Piezoelectric transducer is a key component of acoustic well-logging tool. Transducers used in acoustic well-logging tools usually have limited bandwidth, and their dominant frequencies severely drift with drastic change of borehole temperature when tools are working downhole. An appropriate impedance matching is necessary to broaden the operating bandwidth of the transducer and improve the power output stability of the acoustic emission. In this study, an electrical impedance matching (EIM) method for piezoelectric transducer is proposed based on Butterworth response and network theory. The method can broaden the bandwidth of narrowband transducers while ensuring stable transducer power gain (TPG). A band-pass filtering circuit and its low-pass prototype are constructed on the basis of the equivalent circuit of the piezoelectric transducer and the external impedance matching circuit. According to the filtering characteristics of the filter circuit and a target TPG function, we derive the analytical formulas for calculating power transmission characteristics and electronic component values of arbitrary order matching network. Using different electronic design automation (EDA) software, we simulated the matching network of a specific transducer obtained by the theoretical calculations. Then, we built an experimental system to verify the performance of the matching network. Theoretical calculation, simulation and experimental results demonstrate that the impedance matching network obtained by the proposed method can effectively broaden the operating bandwidth of narrowband transducers and provide stable output power.

A fast calculated kernel matrix method is coupled with a compressed matrix technique to solve the large-scale gravity forward-modeling problem. This method accelerates the coefficient matrix computation by reducing the arctangent, logarithm, and multiplication functions in the prismatic gravity analytical expression. In addition, the use of the compressed matrix technique presents a significant advantage in that it does not require the storage of redundant kernel matrices, further reducing the memory requirements and computation time. Moreover, the discrete convolution of the compressed matrix with density is executed through the 2D fast Fourier transform (FFT). Two typical synthetic models are used to test the performance of the novel algorithm. The results demonstrate that the developed algorithm is approximately 15 times faster than the traditional algorithm. Concurrently, it demands almost 1/7th of the memory while ensuring equivalent computational accuracy. To further illustrate the capability of the algorithm, we apply our method to the terrain correction of an airborne gravity data set using a real digital elevation model. Our approach efficiently calculates 250,000 observation points across 25 million cells in only 5.42 s, compared with 234.81 s for the latest 2D Gauss-FFT method.

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

A new mise-a-la-masse (MALM)-based induced-polarization (IP) method is developed for heavy-metal contamination monitoring, in which the charged source in a nonperfect conductor and contact ...

An important way to improve the resolution of electromagnetic exploration is by using known seismic and logging data. Based on previous work, 2D magnetotelluric (MT) parallel-constrained-inversion, based on an artificial-fish-swarm algorithm is further developed. The finite-difference (FD) method with paralleling frequency is used for 2D MT-forward-modeling, to improve computational efficiency. The results of the FD and finite-element (FE) methods show that the accuracy of FD is comparable to FE in the case of suitable mesh-generation; however, the calculation speed is ten times faster than that of the FE. The artificial-fish-swarm algorithm is introduced and applied to parallel-constrained-inversion of 2D MT data. The results of the synthetic-model test show that the artificial-fish-swarm-inversion based on paralleling forward can recover the model well and effectively improve the inversion speed. The processing and interpretation results of the field data are verified by drilling, which shows that the proposed inversion-method has good practicability.