Leachate leakages from municipal landfills are significant environmental problems that threaten groundwater and soil resources. Geophysical techniques, such as the self-potential (SP) method, are commonly used to detect and delineate underground contaminated plumes. However, traditional inversion techniques for SP source information require precise knowledge of subsurface conductivity, which can be challenging to obtain. In this study, we proposed an inversion algorithm, called SP-Net, based on a convolutional neural network that can directly train the intrinsic relationship between SP signals and the location of SP sources, while being a great performance within a comparative heterogeneous resistivity setting. In this work, we used the U-shaped network as the structure of the SP-Net and treated the problem of locating SP sources as an image segmentation problem. We designed a sandbox experiment model by adding humus and the microorganism calledShewanella oneidensisMR-1 to simulate the scenario of microbial-mediated SP generation, which typically exists at organic-rich contaminated sites. We used this situation to generate numerous 3-D SP datasets for SP-Net training. We tested the SP-Net on both synthetic testing datasets and the measured laboratory case, and the results show the effectiveness of SP-Net. We also developed a field-scale synthetic model for landfills as a preliminary attempt to test the SP-Net in practical applications, and the results reveal that our study has a valuable reference for potential future applications. Our work provides a promising tool to locate SP sources from both laboratory and field-scale SP data.

Seismic impedance inversion is an effective technique for estimating subsurface rock attributes from poststack data. The inversion efficacy, however, can be compromised by factors such as data noise, initial model, and regularization constraints. Traditional single trace inversion usually exhibits obvious spatial discontinuities due to the absence of geometric constraints on the reconstructed impedances, especially in datasets with high noise levels, while the inversion may easily get trapped in local minima because of a poor initial model. To improve the imaging quality, we develop a structure-guided modified total variation (SGMTV) regularization scheme. This introduces seismic features extracted from seismic data into the modified total variation (MTV) regularization scheme, aiming to simultaneously reconstruct multitrace impedances with enhanced structures and suppressed model noise. Moreover, the SGMTV inversion is integrated with a time-domain multiscale strategy to alleviate its dependence on initial model. Both synthetic and field examples demonstrate the superiority of the multiscale SGMTV inversion compared with the conventional methods. The robust performance establishes it as a reliable tool for seismic imaging and interpretations.

Seismic inversion is an effective way to investigate the lithology and fluid in hydrocarbon-bearing reservoirs. In addition to obtaining inversion results, probabilistic seismic prediction can be used for uncertainty evaluation. Its prior probability is usually assumed to follow a specific distribution, which limits the prediction accuracy. By considering both the p-norm and total variation (TV) constraints, an improved prior is proposed. The p-norm constraint is first introduced into the probabilistic seismic prediction, which can be reduced to other probability distribution forms. With the p-norm constraint, the probability density function is updated. Then, the posterior probability with both the p-norm and TV constraints is re-derived. By analysis, the proposed approach helps to preserve the boundary and highlight the sparsity. Compared with a specific prior distribution, the proposed prior is more flexible and can effectively improve the prediction accuracy. The proposed approach is discussed in detail in terms of probabilistic prediction by using synthetic data. Both synthetic data and field data tests demonstrate the superiority of the proposed prior. The construction of the prior with p-norm and TV constraints is of great help to improve probabilistic prediction.

The probabilistic prediction approach can be used not only for obtaining the maximum posterior probability solution but also for uncertainty evaluation. Its prior distribution has a significant impact on the prediction result. An improper prior assumption may lead to prediction deviation. To improve the prediction accuracy of elastic parameters, a Laplace prior with total variation (TV) constraint is introduced in the probabilistic prediction. First, the effect of TV constraint on the probability distribution of elastic parameters is analyzed in detail. Then, two approaches are proposed to handle the cases where the elastic parameters have blocky boundaries and no blocky boundaries: probabilistic prediction scheme for elastic parameters with blocky boundaries and probabilistic prediction scheme with blocky lithology prior constraint. The former imposes a sparse constraint on the elastic parameters, while the latter imposes a sparse constraint on the TV processing lithology. Their posterior probabilities are re-derived. Considering that the discrete lithology is more likely to be blocky compared with the continuous elastic parameters, the sparse lithology constraint can handle more general cases. In addition, this approach allows for lithology prediction. The applications of numerical examples and field seismic data verify the feasibility of the proposed approaches.

Due to the special petrophysical properties of tight reservoirs, such as poor connectivity and low porosity, conventional rock physics models show limitations. Based on an inclusion-based method, a new formula containing fluid pressure is derived without an equilibration assumption of fluid pressures in the inclusions. Then, the formula is simplified with an equivalent pore structure to yield a new fluid identification parameter, the inclusion-based effective fluid modulus (IEFM). By analysis, this fluid identification factor is quite sensitive to water saturation for different pore connectivity. A well-logging data test shows the superiority of the proposed model in identifying tight gas-bearing zones. Seismic data application also demonstrates the validity of the proposed model and the predicted results match well with the well-logging data. In fluid identification, two probabilistic estimation methods are used: Bayes posterior prediction framework is a combination of Bayes’ theory and a deterministic rock physics model; Bayes discriminant method is a statistical rock physics method. The proposed IEFM is a novel identification parameter for tight gas-bearing reservoirs, which can have many applications in the exploration of tight reservoirs.

3-D magnetotelluric (MT) forward modeling has always been faced with the problems of high memory requirements and long computing time. In this article, we design a scalable parallel algorithm for 3-D MT finite element modeling in anisotropic media. The parallel algorithm is based on the distributed mesh storage, including multiple parallel granularities, and is implemented through multiple tools. Message-passing interface (MPI) is used to exploit process parallelisms for subdomains, frequencies, and solving equations. Thread parallelisms for merge sorting, element analysis, matrix assembly, and imposing Dirichlet boundary conditions are developed by Open Multi-Processing (OpenMP). We validate the algorithm through several model simulations and study the effects of topography and conductivity anisotropy on apparent resistivities and phase responses. Scalability tests are performed on the Tianhe-2 supercomputer to analyze the parallel performance of different parallel granularities. Three parallel direct solvers Supernodal LU (SUPERLU), MUltifrontal Massively Parallel sparse direct Solver (MUMPS), and Parallel Sparse matriX package (PASTIX) are compared in solving sparse systems of equations. As a result, reasonable parallel parameters are suggested for practical applications. The developed parallel algorithm is proven to be efficient and scalable.