaluate a time-aligned amplitude variation with angle gather generation approach for our anisotropic AVO inversion using anisotropic prestack time migration. We test the proposed method on synthetic data in ideal and noisy situations, and find that the anisotropic AVO inversion method yields reasonable inversion results. Moreover, we apply our method to field data to show that it can be used to successfully identify complex lithologic and fluid information regarding fine layers in reservoirs.

The study of thin-bed seismic response is an important part in lithologic and methane reservoir modeling, critical for predicting their physical attributes and/or elastic parameters. The complex propagator matrix for the exact reflections and transmissions of thin beds limits their application in thin-bed inversion. Therefore, approximation formulas with a high accuracy and a relatively simple form are needed for thin-bed seismic analysis and inversion. We have derived thin-bed reflection and transmission coefficients, defined in terms of displacements, and approximated them to be in a quasi-Zoeppritz matrix form under the assumption that the middle layer has a very thin thickness. We have verified the approximation accuracy through numerical calculation and concluded that the errors in PP-wave reflection coefficients $R_{\mathrm{PP}}$ are generally smaller than 10% when the thin-bed thicknesses are smaller than one-eighth of the PP-wavelength. The PS-wave reflection coefficients $R_{\mathrm{PS}}$ have lower approximation accuracy than $R_{\mathrm{PP}}$ for the same ratios of thicknesses to their respective wavelengths, and the $R_{\mathrm{PS}}$ approximation is not acceptable for incident angles approaching the critical angles (when they exist) except in the case of extremely strong impedance difference. Errors in phase for the $R_{\mathrm{PP}}$ and $R_{\mathrm{PS}}$ approximation are less than 10% for the cases of thicknesses less than one-tenth of the wavelengths. As expected, a thinner middle layer and a weaker impedance difference would result in higher approximation accuracy.

Tectonically-deformed coal (TDC) is a potential source of threats to coal-mining safety. Finding out the development and distribution of TDCs is a difficult task in coalfield seismic explorations. Based on the previous investigations, the P- to S-wave velocity ratio (α/β) is a stable parameter for the identification of TDCs and most TDCs have α/β values of less than 1.7. Here, a TDC detection method using a model-based joint inversion of the multi-component seismic data is proposed. Following the least square theories, the amplitude variation with offset gathers of the PP- and PS-waves are jointly inverted into the corresponding α/β values. The prior models generated from the P- and S-wave velocity and density logs are employed in the joint inversion to enhance the inversed models. Model test results show that the model-based inversion is of high anti-noise ability and has a good recognition ability of TDCs. The proposed method is applied to a work area of the Guqiao mine in China. The TDCs developed in coal seam 13-1 are effectively identified according to their inverted α/β values of less than 1.7. The detection result is verified by the well and tunnel excavation information.

Compared with the surface, the deep environment has the advantages of allowing “super-quiet and ultra-clean”-geophysical field observation with low vibration noise and little electromagnetic interference, which are conducive to therealization of long-term and high-precision observation of multi-physical fields, thus enabling the solution of a series of geoscience problems. In the Panyidong Coal Mine, where there are extensive underground tunnels at the depth of 848 m belowsea level, we carried out the first deep-underground geophysical observations, including radioactivity, gravity, magnetic, magne-totelluric, background vibration and six-component seismic observations. We concluded from these measurements that (1) the background of deep subsurface gravity noise in the long-period frequency band less than 2 Hz is nearly two orders ofmagnitude weaker than that in the surface observation environment; (2) the underground electric field is obviously weaker thanthe surface electric field, and the relatively high frequency of the underground field, greater than 1 Hz, is more than two orders of magnitude weaker than that of the surface electric field; the east-west magnetic field underground is approximately the same asthat at the surface; the relatively high-frequency north-south magnetic field underground, below 10 Hz, is at least one order ofmagnitude lower than that at the surface, showing that the underground has a clean electromagnetic environment; (3) in additionto the high-frequency and single-frequency noises introduced by underground human activities, the deep underground spacehas a sig-nificantly lower background vibration noise than the surface, which is very beneficial to the detection of weakearthquake and gravity signals; and (4) the underground roadway support system built with ferromagnetic material interferesthe geomagnetic field. We also found that for deep observation in the “ultra-quiet and ultra-clean” environment, the existinggeophysical equipment and observation technology have problems of poor adaptability and insufficient precision as well asdata cleaning problems, such as the effective separation of the signal and noise of deep observation data. It is also urgent tointerpret and comprehensively utilize these high-precision multi-physics observation data.