In seismic exploration, establishing a reliable parameter model (such as velocity, density, impedance) is crucial for seismic migration imaging and reservoir characterization. The interpolation of well data to obtain a complete spatial model is an important aspect of parameter modeling. However, in practical applications, well data are often sparse and irregularly distributed, which complicates the accurate construction of subsurface parameter models. The Kriging method is an effective interpolation method based on discrete well data, but its theoretical assumptions do not meet the practical requirements in seismic exploration, resulting in low modeling accuracy. This article introduces seismic facies information into the Kriging method and proposes a novel parameter modeling method named the facies-constrained Kriging (FC-Kriging) method. The FC-Kriging method modifies the Euclidean distance metric used in Kriging so that the distance between two points depends not only on their spatial coordinates but also on their associated facies categories. The proposed method is a multi-information fusion method that integrates facies information based on well data, enabling good interpolation results even with a limited number of wells. The parameter modeling results based on the FC-Kriging method are more consistent with geological logic, exhibiting clearer boundary features and higher resolution. Furthermore, the FC-Kriging method does not introduce additional computational complexity, making it convenient to implement in a 3D situation. The FC-Kriging method is applied to the 2D Sigsbee model, the 3D Standford V reservoir model and F3 block field data. The results demonstrate its accuracy and effectiveness.

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method for automatic horizon picking using multiple seismic attributes and the Markov decision process (MDP). For the design of the MDP model, the decision time and state are defined as the horizontal and vertical spatial position on a seismic image, respectively. The reward function is defined in multi-dimensional feature attribute space. Multiple attributes can highlight different aspects of a seismic image and therefore overcome the limitations of the single-attribute MDP through the cross-constraint of multiple attributes. The optimal decision is made by searching the largest state value function in the reward function space. By considering cumulative reward, the lateral continuity of a seismic image can be effectively considered, and the impacts of abnormal waveform changes or bad traces in local areas for automatic horizon picking can be effectively avoided. An effective implementation scheme is designed for picking multiple reflection horizons. The proposed method has been successfully tested on both synthetic and field data.

As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in order to enhance the signal-to-noise ratio of the seismic data. At present, the prevailing denoising techniques are based on the assumption that the signal adheres to linear model. However, this assumption is frequently invalid in complex geological conditions. The main challenge lies in the fact that linear models, which are foundational to traditional signal processing, fail to capture the nonlinear components of seismic signals. The objective of this paper is to present a methodology for the detection of nonlinear signal structures, with a particular focus on nonlinear time differences. We propose a method for detecting nonlinear time differences based on template matching, wherein the seismic wavelet is treated as the template. Template matching, a fundamental pattern recognition technique, plays a key role in identifying nonlinear structures within signals. By employing a local signal as a template, the template matching technique can identify all the structure of the signal, thereby enabling the detection of nonlinear features. By employing template matching, the nonlinear time differences in the signal are identified and corrected, thus enabling the signal to align with the assumption of linearity. Subsequently, linear denoising methods are employed to effectively remove noise and enhance the signal-to-noise ratio. The results of numerical experiments demonstrate that the proposed template matching method is highly accurate in detecting nonlinear time differences. Furthermore, the method’s efficacy in removing random noise from real seismic data is evident, underscoring its superiority.