# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 1 Introduction  
Seismic inversion, as a crucial reservoir characterization technique, has received extensive research attention over the past decades. Seismic inversion takes seismic data as input and converts the observed data into rock physics parameters by a mapping model. However, most existing seismic inversion methods, which are largely model-based, often exhibit lower accuracy and resolution when faced with complex seismic information. With the recent widespread application of deep learning across various domains, the technology has also begun to have a significant impact on seismic inversion. Many researchers are now utilizing diverse neural networks to establish inversion mapping models and achieve the mapping of seismic data to wave impedance, aiming to enhance the precision and resolution of seismic inversion. Conventional seismic inversion theories mainly rely on convolutional modeling, initially proposed by [PERSON] et al. in 1983 [1]. Later, [PERSON] and [PERSON] in 1990 presented an iterative sparse pulse inversion method [2], and [PERSON] and [PERSON] introduced the concept of geostatistical inversion using sequential simulation algorithms based on seismic data in 1994 [3]. Combining recursive inversion, constrained sparse pulse inversion, and neural network inversion, [PERSON] et al. achieved highly accurate results reflecting reservoir properties in 2009 [4]. Recently, [PERSON] et al. proposed Seismic Motion Inversion (SMI) in 2015 [5]. Traditional seismic inversions typically involve establishing target equations based on assumptions about inversion equations and imposing certain constraints to solve them, yet real geological conditions and data complexity render it difficult to express theseproblems in a single explicit manner. There are great challenges in both the creation of inverse equations and the solution of such equations. In recent years, deep learning has developed rapidly in many fields [6]. Compared with traditional seismology and seismic engineering methods, the machine learning method has the advantages of processing large numbers of data, a diversity in processing data types, and high precision prediction ability. Therefore, many researchers use machine learning methods to deal with the problems of processing seismic wave equations, seismic detection and phase selection, seismic early warning (EEW), ground motion prediction, seismic tomography and seismic geodesy, etc. [7; 8; 9; 10]. Meanwhile, in the study of seismic inversion, through various deep learning frameworks and model examples [11; 12; 13], researchers have found that neural networks can be used to construct inversion mapping models and realize the mapping of seismic data to wave impedance. The advantage of this method is that it can avoid complex mathematical equation modeling and solving and can complete feature learning, extraction, and prediction in one iteration. By jointly optimizing all parameters, errors are expected to be reduced and the entire processing process simplified. Therefore, many researchers have begun to explore seismic wave impedance inversion methods based on deep learning [14; 15; 16; 17; 18; 19; 20; 21]. At present, the main types of networks applicable to the inversion of seismic wave impedance include Convolutional Neural Networks (CNN) [22] and Recurrent Neural Networks (RNN) [23]. UNet is a network structure commonly used in CNN. U-Net was first proposed by [PERSON] in 2015 and successfully applied to medical image segmentation [24]. UNet is named for its simple and elegant network structure and U-shaped architecture. UNet is a network structure evolved from convolutional neural networks, which can combine the deep and shallow information in the network structure to achieve pixel-level image semantic segmentation.