瞬变电磁法具有探测能力强、适用性广的特点，在煤矿采空区勘探中应用广泛，但其数据反演复杂、结果不确定性高，限制了对采空区类型的识别精度。为解决这一问题，以东沟煤矿为研究对象，在烟圈反演和深度校正的基础上，选择对采空区电性反应敏感的4种瞬变电磁衍生参数。分别在已知的不含水采空区、积水采空区和正常地层各选取50个测点作为训练样本，基于贝叶斯算法建立采空区类型判别模型。模型在回估判别中的正确率达84.7%，并成功应用于3^-3煤层的全区测点采空区类型判别，显示分布特征与钻探验证结果一致。研究表明，基于瞬变电磁多参数的贝叶斯判别模型能够快速、准确识别采空区类型，解决了数据反演的多解性问题，提高了瞬变电磁法在煤矿采空区探测中的可靠性。

The transient electromagnetic method is characterized by strong detection capability and broad applicability, making it widely used in the exploration of coal mine goafs. However, the complexity of data inversion and the high uncertainty of results limit its accuracy in identifying goaf types. To address this issue, Donggou coal mine was selected as the study area. Based on smoke ring inversion and depth correction, four TEM-derived parameters sensitive to the electrical response of goafs were chosen. Fifty measurement points were selected as training samples from known dry goafs, water-filled goafs, and normal strata, respectively, to establish a goaf type discrimination model using the Bayesian algorithm. The model achieved an accuracy of 84.7% in back-estimation and was successfully applied to classify the goaf types of measurement points across the entire 3^-3 coal seam. The resulting distribution characteristics were consistent with drilling verification. The study demonstrates that the Bayes discrimination model, based on TEM multi-parameter analysis, can quickly and accurately identify goaf types, effectively resolving the multi-solution problem of data inversion and significantly improving the reliability of TEM in coal mine goaf detection.