Imaging methods for dispersion energy spectrums of surface waves, electronic devices, and storage media

An imaging method for a dispersion energy spectrum of surface waves, wherein the method is executed by a processor and comprises:

• obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation;
• processing the first surface wave data to obtain second surface wave data, and the second surface wave data corresponding to a space-frequency domain representation;
• and
• processing the second surface wave data based on a preset algorithm to obtain a first imaging result, the first imaging result referring to an image of the second surface wave data in a slowness-frequency domain;
• processing the first imaging result based on a preset transformation algorithm to obtain a second imaging result, the second imaging result including a high-resolution dispersion energy spectrum, the preset transformation algorithm including transforming the first imaging result based on a correlation between slowness and velocity to obtain the second imaging result;
• wherein the processing the second surface wave data based on a preset algorithm to obtain a first imaging result includes:
• constructing an objective function based on the second wave data;
• solving the objective function using a preset solving algorithm, including:
• determining a preferred parameter by processing at least one of the first surface wave data, the second surface wave data, and a third surface wave data based on a parameter determination model;
• wherein
• the preferred parameter includes a preferred iteration step size,
• the parameter determination model is a machine learning model,
• the parameter determination model is trained based on labeled training sample, the training sample includes sample surface wave data, and the label is an optimized parameter for each set of the historical surface wave data;
• determining a gradient direction of the objective function by a following formula:
• gi=LT*ri-1
• wherein gi denotes a gradient direction of an i-th round of iteration, L=ei2πfpx, f denotes a frequency, p denotes slowness, x denotes an offset, rn=d, ri-1 denotes a data residual term of an (i−1)-th round of iteration, and i denotes a current count of rounds of iteration;
• and
• updating a Radon model through at least one round of iteration based on the gradient direction and the preferred iteration step size, stopping the at least one round of iteration until an iteration end condition is met, and obtaining an optimal solution of the objective function;
• wherein a solution of the objective function is the first imaging result, and the preset solving algorithm is a steepest descent algorithm.